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Google Needs Another Database To Attack Oracle, DB2, And SQL Server Directly – The Next Platform

May 16, 2022 | Author: admin

Why does Google need another database, and why in particular does it need to introduce a version of PostgreSQL highly tuned for Googles datacenter-scale disaggregated compute and storage?

It is a good question in the wake of the launch of the AlloyDB relational database last week at the Google I/O 2022 event.

The name Google is practically synonymous with large scale data storage and manipulation in myriad forms. The company created the MapReduce technique for querying unstructured data that inspired Hadoop, the BigTable NoSQL database, the Firestore NoSQL document database, and the Spanner geographically distributed relational SQL database. These tools were used internally at first, and then put on Google Cloud as the Dataproc, Cloud BigTable, and Cloud Spanner services.

Relational databases are back in vogue, due in part by Google showing that a true relational database is can scale with the advent of Spanner. And to try to encourage adoption of Spanner on the cloud, Google last year created a PostgreSQL interface for Spanner that makes it look and feel like that increasingly popular open source database. This is important because PostgreSQL has become the database of choice in the aftermath of Oracle buying Sun Microsystems in early 2010 and taking control of the much more widely used open source MySQL relational database that Sun itself took control of two years earlier.

The reason why Google needs a true version of PostgreSQL running in the cloud is that it needs to help enterprise customers who are stuck on IBM DB2, Oracle, and Microsoft SQL Server relational databases as their back-end datastores for their mission-critical systems of record get off those databases and not only move to a suitable PostgreSQL replacement, but to also make the move from on-premises applications and databases to the cloud.

That is the situation in a nutshell, Andi Gutmans, vice president and general manager of databases at the search engine, ad serving, and cloud computing giant.

Google has been an innovator on data, and we have had to innovate because we have had these billion user businesses, says Gutmans. But our strength has really been in cloud native, very transformative databases. But Google Cloud has accelerated its entrance into mainstream enterprises we have booming businesses in financial services, manufacturing, and healthcare, and we have focused on heritage systems and making sure that lifting and shifting applications into the cloud. Over the past two years, we have focused on supporting MySQL, PostgreSQL, SQL Server, Oracle, and Redis, but the more expensive, legacy, and proprietary relational databases like SQL Server and Oracle have unfriendly licensing models that really force them into one specific cloud. And we continue to get requests to help customers modernize off legacy and proprietary databases to open source.

The AlloyDB service is the forklift that Google created for this lift and shift, and dont expect for Google to open up all of the goodies it has added to PostgreSQL because these are highly tuned for Google own Colossus file system and its physical infrastructure. But, it could happen in the long run, just as Google took its Borg infrastructure and container controller and open sourced a variant of it as Kubernetes.

As we have pointed out before, the database, not the operating system and certainly not the server infrastructure, is arguably the stickiest thing in the datacenter, and companies make database decisions that span one or two decades and sometimes more. So having a ruggedized, scalable PostgreSQL that can span up to 64 vCPUs running on Google Cloud is important, as will be scaling it to 128 vCPUs and more in the coming years, which Gutmans says Google is working on.

But that database stickiness has to do with databases implementing different dialects of the SQL query language, and also having different ways of creating and embedding stored procedures and triggers within those databases. Stored procedures and triggers essentially embed elements of an application within the database rather than outside of it for reuse and performance, but there is no universally accepted and compatible way to implement these functions, and this has created lock in.

That is one of the reasons why Google acquired CompilerWorks last October. CompilerWorks has created a tool called Transpiler, which can be used to convert SQL, stored procedures, and triggers from one database to another. As a case in point, Gutmans says that Transpiler, which is not yet available as a commercial service, can convert about 70 percent of Oracles PL SQL statements to another format, and that Google Cloud is working with one customer that has 4.5 million lines of PL SQL code that it has to deal with. To help with database conversions, Google has tools to do data replication and scheme conversion, and has provided additional funding where they can get human help from systems integrators.

AlloyDB is not so much a distribution of PostgreSQL as it is a storage layer designed to work with Googles compute and storage infrastructure.

And while Google has vast scale for supporting multi-tenant instances of PostgreSQL, you will not that it doesnt have databases that span hundreds or even thousands of threads. IBMs DB2 on Power10 processors, which has 1,920 threads in a 240-core, 16-socket system with SMT8 simultaneous multithreading turned on, can grab any thread that is not being used by AIX or Linux and use it to scale the database, just to give you a sense of what real enterprise scale is for relational databases. But we are confident that is Google needed to create a 2,000-thread implementation of PostgreSQL, it could do it with NUMA clustering across its network and other caching techniques or by installing eight-way X86 servers that would bring 896 threads to bear with 56-core Sapphire Rapids Xeon SPs and 1,204 threads to bear with 64-core Granite Rapids Xeon SPs. (Again, the operating system would eat a bunch of these threads, but certainly not as much as the database could.) The latter approach using NUMA-scaled hardware is certainly easier when it comes to scaling AlloyDB, but it also means adding specialized infrastructure that is really only suitable for databases. And that cuts against the hyperscaler credo of using cheap servers and only a few configurations of them at that to run everything.

So what exactly did Google do to PostgreSQL to create AlloyDB? Google took the PostgreSQL storage engine and built what Gutmans called a cloud native storage fleet that is linked to the main PostgreSQL node, database logging and point in time recovery for the database runs on this distributed storage engine. Google also did a lot of work on the transaction engine at the heart of PostgreSQL and as a result, Google is able to get complete linear scaling up to 64 virtual cores on its Google Cloud infrastructure. Google has also added an ultra fast cache inside of PostgreSQL, and if there is a memory miss in the database, this cache can bring data into memory with microsecond latencies instead of the millisecond latencies that other caches have.

In initial tests running the TPC-C online transaction processing benchmark against AlloyDB, Gutmans says that AlloyDB was 4X faster than open source PostgreSQL and 2X faster than the Aurora relational database (which has a PostgreSQL compatible layer on top) from Amazon Web Services.

And to match the high reliability and availability of those legacy databases such as Oracle, SQL Server, and DB2, Google has a 99.99 percent uptime guarantee on the AlloyDB service, and this uptime importantly includes maintenance of the database. Gutmans says that other online databases only count unscheduled and unplanned downtime in their stats, not planned maintenance time. Finally, AlloyDB has an integrated columnar representation for datasets that is aimed at doing machine learning analysis on operational data stored in the database, and this columnar format can get up to 100X better performance on analytical queries than the open source PostgreSQL.

The PostgreSQL license is very permissive about allowing innovation in the database, and Google does not have to contribute these advances to the community. But that said, Gutmans adds that Google intends to contribute bug fixes and some enhancements it has made to the PostgreSQL community. He was not specific, but stuff that is tied directly to Googles underlying systems like Borg and Colossus are not going to be opened up.

So now Google has three different ways to get PostgreSQL functionality to customers on the Google Cloud. Cloud SQL for PostgreSQL is a managed version of the open source PostgreSQL. AlloyDB is s souped up version of PostgreSQL. And Spanner has a PostgreSQL layer thrown on top but it doesnt have compatibility for stored procedures and triggers because Spanner is a very different animal from a traditional SQL database.

Here is another differentiator. With the AlloyDB service, Google is pricing it based on the amount of compute and storage customers consume, but the IOPS underpinning access to the database are free. Unmetered. Unlike many cloud database services. IOPS gives people agita because it cannot be easily predicted, and it can be upwards of 60 percent of the cost of using a cloud database.

AlloyDB has been in closed preview for six months and is now in public preview. General availability on Google Cloud is expected in the second half of this year.

Which leads us to our final thought. Just how many database management systems and formats does a company need?

We think of ourselves as the pragmatists when it comes to databases, says Gutmans, who is also famous as the co-founder of the PHP programming language and the Zend company that underpins its support. If you look at the purpose built database, there is definitely a benefit, where you can actually optimize the query language and the query execution engine to deliver best in class price and performance for that specific workload. The challenge is, of course, that if you have too many of these, it starts to become cognitive overload for the developers and system managers. And so theres probably a sweet spot in the middle ground between monolithic and multimodal. You dont go multimodal completely because then you lose that benefit around price, performance, use case specific optimization. But if you go too broad with too many databases, it becomes complicated. On the relational side, customers definitely have at least one relational database and in many cases they also are dealing with legacy database. And with those legacy databases, we are definitely seeing more and more interest in standardizing on a great open source relational database. Document databases provide a lot of ease of use, especially under web facing side of applications when you want to do things like customer information and session management with a very loose schemas, to basically have a bag of information about a customer or transaction or song. I am also a big fan of graph databases. Graph is really going through a renaissance because not only is it very valuable in the traditional use cases around fraud detection and recommendation engines and drug discovery and master data management, but with machine learning, people are using graph databases to extract more relationships out of the data, which can then be used to improve inferencing. Beyond that, we have some other database models that, in my opinion, have some level of diminishing returns, like time series or geospatial databases.

