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The Next Terra Luna? A Major $5 Billion Cryptocurrency Could Be About To Self-DestructPotentially Hitting The Price Of Bitcoin and Ethereum - Forbes
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From regulation to taxation, Japan has been hostile to cryptocurrency gaming. That stance is threatening the country's position as a global leader in...
Cryptocurrency rally comes to a screeching halt: market already looking ahead to Fed meeting ShareCast
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Cryptocurrency rally comes to a screeching halt: market already looking ahead to Fed meeting - ShareCast
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Bitcoin and Other Cryptos Surge. Heres Why and What Happens Next. - Barron's
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Bitcoin liquidates over $1 billion as BTC price hits 6-week highs - Cointelegraph
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There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it.
Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid
Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate from expected norms.
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Data Mining - Overview - tutorialspoint.com
Read More..Assembly
A database providing information on the structure of assembled genomes, assembly names and other meta-data, statistical reports, and links to genomic sequence data.
A curated set of metadata for culture collections, museums, herbaria and other natural history collections. The records display collection codes, information about the collections' home institutions, and links to relevant data at NCBI.
A collection of genomics, functional genomics, and genetics studies and links to their resulting datasets. This resource describes project scope, material, and objectives and provides a mechanism to retrieve datasets that are often difficult to find due to inconsistent annotation, multiple independent submissions, and the varied nature of diverse data types which are often stored in different databases.
The BioSample database contains descriptions of biological source materials used in experimental assays.
A collection of biomedical books that can be searched directly or from linked data in other NCBI databases. The collection includes biomedical textbooks, other scientific titles, genetic resources such as GeneReviews, and NCBI help manuals.
A resource to provide a public, tracked record of reported relationships between human variation and observed health status with supporting evidence. Related information intheNIH Genetic Testing Registry (GTR),MedGen,Gene,OMIM,PubMedand other sources is accessible through hyperlinks on the records.
A registry and results database of publicly- and privately-supported clinical studies of human participants conducted around the world.
A centralized page providing access and links to resources developed by the Structure Group of the NCBI Computational Biology Branch (CBB). These resources cover databases and tools to help in the study of macromolecular structures, conserved domains and protein classification, small molecules and their biological activity, and biological pathways and systems.
A collaborative effort to identify a core set of human and mouse protein coding regions that are consistently annotated and of high quality.
A collection of sequence alignments and profiles representing protein domains conserved in molecular evolution. It also includes alignments of the domains to known 3-dimensional protein structures in the MMDB database.
The dbVar database has been developed to archive information associated with large scale genomic variation, including large insertions, deletions, translocations and inversions. In addition to archiving variation discovery, dbVar also stores associations of defined variants with phenotype information.
An archive and distribution center for the description and results of studies which investigate the interaction of genotype and phenotype. These studies include genome-wide association (GWAS), medical resequencing, molecular diagnostic assays, as well as association between genotype and non-clinical traits.
Includes single nucleotide variations, microsatellites, and small-scale insertions and deletions. dbSNP contains population-specific frequency and genotype data, experimental conditions, molecular context, and mapping information for both neutral variations and clinical mutations.
The NIH genetic sequence database, an annotated collection of all publicly available DNA sequences. GenBank is part of the International Nucleotide Sequence Database Collaboration, which comprises the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at NCBI. These three organizations exchange data on a daily basis. GenBank consists of several divisions, most of which can be accessed through the Nucleotide database. The exceptions are the EST and GSS divisions, which are accessed through the Nucleotide EST and Nucleotide GSS databases, respectively.
A searchable database of genes, focusing on genomes that have been completely sequenced and that have an active research community to contribute gene-specific data. Information includes nomenclature, chromosomal localization, gene products and their attributes (e.g., protein interactions), associated markers, phenotypes, interactions, and links to citations, sequences, variation details, maps, expression reports, homologs, protein domain content, and external databases.
A public functional genomics data repository supporting MIAME-compliant data submissions. Array- and sequence-based data are accepted and tools are provided to help users query and download experiments and curated gene expression profiles.
Stores curated gene expression and molecular abundance DataSets assembled from the Gene Expression Omnibus (GEO) repository. DataSet records contain additional resources, including cluster tools and differential expression queries.
Stores individual gene expression and molecular abundance Profiles assembled from the Gene Expression Omnibus (GEO) repository. Search for specific profiles of interest based on gene annotation or pre-computed profile characteristics.
A collection of expert-authored, peer-reviewed disease descriptions on the NCBI Bookshelf that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions.
Summaries of information for selected genetic disorders with discussions of the underlying mutation(s) and clinical features, as well as links to related databases and organizations.
A voluntary registry of genetic tests and laboratories, with detailed information about the tests such as what is measured and analytic and clinical validity. GTR also is a nexus for information about genetic conditions and provides context-specific links to a variety of resources, including practice guidelines, published literature, and genetic data/information. The initial scope of GTR includes single gene tests for Mendelian disorders, as well as arrays, panels and pharmacogenetic tests.