PostgreSQL has very good JSON support now, so it can be morphed into a document database, and it is getting geospatial support together, too. There is a reason why Google is backing this database horse, and getting it fit for the race. It seems unlikely that any relational database could have a good graph overlay, or that a graph database could have a good relational overlay, but that latter item is something to think about another day. . . .

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Google Needs Another Database To Attack Oracle, DB2, And SQL Server Directly - The Next Platform

How Web3 and Cloud3 will power collaborative problem-solving and a stronger workforce – VentureBeat

May 16, 2022 | Author: admin

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

The onset of the COVID-19 pandemic propelled cloud adoption at an unprecedented rate. The benefits of cloud computing combined with the promises Web3 holds for such things as blockchain-backed decentralization, scalability and increased ownership for everyday users became clearer when the world shut down in March 2020.

Now, Web3s lesser-known but important counterpart, Cloud3, is also beginning to gain traction. Executives like Salesforce CEO Marc Bennioff are already mapping how their companies will adopt the new iteration of cloud computing the core of which is built around working from anywhere further supporting the workforce shift.

Were in a new world. This is a huge opportunity to create and extend and complement our platform. We realized for each and every one of our clouds, it was time to transform to become a work-from-anywhere environment. We ultimately are focused on delivering the operating system for Cloud3, Bennioff said in a company press release earlier this year.

Cloud3 and Web3 may sound like the latest tech buzzwords, but according to industry experts, the two are on the rise, and enterprise executives and community leaders need to pay attention or risk getting left behind.

Before there was a third iteration of anything, there had to, of course, be a first and second to lay the foundation.

The original iteration of the World Wide Web created by Tim Berners-Lee in 1990. It focused on HTML, specification of URLs and hypertext transfer protocol (HTTP) commands. While Web 2.0 is complex, it can be simplistically defined as what we know the internet to be today, including access to the web via Wi-Fi, smartphones and the rise of social media usage.

Web3s features ensure more democratization over the web. With blockchain-backed decentralization and scalability, there will be less oversight, which may, of course, lead to bad actors, but could also pave the way for underrepresented people, communities and companies to gain more control.

It always seems that POC [people of color] create the culture of communities or companies, but end up benefiting last from it. And with this kind of new paradigm of power Web3 can provide, were able to finally take ownership of our communities, said Cheryl Campos, head of venture growth and partnerships at Republic.

We can use Web3 to more easily and equally share the wealth with others and make sure we are sharing the profits with others. What is so exciting is that Web3 allows for that through non-fungible tokens (NFTs) and decentralized autonomous organizations (DAOs)and even with new DeFi (decentralized finance) products coming out that focus on supporting communities or loaning to others. That is not just the wealth gap, but also the ownership gap, that Web3 helps bring back to the hands of communities and the people within in them in a meaningful way, Campos added.

Founder and partner of the Open Web Collective, Mildred Mimi Idada, agrees: The Web3 ethos can bring in more diversity, not just in terms of race, nationality or gender, but also diversity in backgrounds, skills and perspectives.

Diverse skill sets and perspectives are also necessary for innovation in the Web3 space. We need not only technical talent, such as developers, but also creatives, lawyers, bankers and community builders, Idada said.

That said, innovation and benefits Web3 can provide to communities, businesses and investors alike wont happen overnight.

According to Greg Isenberg, cofounder and CEO of Late Checkout, a company that designs, creates and acquires Web3 and community-based technology businesses, Web3 still has a ways to go until the full breadth of its benefits is visible, but its important for executives and community leaders to pay attention now.

Web3 doesnt, and cant, work unless the UX [user experience] is very simple so much so that your grandmother could buy a digital asset like an NFT to have ownership in Web3. But to do that, we need a lot of infrastructure in place, Isenberg said.

Isenberg said he has seen several companies make great strides in UX with a proactive eye toward the rise of Web3 like Rainbow, the Ethereum wallet that allows you to manage many digital assets in one place. Isenberg said he expects other companies across industries to soon follow suit. He also echoes Campos and Idadas excitement and predictions regarding Web3, citing the impressive outpouring of cryptocurrency donations made to Ukraine totaling around $55 million in just days. Its the Web3 infrastructure that these platforms and currencies like crypto are beginning to build that creates the scalability of donations like this.

What gets me excited about Web3 in general is the coordination it brings to capital to address important things. That [was possible] because of the web infrastructure that was built on top of it, Isenberg said. I expect social causes to be a huge part of popular Web3 projects going forward. Now Im thinking, What else can this help change? Its interesting because theres a perception that Web3 is bad for the environment, for example, but I actually think that a large part of solving the worlds problems will stem from coordinating people and capital and Web3 has already proven to be really good at that.

Part of the needed infrastructure to support Web3s promise to coordinate and help solve community and world problems efficiently and at scale will require Cloud3s advanced capabilities which assure secure access to collaborative tools from anywhere.

The evolution of cloud technology began with large IT operations that were disrupted by the software-as-a-service boom. Next came infrastructure-as-a-service and platform-as-a-service technologies, further relieving pressures placed on IT teams and developers alike. Now, the demand for everything the prior cloud iterations provide is just as fierce as the demand from companies and the public alike to access these tools from wherever, whenever while simultaneously having strong IT security as a backbone from wherever, whenever.

Cloud3 will empower businesses to leverage cloud-based experience platforms as a toolkit to seamlessly compose personalized communication experiences, said Steve Forcum Cloud3 expert and director and chief evangelist for marketing at Avaya.

A report from the health information technology and clinical research company, Iqvia, underscores that emerging Cloud3 technologies will disrupt application development in organizations across all industries. Companies in the life sciences and financial industries, in particular, are well-positioned to leverage Cloud3 to differentiate themselves by applying artificial intelligence to big data.

Cloud3s emergence will also transform how businesses are run and how tools and information are supported and accessed to match the pace and style of life that the world has shifted to post-pandemic.

Rather than businesses focusing on moving to the cloud, [with Cloud3] theyll be forced to think of ways to transform within the cloud. With this comes innovation and new, cloud-based technologies. Disruptive technology should not require disruption to your business, Forcum said. A converged platform approach with composability at its core is malleable in nature, adjusting to the organizations business processes, versus forcing processes to compromise around the limitations of a cloud platform or app.

Though intriguing promises and benefits stem from both the emergence of Web3 and Cloud3, there are concerns where they overlap.

A drawback we do see with [the overlap of the] decentralized web [Web3] and Cloud3, is more the industry recognizing that while there are similarities, these are also two very different spaces with very different mechanisms and tools to achieve their goals, said Idada. Nonetheless, hardware, computation power and cloud computing will be key pieces to the next phase of the web. Improved and enhanced capabilities will change how everyday apps operate and what is possible to meet our changing faster pace and on the go lifestyles.

As for what the future holds as innovation increases and cloud adoption accelerates, pay attention or risk getting left behind is the consensus from experts.

Isenberg predicts that moving closer to the fully fleshed out iterations of both Web3 and Cloud3 that we may see more legacy companies begin to adopt them and make moves in the space, but that along with it, particularly for Web3, we may also see many of those companies fail.

Well likely see legacy companies embrace Web3 and its probably not going to go very well for many of them, he said. I think youre going to see a small percentage, maybe 1% to 5%, embrace it really, really well and become category leaders among crypto data brands while others struggle to find their place.

The future of work is remote. So, you have to make sure that there is infrastructure that will allow for this, or otherwise, you will not retain or get the best talent right for your operations. And more than ever, it has been clear that companies that embrace this Web3 space are more likely to attract younger talent and folks that are bullish on the space, Campos added.

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How Web3 and Cloud3 will power collaborative problem-solving and a stronger workforce - VentureBeat

Optical Network Hardware Market Forecasted to Hit USD 8.21 Billion by 2030 with a CAGR of 4.62% – Report by Market Research Future (MRFR) -…

May 16, 2022 | Author: admin

New York, US, May 16, 2022 (GLOBE NEWSWIRE) -- Optical Network Hardware Market Overview: According to a comprehensive research report by Market Research Future (MRFR), Optical Network Hardware Market information by Equipment, by Application and Region Forecast to 2030 market size to reach USD 8.21 billion, growing at a compound annual growth rate of 4.62% by 2030.

Optical Network Hardware Market Scope: Optical networking is a sort of communication that employs light-encoded signals to convey data through various telecommunications networks. These include long-distance national, international, and transoceanic networks and limited-range local-area networks or wide-area networks that cross metropolitan and regional areas. Optical fibers are used to connect optical network hardware. Optical fibers are typically very thin glass cylinders or filaments conveying light information.

Dominant Key Players on Optical Network Hardware Market Covered are:

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Factors compelling demand for optical network gear include the huge expansion in online connected people, increased bandwidth needs for heavy network applications, and growing adoption of data centers. Point-to-point networks establish permanent connections between two or more points, allowing any pair of nodes to communicate with each other; point-to-multipoint networks broadcast the very same signals to many different nodes at the same time; and switched networks, such as the telephone system, including switches that establish temporary connections between pairs of nodes.

Market USP Exclusively Encompassed:Optical Network Hardware Market DriversThe escalating use of high-bandwidth internet and cloud computing is projected to stimulate the need for fiber optic cables, which would boost growth in the global optical network hardware market. Data centers require dependable components to meet the stringent requirements of the cloud while also sustaining the network's physical architecture. Over the projection period, the advent of mobile services, reliance on connected devices, and reliance on 3G and 4G services may increase market demand. Fiber-to-the-home (FTTH) is critical for connecting millions of houses to the internet. As citizens increasingly seek online consultations, telehealth is responsible for driving the most demand. The proliferation of virtual healthcare may have a positive impact on the market. The usage of the internet for streaming material and conducting video conferences creates new industry prospects.

Browse In-depth Market Research Report (85 Pages) on Optical Network Hardware Market:https://www.marketresearchfuture.com/reports/optical-network-hardware-market-5446

Market Restraints:

The lack of developed infrastructure in emerging nations may stymie the growth of the worldwide optical network hardware market. Regulations and shifting government policies might limit market demand. The market may face challenges due to the necessity for appropriate fiber management to take advantage of available ports and ensure data center uptime. The need for qualified employees to maintain continuous use of data services without sacrificing speed may result in high demand for hardware engineers.

COVID 19 Analysis

The COVID-19 epidemic has hampered global optical network hardware operations. The consequences of the epidemic and government-enforced lockdown limitations have lowered the demand for hardware. Homeschooling and online education necessitate continuous connectivity to ensure the training of children and adults and serve as a viable market proponent. Low tolerance for delay can be beneficial to the market. Furthermore, there has been an increase in internet buying during the epidemic, with e-commerce companies demanding fast speeds to keep people engaged. The need for appropriate network hardware might be driven by the necessity for continual end-to-end latency and sustaining interaction with users. The development of virtualization and cloud computing is projected to threaten market demand.

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However, network design upgrades, WDM equipment spending, and the requirement for capacity for broadband services and IP video can bring market reprieve. Companies have seen the necessity for network flexibility and adaptability improvements due to the COVID-19 epidemic. To accommodate the influx of remote teleworkers and other types of online business activity flooding wide area network infrastructure, top communication service providers (CSPs) and their clients have had to accelerate connectivity across the board.

Segmentation of Market Covered in the Research:By Equipment

Wavelength-division multiplexing (WDM) is expected to grow at a 15% CAGR during the evaluation period. Over the projected period, the utilization of 100 Gbps data rates is likely to attract additional customers and enhance segment demand.

By Application

The broadband infrastructure sector is predicted to provide significant revenue for the worldwide optical network hardware market. The swelling penetration of cellphones and the internet may help to accelerate the trend.

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Regional AnalysisAccording to estimates, North America will dominate the worldwide optical network hardware market. Because users rely on fiber networks to complete daily chores. The expansion of work-from-home opportunities and unified communication software may increase the need for fiber-enabled broadband networks. The requirement for seamless connectivity for online learning, content streaming, and telehealth is expected to drive the optical network hardware market demand throughout the research period. APAC is a key hub for consumer electronics, healthcare, automobile, and other industries. This gives the market a big opportunity to expand its offerings to boost its consumer base. Over the assessment period, the region is predicted to grow at a CAGR of 16%. Usage of smartphones, emphasis on network quality, demand for connection, and the advent of video streaming can all contribute to market expansion.

Grain Management, LLC, a top private investment firm focused solely on broadband technology and the global communications industry, announced the acquisition of LightRiver's Technologies & Software entities, which comprise a premier optical network integration system solution to the telecommunications, utilities, datacenter, and cloud industries. The company provides full lifecycle software, hardware, services, and support solutions in multi-technology networking. It focuses on designing, acquiring, delivering, and continuing technical support of heterogeneous transport networks and open software tools for discovering, monitoring, provisioning, and controlling multi-vendor packet-optical networks.

Related Reports:Time-Sensitive Networking Market Research Report: Information based on types of Standards, based on Component - Forecast till 2027

Network Slicing Market Research Report: Information by Component, End User, Application, and Region Forecast till 2027

Network Probe Market Research Report: Information by Component, Organization Size, Deployment Mode, End Users, and Region - Forecast Till 2027

About Market Research Future:Market Research Future (MRFR) is a global market research company that takes pride in its services, offering a complete and accurate analysis regarding diverse markets and consumers worldwide. Market Research Future has the distinguished objective of providing the optimal quality research and granular research to clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help answer your most important questions.

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Optical Network Hardware Market Forecasted to Hit USD 8.21 Billion by 2030 with a CAGR of 4.62% - Report by Market Research Future (MRFR) -...

Paddle, the company that wants to take on Apple in IAP, raises $200M at a $1.4B valuation to supercharge SaaS payments – TechCrunch

May 16, 2022 | Author: admin

Software as a service has become the default for how organizations adopt and use apps these days, thanks to advances in cloud computing and networking, and the flexibility of pay-as-you-use models that adapt to the evolving needs of a business. Today, a company called Paddle, which has built a large business out of providing the billing backend for those SaaS products, is announcing a large funding round of $200 million as it gears up for its own next stage of growth.

The Series D investment led by KKR with participation from previous backers FTV Capital, 83North, Notion Capital, Kindred Capital, with debt from Silicon Valley Bank values London-based Paddle at $1.4 billion. With this round, the startup has raised $293 million.

Paddle today works with more than 3,000 software customers in 200 markets, where it provides a platform for them to set up and sell their SaaS products in those regions, primarily in a B2B model. But with so many consumer services also sold these days in SaaS models, its ambitions include a significant expansion of that to areas like in-app payments.

Were been growing a lot in the last couple of years. We thought it would tail off [after the COVID-19 peak] but it didnt, said Christian Owens, the CEO and co-founder. Indeed that includes more videoconferencing use by everyday people, arranging Zoom dinner, but also the explosion of streamed media and other virtual consumer services. B2C software has over the years blurred with what is thought of as B2B. Suddenly everyone needed our B2B tools.

Payments has long been a complicated and fragmented business in the digital world: banking practices, preferred payment methods and regulations differ depending on the market in question, and each stage of taking and clearing payments typically involves piecing together a chain of providers. Paddle positions itself as a merchant of record that has built a set of services around the specific needs of businesses that sell software online, covering checkout, payment, subscription management, invoicing, international taxes and financial compliance processes.

Sold as a SaaS itself basic pricing is 5% + 50 cents per transaction Paddles premise follows the basic principle of so many other business tools: payments is typically not a core competency of, say, a video conferencing or security company (one of its customers is BlueJeans, now owned by Verizon, which used to own TechCrunch; another is Fortinet).

To be fair, there are dozens (maybe hundreds) of merchants of record in the market for payments services from PayPal and Stripe through to Amazon and many more no surprise since it is complicated and just about any businesses selling online will turn to these at some point to handle that flow. However, Paddle believes (and has proven) that there is a business to be made in bringing together the many complicated parts of providing a billing and payments service into a single product specifically tailored to software businesses. It does not disclose actual revenues or specific usage numbers, but notes that revenue growth (not necessarily revenue) has doubled over the last 18 months.

Paddle as a company name doesnt have a specific meaning.

Its not a reference to anything, just a name we liked, Owens who himself is a Thiel Fellow said. And that impulse to make decisions on a hunch that it could be catchy is something that seems to have followed him and the company for a while.

Image Credits: Paddle

He came to the idea of Paddle with Harrison Rose (currently chief strategy officer and credited with building its sales ethos), after the two tried their hands at a previous software business they founded when they were just 18, an experience that gave them a taste of one of the big challenges for startups of that kind.

You make your first $1 million-$2 million in revenue with a handful of employees, but gradually those businesses become $2-20 million in sales, and then $300 million, but the basic problems of running them dont go away, he said.

Billing and payments present a particularly thorny problem because of the different regulations and compliance requirements, and practices, that scaling software companies face across different jurisdictions. Paddle itself works with some half dozen major payment companies to enable localized transactions, and many more partners, to provide that as a seamless service for its customers (which arenot payment companies themselves).

You may recognize the name Paddle for having been in the news last autumn, when it took its observations on the challenges of payments to a new frontier: apps, and specifically in-app payments. It announced last October that it was building an alternative to Apples in-app payments service.

This was arrived at through much of the observational logic that started Paddle itself, as Owens describes it. Apple, as is well known, has been locked in a protracted dispute with a number of companies that sell apps through the app store, which have wanted to have more control over their billing (and to give Apple less of a cut of those proceeds). Owens said Paddle felt encouraged to build an alternative in the heat of that dispute, before it has even been resolved, based on the response from the market (and specifically developers and app publishers) to that public dispute and governments stance.

Its approach is not unlike Apples itself, ironically:

There is one thing Apple has done right, which is to build a full set of tools around commerce for these businesses, he said. But, he added, its failing has been in not giving customers a choice of when to use it, and how much to charge for it. There has to be an alternative to cover all that as well.

(Paddle plans to charge 10% for transactions under $10, and just 5% on transactions over $10, compared to Apples 30%, a spokesperson later told me.)

The product is built and ready to go, Owens said, adding that there are already 2,000 developers signed up, representing $2 billion in app store volume, ready to try it out. Due to launch in December, Paddle has held off as Apples case with Epic (one of the most outspoken critics of IAP) has dragged on.

And he said, found Paddles name included, and not in a good way, in an update to Apples complaint.

That bold attitude may indeed keep Paddle in Apples bad books, but has made it a hero to third-party developers.

Paddle is solving a significant pain point for thousands of SaaS companies by reducing the friction and costs associated with managing payments infrastructure and tax compliance, said Patrick Devine, a director at KKR, in a statement. By simplifying the payments stack, Paddle enables faster, more sustainable growth for SaaS businesses. Christian and the team have done a phenomenal job building a category-defining business in this space, and we are excited to be supporting them as they embark on the next phase of growth.

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Paddle, the company that wants to take on Apple in IAP, raises $200M at a $1.4B valuation to supercharge SaaS payments - TechCrunch

Global Anomaly Detection Market to Reach US$8.6 Billion by the Year 2026 – Yahoo Finance

May 16, 2022 | Author: admin

ReportLinker

Abstract: What`s New for 2022? - Global competitiveness and key competitor percentage market shares. - Market presence across multiple geographies - Strong/Active/Niche/Trivial.

New York, May 16, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Anomaly Detection Industry" - https://www.reportlinker.com/p05797895/?utm_source=GNW - Online interactive peer-to-peer collaborative bespoke updates - Access to our digital archives and MarketGlass Research Platform - Complimentary updates for one year - Global Anomaly Detection Market to Reach US$8.6 Billion by the Year 2026

- Anomaly detection, also referred to as outlier detection, in data mining, refers to the identification of observations, events or items that are rare and which create suspicions by way of being significantly different from majority or rest of data. Detecting anomalies or abnormalities in datasets, in the early stages of their occurrence is critical. Growth in the global market is attributed to increasing incident of internal threats and cyber frauds that have established anomaly detection as a mega trend with universal recognition. These solutions are gaining traction among businesses to detect strange patterns in network data traffic indicating hacking attempt and fraudulent activities. The use of anomaly detection is not only limited to fraud detection in online transactions, but also entails detection of faults in operating environments. Anomaly detection is finding increasing attention from proliferation of the IoT and growing demand for advanced solutions to monitor associated use cases. The market growth is favored by increasing use of anomaly detection solutions in software testing along with rising attention on high-performance data analysis (HPDA). Global demand for anomaly detection is also attributed to increasing introduction of advanced solutions with new functions and features. Various companies serving the market are exploiting machine learning and AI technologies for developing solutions intended to assist users in quickly identifying abrupt changes in patterns and behavior.

- Amid the COVID-19 crisis, the global market for Anomaly Detection estimated at US$4.8 Billion in the year 2022, is projected to reach a revised size of US$8.6 Billion by 2026, growing at a CAGR of 15.8% over the analysis period. Solutions, one of the segments analyzed in the report, is projected to grow at a 16.4% CAGR to reach US$7.2 Billion by the end of the analysis period. After a thorough analysis of the business implications of the pandemic and its induced economic crisis, growth in the Services segment is readjusted to a revised 14.2% CAGR for the next 7-year period. This segment currently accounts for a 27.9% share of the global Anomaly Detection market. The U.S. Market is Estimated at $2.1 Billion in 2022, While China is Forecast to Reach $997.4 Million by 2026

- The Anomaly Detection market in the U.S. is estimated at US$2.1 Billion in the year 2022. The country currently accounts for a 43.18% share in the global market. China, the world`s second largest economy, is forecast to reach an estimated market size of US$997.4 Billion in the year 2026 trailing a CAGR of 16.7% through the analysis period. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 15% and 14.7% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 15.9% CAGR while Rest of European market (as defined in the study) will reach US$545.5 Million by the close of the analysis period. North America maintains the leading position in the global anomaly detection market owing to extensive uptake of the cloud and IoT technology, the bring-your-own-device (BYOD) trend and the presence of major players. The BYOD culture in the region along with proliferation of IoT and connected devices is resulting in high incident of anomalies and network breach. The trend is prompting an increasing number of organizations to embrace anomaly detection solutions and services for safeguarding business-critical information. The regional market is also gaining from high uptake of anomaly detection services among security and government agencies. Europe represents the second-leading market for anomaly detection due to technological advancements and significant investments by companies to come up with new solutions. In addition, various small-sized companies are partnering with major vendors for offering better solutions and extending their customer base. On the other hand, the anomaly detection market in Asia-Pacific is predicted to register the fastest growth owing to continuous expansion of the IT sector and increasing uptake of emerging technologies like the IoT, cyber-security, the cloud, augmented reality and big data & analytics. Select Competitors (Total 94 Featured) Broadcom, Inc. Cisco Systems, Inc. Dell Technologies, Inc. Happiest Minds Technologies Limited Hewlett Packard Enterprise Development LP International Business Machines Corporation Microsoft Corporation Nippon Telegraph and Telephone Corporation SAS Institute Inc. Securonix, Inc. Splunk, Inc. Trend Micro Incorporated Verint Systems Inc. Wipro Limited WSO2, Inc.

Read the full report: https://www.reportlinker.com/p05797895/?utm_source=GNW

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW The Race Between the Virus & Vaccines Intensifies. Amidst this Chaotic Battle, Where is the World Economy Headed? Progress on Vaccinations: Why Should Businesses Care? COVID-19-Led Shift to Remote Working Industries Expedite Digital Transformation Strategies: AI Gains Significant Interest Applications of AI in War against the Pandemic Machine Learning Benefits Healthcare Organizations Retailers Rely on AI to Stay Afloat & Embrace New Normal Competitive Scenario EXHIBIT 1: Anomaly Detection - Global Key Competitors Percentage Market Share in 2022 (E) Competitive Market Presence - Strong/Active/Niche/Trivial for Players Worldwide in 2022 (E) Global Market Prospects & Outlook Emerging Threat Landscape Augurs Well for Global Anomaly Detection Market Anomaly Detection Goes Common with Favorable Trends & Drivers With IMF?s Upward Revision of Global GDP Forecasts, Most Companies are Bullish about an Economic Comeback Despite a Continuing Pandemic EXHIBIT 2: A Strong Yet Exceedingly Patchy & Uncertain Recovery Shaped by New Variants Comes Into Play: World Economic Growth Projections (Real GDP, Annual % Change) for 2020 through 2022 EXHIBIT 3: Easing Unemployment Levels in 2021 Although Moderate Will Infuse Hope for Industries Reliant on Consumer Discretionary Incomes: Global Number of Unemployed People (In Million) for Years 2019, 2020, 2021, and 2022 Analysis by Technology EXHIBIT 4: Global Anomaly Detection Market by Component (2021 & 2027): Percentage Breakdown of Revenues for Solutions, and Services EXHIBIT 5: Global Anomaly Detection Market by Technology (2021 & 2027): Percentage Breakdown of Revenues for Big Data Analytics, Data Mining & Business Intelligence, and Machine Learning & Artificial Intelligence Analysis by End-Use EXHIBIT 6: Global Anomaly Detection Market by End-Use (2021 & 2027): Percentage Breakdown of Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare, and Other End-Uses Regional Analysis EXHIBIT 7: World Anomaly Detection Market by Region (2021 & 2027): Percentage Breakdown of Revenues for Developed and Developing Regions EXHIBIT 8: World Data Governance Market - Geographic Regions Ranked by CAGR (Revenues) for 2020-2027: China, Asia-Pacific, USA, Europe, Japan, Canada, and Rest of World An Introduction to Anomaly Detection Technique Categories Anomaly Detection Settings Time-Series Anomalies: Types & Detection Methods Benefits and Use Cases of Anomaly Detection Applications Recent Market Activity Select Global Brands Emphasis on Technology Adoption Elicits AI Implementation in Manufacturing Industry

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS The Pace of Digital Transformation to Determine Pace of Demand for Anomaly Detection EXHIBIT 9: Digital Transformation by Industry: 2020 EXHIBIT 10: Industry Adoption of Artificial Intelligence (AI) by Function: 2020 Anomaly Detection Emerges as Future-Proof Strategy for Businesses Anomaly Detection, Analytics & Cognitive Intelligence Real-Time Anomaly Detection & Analytics Get Intertwined with Cognitive Intelligence Real-Time Anomaly Detection in Clusters Trends in Big Data to Shape Future of AI and Anomaly Detection Edge Computing Critical to IoT Anomaly Detection Hosting at Edge to Drive Growth EXHIBIT 11: Global Edge Computing Market in US$ Billion: 2020, 2024, and 2026 Convergence of AI and IoT to Bring in Efficiencies EXHIBIT 12: Global Breakdown of Investments in Manufacturing IoT (in US$ Billion) for the Years 2016, 2018, 2020 and 2025 EXHIBIT 13: Industry 4.0 Technologies with Strongest Impact on Organizations: 2020 ML-based Anomaly Detection Machine Learning and AI-Assisted Platforms Personalize Customer Experiences in Marketing Applications Leading to Demand Growth of Anomaly Detection MLAD Holds Potential to Identify Anomalies Anomaly Detection: Real Opportunities to Identify Time-Series Data Anomalies Anomalous Data Detection with Self-Supervised Learning: A Review Anomaly Detection Emerges as Hot Trend in Ad Tech Landscape Financial Sector: AI and ML Offer Numerous Gains for Anomaly Detection EXHIBIT 14: Top Technology Investments in BFSI Sector: 2021 EXHIBIT 15: Post Pandemic Focus of Banks on Digital Transformation to Benefit Anomaly Detection: % of Organizations Citing Priority for 2020 Anomaly Detection through AI Technology Steps into Manufacturing Space to Transform Diverse Aspects Industrial AI to Influence Manufacturing in a Major Way Industrial IoT, Robotics and Big Data to Stimulate Anomaly Detection EXHIBIT 16: Global Investments on Industry 4.0 Technologies (in US$ Billion) for the Years 2017, 2020, & 2023 EXHIBIT 17: Global Predictive Maintenance by Market in US$ Billion for Years 2020, 2022, 2024, and 2026 Challenges in Anomaly Detection Ad Fraud: The Implications & Anomaly Detection

4. GLOBAL MARKET PERSPECTIVE Table 1: World Recent Past, Current & Future Analysis for Anomaly Detection by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 2: World Historic Review for Anomaly Detection by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 3: World 12-Year Perspective for Anomaly Detection by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets for Years 2015, 2021 & 2027

Table 4: World Recent Past, Current & Future Analysis for Solutions by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 5: World Historic Review for Solutions by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 6: World 12-Year Perspective for Solutions by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 7: World Recent Past, Current & Future Analysis for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 8: World Historic Review for Services by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 9: World 12-Year Perspective for Services by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 10: World Recent Past, Current & Future Analysis for Big Data Analytics by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 11: World Historic Review for Big Data Analytics by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 12: World 12-Year Perspective for Big Data Analytics by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 13: World Recent Past, Current & Future Analysis for Data Mining & Business Intelligence by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 14: World Historic Review for Data Mining & Business Intelligence by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 15: World 12-Year Perspective for Data Mining & Business Intelligence by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 16: World Recent Past, Current & Future Analysis for Machine Learning & Artificial Intelligence by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 17: World Historic Review for Machine Learning & Artificial Intelligence by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 18: World 12-Year Perspective for Machine Learning & Artificial Intelligence by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 19: World Recent Past, Current & Future Analysis for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 20: World Historic Review for BFSI by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 21: World 12-Year Perspective for BFSI by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 22: World Recent Past, Current & Future Analysis for IT & Telecom by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 23: World Historic Review for IT & Telecom by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 24: World 12-Year Perspective for IT & Telecom by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 25: World Recent Past, Current & Future Analysis for Government & Defense by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 26: World Historic Review for Government & Defense by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 27: World 12-Year Perspective for Government & Defense by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 28: World Recent Past, Current & Future Analysis for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 29: World Historic Review for Manufacturing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 30: World 12-Year Perspective for Manufacturing by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 31: World Recent Past, Current & Future Analysis for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 32: World Historic Review for Healthcare by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 33: World 12-Year Perspective for Healthcare by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

Table 34: World Recent Past, Current & Future Analysis for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 35: World Historic Review for Other End-Uses by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 36: World 12-Year Perspective for Other End-Uses by Geographic Region - Percentage Breakdown of Value Revenues for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2015, 2021 & 2027

III. MARKET ANALYSIS

UNITED STATES Anomaly Detection Market Presence - Strong/Active/Niche/Trivial - Key Competitors in the United States for 2022 (E) Market Analytics Table 37: USA Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 38: USA Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 39: USA 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Table 40: USA Recent Past, Current & Future Analysis for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 41: USA Historic Review for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 42: USA 12-Year Perspective for Anomaly Detection by Technology - Percentage Breakdown of Value Revenues for Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence for the Years 2015, 2021 & 2027

Table 43: USA Recent Past, Current & Future Analysis for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 44: USA Historic Review for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 45: USA 12-Year Perspective for Anomaly Detection by End-Use - Percentage Breakdown of Value Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses for the Years 2015, 2021 & 2027

CANADA Anomaly Detection Market Presence - Strong/Active/Niche/Trivial - Key Competitors in Canada for 2022 (E) Market Analytics Table 46: Canada Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 47: Canada Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 48: Canada 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Table 49: Canada Recent Past, Current & Future Analysis for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 50: Canada Historic Review for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 51: Canada 12-Year Perspective for Anomaly Detection by Technology - Percentage Breakdown of Value Revenues for Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence for the Years 2015, 2021 & 2027

Table 52: Canada Recent Past, Current & Future Analysis for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 53: Canada Historic Review for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 54: Canada 12-Year Perspective for Anomaly Detection by End-Use - Percentage Breakdown of Value Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses for the Years 2015, 2021 & 2027

JAPAN Anomaly Detection Market Presence - Strong/Active/Niche/Trivial - Key Competitors in Japan for 2022 (E) Market Analytics Table 55: Japan Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 56: Japan Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 57: Japan 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Table 58: Japan Recent Past, Current & Future Analysis for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 59: Japan Historic Review for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 60: Japan 12-Year Perspective for Anomaly Detection by Technology - Percentage Breakdown of Value Revenues for Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence for the Years 2015, 2021 & 2027

Table 61: Japan Recent Past, Current & Future Analysis for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 62: Japan Historic Review for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 63: Japan 12-Year Perspective for Anomaly Detection by End-Use - Percentage Breakdown of Value Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses for the Years 2015, 2021 & 2027

CHINA Table 64: China Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 65: China Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 66: China 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Table 67: China Recent Past, Current & Future Analysis for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 68: China Historic Review for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 69: China 12-Year Perspective for Anomaly Detection by Technology - Percentage Breakdown of Value Revenues for Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence for the Years 2015, 2021 & 2027

Table 70: China Recent Past, Current & Future Analysis for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 71: China Historic Review for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 72: China 12-Year Perspective for Anomaly Detection by End-Use - Percentage Breakdown of Value Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses for the Years 2015, 2021 & 2027

EUROPE Anomaly Detection Market Presence - Strong/Active/Niche/Trivial - Key Competitors in Europe for 2022 (E) Market Analytics Table 73: Europe Recent Past, Current & Future Analysis for Anomaly Detection by Geographic Region - France, Germany, Italy, UK and Rest of Europe Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2020 through 2027 and % CAGR

Table 74: Europe Historic Review for Anomaly Detection by Geographic Region - France, Germany, Italy, UK and Rest of Europe Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 75: Europe 12-Year Perspective for Anomaly Detection by Geographic Region - Percentage Breakdown of Value Revenues for France, Germany, Italy, UK and Rest of Europe Markets for Years 2015, 2021 & 2027

Table 76: Europe Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 77: Europe Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 78: Europe 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Table 79: Europe Recent Past, Current & Future Analysis for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 80: Europe Historic Review for Anomaly Detection by Technology - Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 81: Europe 12-Year Perspective for Anomaly Detection by Technology - Percentage Breakdown of Value Revenues for Big Data Analytics, Data Mining & Business Intelligence and Machine Learning & Artificial Intelligence for the Years 2015, 2021 & 2027

Table 82: Europe Recent Past, Current & Future Analysis for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 83: Europe Historic Review for Anomaly Detection by End-Use - BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 84: Europe 12-Year Perspective for Anomaly Detection by End-Use - Percentage Breakdown of Value Revenues for BFSI, IT & Telecom, Government & Defense, Manufacturing, Healthcare and Other End-Uses for the Years 2015, 2021 & 2027

FRANCE Table 85: France Recent Past, Current & Future Analysis for Anomaly Detection by Component - Solutions and Services - Independent Analysis of Annual Revenues in US$ Million for the Years 2020 through 2027 and % CAGR

Table 86: France Historic Review for Anomaly Detection by Component - Solutions and Services Markets - Independent Analysis of Annual Revenues in US$ Million for Years 2015 through 2019 and % CAGR

Table 87: France 12-Year Perspective for Anomaly Detection by Component - Percentage Breakdown of Value Revenues for Solutions and Services for the Years 2015, 2021 & 2027

Kriya Therapeutics Scores $270 Million to Support Gene Therapy Pipeline – BioSpace

May 16, 2022 | Author: admin

Kriya Therapeutics secured $270 million in Series C financing that will be used to support the advancement of the companys fully integrated gene therapy pipeline and expand its engineering, manufacturing and computational platforms.

Since the turn of the year, Redwood City, Calif.-based Kriya, which was one of BioSpaces Class of 2021 life sciences startups to watch, has expanded its gene therapy programs through the acquisition of exclusive rights to preclinical assets from Warden Bio, as well as an antibody discovery agreement withTwist Bioscience.

In January of this year, the company forged an exclusive agreement with theMedical University of South Carolina Foundation for Research Development to license next-generation complement-targeted gene therapies for the treatment of geographic atrophy, also known as atrophic age-related macular degeneration, and other ocular diseases.

The company also scaled its machine learning-enabled technology and cloud computing architecture, dubbed Sirve. The scaling was critical in order to support the integration of large datasets generated by the companys high throughput screening, next-generation sequencing, and algorithmic data mining platforms, Kriya said.

In addition to the expansion of its pipeline and technology platform, Kriya also opened the doors to its new scalable GMP manufacturing space in North Carolina that will bolster the manufacturing of its gene therapies for oncology, diabetes, severe obesity, ophthalmology and other indications.

Shankar Ramaswamy, co-founder and CEO of Kriya, said the company was founded on the vision of addressing some of the issues related to the earliest generations of gene therapies. The Series C financing will support the companys continued growth as it pushes into the clinic and scales its platform.

We believe gene therapy has the potential to redefine medicine over the next decade. However, the field has been constrained by technological and operational challenges that make it difficult and expensive to deliver new products, Ramaswamy said in a statement. He added that the financing will allow the company to achieve our ultimate vision of expanding the reach and unlocking the full potential of gene therapy as a modality.

The Series C financing was led by Patient Square Capital. The $270 million round was supported by Bluebird Ventures, CAM Capital, Dexcel Pharma, Foresite Capital, JDRF T1D Fund, Lightswitch Capital, Narya Capital, QVT, Transhuman Capital, as well as other undisclosed investors.

Jim Momatazee, the managing partner of Patient Square Capital and a member of the Kriya Board of Directors, said the company has made tremendous strides over the past few years. In addition to the partnerships the company forged, Kriya has also tapped world-class talent and scaled its infrastructure to unlock the full potential of gene therapy, he said.

We believe the company has the potential to be the clear leader in the evolving gene therapy field, consistent with Patient Square Capitals focus to build and support category leading companies in health care, Momatazee said in a statement.

For Kriya, the latest financing is more than double the amount it raised less than one year ago. In July 2021, the company raised $100 million in a Series B financing round, which built on the $80.5 million it raised the previous year in a Series A financing round.

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Kriya Therapeutics Scores $270 Million to Support Gene Therapy Pipeline - BioSpace

High-performance Computing (HPC) Market worth $49.9 billion by 2027 – Exclusive Report by MarketsandMarkets – PR Newswire

May 16, 2022 | Author: admin

CHICAGO, May 16, 2022 /PRNewswire/ --According to a research report "High-performance Computing (HPC) Market with COVID-19 Impact Analysis, by Component, Computation Type (Parallel Computing, Distributed computing and Exascale Computing), Industry, Deployment, Server Price Band, Verticals & Region - Global Forecast to 2027", published by MarketsandMarkets,the High performance computing (HPC) market is expected to grow from USD 36.0 billion in 2022 to USD 49.9 billion by 2027, at a CAGR of 6.7%. The market growth can be attributed to several factors, such as increasing demand for high performance computing in Banking, financial services and insurance (BFSI) applications.

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Services segment of high performance computing to grow with the highest CAGR during the forecast period

HPC services are required to be highly customized to be free from product design overheads. HPC services help employees within an organization to meet their ultimate objective of improved operability and energy and resource utilization. The growing complexities in the installation and deployment of HPC systems are expected to boost the demand for HPC services. HPC services are only available for deployment via the cloud. HPC systems can be hosted over managed cloud services such as AWS (Amazon Web Services) and Microsoft Azure. By making use of HPC over the managed cloud, organizations can tap into the power of these HPC systems without requiring to set up HPC systems manually. The use of HPC hosted over the cloud follows a pay-as-you-go pricing model.

Parallel computing to hold the largest share of the market during the forecast period

In parallel computing, multiple processing elements run simultaneously to solve complex problems. These problems are divided into smaller problems, and a dedicated computing resource is assigned to solve them. Parallel computing has gained popularity across various applications in the industrial sector. The use of other computing techniques has physical constraints that prevent frequency scaling, thereby reducing the system's capability of operating at optimal computational speeds. Parallel computing has become the dominant standard in computer architecture, as it is being increasingly used in modern multi-core processors due to its less power consumption and heat generation features in data processing.

Browsein-depth TOC on"High-performance Computing (HPC) Market130 Tables 54 Figures 206 Pages

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North America for High performance computing is expected to witness the highest growth during the forecast period.

North America is expected to hold the largest market share (~44%) during the forecast period as it is a technologically advanced region and has a significant presence of major market players. Moreover, the region has the largest presence of cloud service providers and is witnessing an increase in the investments in the technological development of existing infrastructures, which is fueling the deployment rate of HPC systems. The region is also experiencing a high demand for compute and on-demand access to process and analyze data. Thus, the market for HPC server solutions is expected to hold a major share of the regional market during the forecast period. The top countries contributing to the growth of the HPC market in North America include the US and Canada. Many major IT companies are based in the US and have invested heavily in setting up high-capacity storage and high-speed data analysis facilities that handle and analyze complex global data.

Some of the key companies operating in the market are Advanced Micro Devices (US), Intel (US), HPE (US), IBM (US), Dell (US), Lenovo (China), Fujitsu (Japan), Atos (France), CISCO (US), Nvidia (Japan), NEC Corporation (Japan) and so on.

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High-performance Computing (HPC) Market worth $49.9 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire

CAREER Award: Teaching machines the art of human decision-making – EurekAlert

May 16, 2022 | Author: admin

image:Jundong Li, University of Virginia assistant professor of electrical and computer engineering, computer science and data science, earned a National Science Foundation CAREER program award, one of the NSFs most prestigious awards for early-career faculty. The award recognizes Li's potential for leadership in research and education and his expertise in data mining, machine learning, and artificial intelligence. view more

Credit: Tom Cogill

Children attend pre-school and kindergarten to develop the skills they need to thrive. They practice sharing and making friends. They learn to use words and numbers. They gain confidence through movement and self-control. Teachers have an array of techniques to help children develop these skills.

But how should they help students who struggle? Theres no shortage of opinions from educators, parents and policy-makers about effective ways to teach children, based on observations of what happens in classrooms and childrens behavior in and out of school. These observations may be missing something important, however - factors that are unseen, unrecognized or unreported but are powerful influences on a students performance.

Jundong Li, University of Virginia assistant professor of electrical and computer engineering, computer science and data science, is conducting research that could help teachers and administrators more accurately determine which learning methods are best for their youngest pupils.

Li has earned a prestigious National Science Foundation CAREER award to better understand cause and effect in human decision-making in the era of big data. Li will use his $600,000 five-year award to develop a suite of sophisticated algorithms and mathematical models, informed by human experience and intuition, to find cause-and-effect relationships in a huge amount of data. His work has the potential for broad applications in public health and medicine in addition to education.

The CAREER program, one of the NSFs most prestigious awards for early-career faculty, recognizes the recipients potential for leadership in research and education. Lis award recognizes his expertise in data mining, machine learning, and artificial intelligence, which are part of a research strength area for the Charles L. Brown Department of Electrical and Computer Engineering within UVAs School of Engineering and Applied Science.

The basic problem here is that machine learning and data mining alone are often insufficient to make decisions for humans, Li said. Typically, given a large amount of data, machine learning models can find correlations and then use those correlations to make inferences and predict outcomes.

Because machines cannot really understand human needs, expectations and behaviors, their predictions and recommendations may be based on spurious correlations.

We all know that correlation does not necessarily imply causation, Li said. In order to make a decision, typically we need to have a better understanding of what is cause and what is outcome. We want to find causal relations between variables at play. This means creating what Li calls a causal inference model, which quantifies the strength of cause-and-effect relationships between different variables and uses the strongest to make a decision.

Nowadays, research to make machine learning algorithms and models better at reasoning is largely data-driven, Li said. For my CAREER award project, I want to incorporate prior human knowledge into these algorithms, to give the model the benefit of human wisdom as it processes data and interprets decision-making scenarios.

Li has had preliminary success in his proposed approach, working in the public health arena supported by a RAPID grant from the UVAs Global Infectious Diseases Institute. Li collaborated with Daniel Mietchen, formerly with UVAs School of Data Science and now a researcher at the Fraunhofer Institute for Biomedical Engineering in Germany, to assess the impact of COVID-19 related policies on outbreaks. Three members of Lis research group assisted with the study.

The teams model shows how COVID-19 policies such as social distancing affected outbreaks at the county level, taking into account peoples vigilance over the virus over time.

A county government may issue policies to enforce social distancing at an early stage of the pandemic, but if residents in the county tend to be more alert to COVID-19, they likely would have a lower probability of infection. In this case, vigilance is a confounding variable, influencing both the treatment, or the policy of social distancing, and the outcome, or the number of individuals who get sick.

Publicly available information online provided an important resource. For example, the team used the popularity of Google searches about COVID-19 at different time periods as a measure of residents vigilance. Using this indicator and others, the team developed a framework that captures information from different time periods and handles information among counties to estimate how various policies affected COVID-19 outbreaks. The framework shows the cause and effect of policies at different degrees of specificity, from a category of policies with a certain goal, to a single policy.

The team members presented the results of their study in a research paper, Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US, published in the proceedings of the Association for Computing Machinerys Web Conference 2022 in April.

Our web conference paper captures outbreak dynamics more accurately than statistical methods alone, Li said. Additionally, our assessment of policies is more consistent with existing epidemiological studies of COVID-19. This suggests that public health officials can use our framework when randomized controlled trials, the gold standard of cause-and-effect estimation, are not feasible.

Lis next step involves collaboration with individuals who are knowledgeable about the application areas, such as medical doctors, public health officials and experts in learning and development. He will also identify publicly available data in the areas of health and education that he can mine to further test and develop his decision-making framework.

Ultimately, Li envisions developing sophisticated algorithms that will pinpoint cause and effect, so physicians can use them to customize treatments based on patient information, and decision-makers can plug the algorithms into their own data systems to deliver policies that improve their constituents health, economic well-being and quality of life.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

CAREER Award: Teaching machines the art of human decision-making - EurekAlert

Mawson Infrastructure Group Announces Financial Results for First Quarter 2022 – Business Wire

May 16, 2022 | Author: admin

SYDNEY & NEW YORK--(BUSINESS WIRE)--Mawson Infrastructure Group Inc. (NASDAQ:MIGI) (Mawson), a digital infrastructure provider, is pleased to announce business highlights and financial results for the first quarter of 2022.

Q1 2022 Financial and Business Highlights

Subsequent to Quarter End

2022 Strategic Focus

James Manning, CEO and Founder of Mawson Infrastructure, said, "Q1 2021 was a solid operational quarter for our business. We significantly increased our Bitcoin self-mining operational footprint, producing 459 Bitcoin in Q1, delivered Q1 revenue of $19.4 million, up 178% vs Q1 2021, delivered Q1 gross profit of $11.0 million, up 138% vs Q1 2021, and posted Q1 non-GAAP EBITDA of $4.5 million, up 160% vs Q1 2021. Our hosting co-location business accelerated in Q1 - we signed new 100-megawatt hosting co-location customer Celsius Mining LLC, as well as new 12-megawatt hosting co-location customer Foundry Digital LLC. In very exciting news, we gained approval to expand our Georgia Bitcoin Mining facility to 230-megawatts, which is capable of accommodating up to 7.5 Exahash. The pipeline of strong demand for our hosting co-location business which adds an additional revenue stream for the group continues to expand, and we look forward to updating stockholders on this front in due course.

Conference Call Details:

The company has scheduled a webcast for May 16, 2022 at 5:00 p.m. Eastern Time, to discuss results for the first quarter of 2022.

A new Investor Presentation will be available on the website at http://www.mawsoninc.com prior to the call.

Conference Call Information:

Date: Monday, May 16, 2022Time: 5:00 p.m. Eastern TimeDial in Number for U.S. Callers: 1-877-407-4018Dial in Number for International Callers: 1-201-689-8471Please Reference Conference ID: 13729849

The call will also be accompanied live by webcast and will be accessible at:https://viavid.webcasts.com/starthere.jsp?ei=1548153&tp_key=96463d1f1a

To join the live conference call, please dial in to the above referenced telephone numbers five to ten minutes prior to the scheduled conference call time.

A replay will be available starting on May 16, 2022 at approximately 8:00 p.m. ET through May 30, 2022 at 11:59 P.M. ET. To access the replay, please dial 1-844-512-2921 in the U.S. and 1-412-317-6671 for international callers. The conference ID# is 13729849.

About Mawson Infrastructure

Mawson Infrastructure Group (NASDAQ: MIGI) is a digital infrastructure provider, with multiple operations throughout the USA and Australia. Mawsons vertically integrated model is based on a long-term strategy to promote the global transition to the new digital economy. Mawson matches sustainable energy infrastructure with next-generation mobile data centre (MDC) solutions, enabling low-cost Bitcoin production and on-demand deployment of infrastructure assets. With a strong focus on shareholder returns and an aligned board and management, Mawson Infrastructure Group is emerging as a global leader in ESG focused Bitcoin mining and digital infrastructure.

For more information, visit: http://www.mawsoninc.com

CAUTIONARY NOTE REGARDING FORWARD-LOOKING STATEMENTS

Mawson cautions that statements in this press release that are not a description of historical fact are forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements may be identified by the use of words referencing future events or circumstances such as expect, intend, plan, anticipate, believe, and will, among others. Because such statements are subject to risks and uncertainties, actual results may differ materially from those expressed or implied by such forward-looking statements. These forward-looking statements are based upon Mawsons current expectations and involve assumptions that may never materialize or may prove to be incorrect. Actual results and the timing of events could differ materially from those anticipated in such forward-looking statements as a result of various risks and uncertainties, which include, without limitation, the possibility that Mawsons need and ability to raise additional capital, the development and acceptance of digital asset networks and digital assets and their protocols and software, the reduction in incentives to mine digital assets over time, the costs associated with digital asset mining, the volatility in the value and prices of cryptocurrencies and further or new regulation of digital assets. More detailed information about the risks and uncertainties affecting Mawson is contained under the heading Risk Factors included in Mawsons Annual Report on Form 10-K filed with the SEC on March 21, 2022 and Mawsons Quarterly Report on Form 10-Q filed with the SEC on May 16, 2022, and in other filings Mawson has made and may make with the SEC in the future. One should not place undue reliance on these forward-looking statements, which speak only as of the date on which they were made. Because such statements are subject to risks and uncertainties, actual results may differ materially from those expressed or implied by such forward-looking statements. Mawson undertakes no obligation to update such statements to reflect events that occur or circumstances that exist after the date on which they were made, except as may be required by law.

Mawson Infrastructure Group Announces Financial Results for First Quarter 2022 - Business Wire

Data Analysis Services Market Size in 2022 : Impact of COVID-19 and Comprehensive Analysis, Growth Revenue, Future Demands, SWOT analysis, industry…

May 16, 2022 | Author: admin

The Data Analysis Services Market 2022 report provides the historical as well as present growth factors of the global market. The report contains financial data achieve from various research sources to provide specific and reliable analysis. The report presents information about the top regions of the world and countries with their regional development status, volume, market size, market value, and price data.

Data Analysis ServicesMarket 2022 Impact of COVID-19 on the Market: The report contains financial data achieve from various research sources to provide specific and reliable analysis. The report presents information about the top regions of the world and countries with their regional development status, volume, market size, market value, and price data.

Data Analysis Services Market (2022-2028) report identifies Sales of Market by regional analysis by product type and product applications. The competitive data type analysis includes capacity, market trends, profit margin, market growth, imports, exports, revenue and Marketing strategies, policies, industry chain analysis that are changing the wave of the market are also catered in the report. The Report provides potential market opportunities and Major Regions that plays a vital role in market are North America, Europe, China, Japan, Middle East and Africa, India, South America and Others.

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Data Analysis Services Market Segmentation by Types, By Applications and By Region:

Data Analysis Services market is analyses and market size information is provided by regions (countries). Segment by Application and type, the Data Analysis Services market is segmented into United States, Europe, China, Japan, Southeast Asia, India and Rest of World. The report includes region-wise market size for the period 2022-2028.It also includes market size and forecast by players, by Type, and by Application segment in terms of sales and revenue for the period 2022-2028.

Data Analysis Services Market Segment by Applications:

Data Analysis Services Market Segment/types by Type:

List of Top Key Players in Data Analysis Services Market Report are:

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Brief Description for Data Analysis Services Market:

According to this latest study, the 2021 growth of Data Analysis Services will have significant change from previous year. By the most conservative estimates of global Data Analysis Services market size (most likely outcome) will be a year-over-year revenue growth rate of % in 2021, from USD million in 2020. Over the next five years the Data Analysis Services market will register a % CAGR in terms of revenue, the global market size will reach USD million by 2026.This report presents a comprehensive overview, market shares, and growth opportunities of Data Analysis Services market by product type, application, key players and key regions and countries.

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Competitive Landscape and Data Analysis Services Market Share Analysis:

Data Analysis Services market competitive landscape provides details and data information by players. The report offers comprehensive analysis and accurate statistics on revenue by the key player for the period 2021. It also offers detailed analysis supported by reliable statistics on revenue (global and regional level) by players for the period 2021. Details included are company description, major business, company total revenue and the sales, revenue generated in Data Analysis Services business, the date to enter into the Data Analysis Services market, Data Analysis Services product introduction, recent developments, etc.

Key Questions Answered in The Report:

Major Points from Table of Contents:

Global Data Analysis Services Market Research Report 2022-2028, by Manufacturers, Regions, Types and Applications

1 Study Coverage

1.1 Data Analysis Services Product Introduction

1.2 Market by Type

1.2.1 Global Data Analysis Services Market Size Growth Rate by Type

1.3 Market by Application

1.3.1 Global Data Analysis Services Market Size Growth Rate by Application

1.4 Study Objectives

1.5 Years Considered

2 Global Data Analysis Services Production

2.1 Global Data Analysis Services Production Capacity (2016-2028)

2.2 Global Data Analysis Services Production by Region: 2016 VS 2022 VS 2028

2.3 Global Data Analysis Services Production by Region

2.3.1 Global Data Analysis Services Historic Production by Region (2016-2022)

2.3.2 Global Data Analysis Services Forecasted Production by Region (2022-2028)

3 Global Data Analysis Services Sales in Volume and Value Estimates and Forecasts

3.1 Global Data Analysis Services Sales Estimates and Forecasts 2016-2028

3.2 Global Data Analysis Services Revenue Estimates and Forecasts 2016-2028

3.3 Global Data Analysis Services Revenue by Region: 2016 VS 2022 VS 2028

3.4 Global Top Data Analysis Services Regions by Sales

3.4.1 Global Top Data Analysis Services Regions by Sales (2016-2022)

3.4.2 Global Top Data Analysis Services Regions by Sales (2022-2028)

3.5 Global Top Data Analysis Services Regions by Revenue

3.5.1 Global Top Data Analysis Services Regions by Revenue (2016-2022)

3.5.2 Global Top Data Analysis Services Regions by Revenue (2022-2028)

3.6 North America

3.7 Europe

3.8 Asia-Pacific

3.9 Latin America

3.10 Middle East and Africa

4 Competition by Manufactures

4.1 Global Data Analysis Services Supply by Manufacturers

4.1.1 Global Top Data Analysis Services Manufacturers by Production Capacity (2022 VS 2022)

4.1.2 Global Top Data Analysis Services Manufacturers by Production (2016-2022)

4.2 Global Data Analysis Services Sales by Manufacturers

4.2.1 Global Top Data Analysis Services Manufacturers by Sales (2016-2022)

4.2.2 Global Top Data Analysis Services Manufacturers Market Share by Sales (2016-2022)

4.2.3 Global Top 10 and Top 5 Companies by Data Analysis Services Sales in 2022

4.3 Global Data Analysis Services Revenue by Manufacturers

4.3.1 Global Top Data Analysis Services Manufacturers by Revenue (2016-2022)

4.3.2 Global Top Data Analysis Services Manufacturers Market Share by Revenue (2016-2022)

4.3.3 Global Top 10 and Top 5 Companies by Data Analysis Services Revenue in 2022

4.4 Global Data Analysis Services Sales Price by Manufacturers

4.5 Analysis of Competitive Landscape

4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)

4.5.2 Global Data Analysis Services Market Share by Company Type (Tier 1, Tier 2, and Tier 3)

4.5.3 Global Data Analysis Services Manufacturers Geographical Distribution

4.6 Mergers and Acquisitions, Expansion Plans

5 Market Size by Type

5.1 Global Data Analysis Services Sales by Type

5.1.1 Global Data Analysis Services Historical Sales by Type (2016-2022)

5.1.2 Global Data Analysis Services Forecasted Sales by Type (2022-2028)

5.1.3 Global Data Analysis Services Sales Market Share by Type (2016-2028)

5.2 Global Data Analysis Services Revenue by Type

5.2.1 Global Data Analysis Services Historical Revenue by Type (2016-2022)

5.2.2 Global Data Analysis Services Forecasted Revenue by Type (2022-2028)

5.2.3 Global Data Analysis Services Revenue Market Share by Type (2016-2028)

5.3 Global Data Analysis Services Price by Type

5.3.1 Global Data Analysis Services Price by Type (2016-2022)

5.3.2 Global Data Analysis Services Price Forecast by Type (2022-2028)

6 Market Size by Application

6.1 Global Data Analysis Services Sales by Application

6.1.1 Global Data Analysis Services Historical Sales by Application (2016-2022)

6.1.2 Global Data Analysis Services Forecasted Sales by Application (2022-2028)

6.1.3 Global Data Analysis Services Sales Market Share by Application (2016-2028)

6.2 Global Data Analysis Services Revenue by Application

6.2.1 Global Data Analysis Services Historical Revenue by Application (2016-2022)

6.2.2 Global Data Analysis Services Forecasted Revenue by Application (2022-2028)

6.2.3 Global Data Analysis Services Revenue Market Share by Application (2016-2028)

6.3 Global Data Analysis Services Price by Application

6.3.1 Global Data Analysis Services Price by Application (2016-2022)

6.3.2 Global Data Analysis Services Price Forecast by Application (2022-2028)

7 Data Analysis Services Consumption by Regions

7.1 Global Data Analysis Services Consumption by Regions

7.1.1 Global Data Analysis Services Consumption by Regions

7.1.2 Global Data Analysis Services Consumption Market Share by Regions

7.2 North America

7.2.1 North America Data Analysis Services Consumption by Application

7.2.2 North America Data Analysis Services Consumption by Countries

7.2.3 United States

7.2.4 Canada

7.2.5 Mexico

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Data Analysis Services Market Size in 2022 : Impact of COVID-19 and Comprehensive Analysis, Growth Revenue, Future Demands, SWOT analysis, industry...

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