Contains sequence and map data from the whole genomes of over 1000 organisms. The genomes represent both completely sequenced organisms and those for which sequencing is in progress. All three main domains of life (bacteria, archaea, and eukaryota) are represented, as well as many viruses, phages, viroids, plasmids, and organelles.
The Genome Reference Consortium (GRC) maintains responsibility for the human and mouse reference genomes. Members consist of The Genome Center at Washington University, the Wellcome Trust Sanger Institute, the European Bioinformatics Institute (EBI) and the National Center for Biotechnology Information (NCBI). The GRC works to correct misrepresented loci and to close remaining assembly gaps. In addition, the GRC seeks to provide alternate assemblies for complex or structurally variant genomic loci. At the GRC website (http://www.genomereference.org), the public can view genomic regions currently under review, report genome-related problems and contact the GRC.
A centralized page providing access and links to glycoinformatics and glycobiology related resources.
A database of known interactions of HIV-1 proteins with proteins from human hosts. It provides annotated bibliographies of published reports of protein interactions, with links to the corresponding PubMed records and sequence data.
A collection of consolidated records describing proteins identified in annotated coding regions in GenBank and RefSeq, as well as SwissProt and PDB protein sequences. This resource allows investigators to obtain more targeted search results and quickly identify a protein of interest.
A compilation of data from the NIAID Influenza Genome Sequencing Project and GenBank. It provides tools for flu sequence analysis, annotation and submission to GenBank. This resource also has links to other flu sequence resources, and publications and general information about flu viruses.
Subset of the NLM Catalog database providing information on journals that are referenced in NCBI database records, including PubMed abstracts. This subset can be searched using the journal title, MEDLINE or ISO abbreviation, ISSN, or the NLM Catalog ID.
MeSH (Medical Subject Headings) is the U.S. National Library of Medicine's controlled vocabulary for indexing articles for MEDLINE/PubMed. MeSH terminology provides a consistent way to retrieve information that may use different terminology for the same concepts.
A portal to information about medical genetics. MedGen includes term lists from multiple sources and organizes them into concept groupings and hierarchies. Links are also provided to information related to those concepts in the NIH Genetic Testing Registry (GTR), ClinVar,Gene, OMIM, PubMed, and other sources.
A comprehensive manual on the NCBI C++ toolkit, including its design and development framework, a C++ library reference, software examples and demos, FAQs and release notes. The manual is searchable online and can be downloaded as a series of PDF documents.
Provides links to tutorials and training materials, including PowerPoint slides and print handouts.
Part of the NCBI Handbook, this glossary contains descriptions of NCBI tools and acronyms, bioinformatics terms and data representation formats.
An extensive collection of articles about NCBI databases and software. Designed for a novice user, each article presents a general overview of the resource and its design, along with tips for searching and using available analysis tools. All articles can be searched online and downloaded in PDF format; the handbook can be accessed through the NCBI Bookshelf.
Accessed through the NCBI Bookshelf, the Help Manual contains documentation for many NCBI resources, including PubMed, PubMed Central, the Entrez system, Gene, SNP and LinkOut. All chapters can be downloaded in PDF format.
A project involving the collection and analysis of bacterial pathogen genomic sequences originating from food, environmental and patient isolates. Currently, an automated pipeline clusters and identifies sequences supplied primarily by public health laboratories to assist in the investigation of foodborne disease outbreaks and discover potential sources of food contamination.
Bibliographic data for all the journals, books, audiovisuals, computer software, electronic resources and other materials that are in the library's holdings.
A collection of nucleotide sequences from several sources, including GenBank, RefSeq, the Third Party Annotation (TPA) database, and PDB. Searching the Nucleotide Database will yield available results from each of its component databases.
A database of human genes and genetic disorders. NCBI maintains current content and continues to support its searching and integration with other NCBI databases. However, OMIM now has a new home at omim.org, and users are directed to this site for full record displays.
Database of related DNA sequences that originate from comparative studies: phylogenetic, population, environmental and, to a lesser degree, mutational. Each record in the database is a set of DNA sequences. For example, a population set provides information on genetic variation within an organism, while a phylogenetic set may contain sequences, and their alignment, of a single gene obtained from several related organisms.
A collection of related protein sequences (clusters), consisting of Reference Sequence proteins encoded by complete prokaryotic and organelle plasmids and genomes. The database provides easy access to annotation information, publications, domains, structures, external links, and analysis tools.
A database that includes protein sequence records from a variety of sources, including GenPept, RefSeq, Swiss-Prot, PIR, PRF, and PDB.
A database that includes a collection of models representing homologous proteins with a common function. It includes conserved domain architecture, hidden Markov models and BlastRules. A subset of these models are used by the Prokaryotic Genome Annotation Pipeline (PGAP) to assign names and other attributes to predicted proteins.
Consists of deposited bioactivity data and descriptions of bioactivity assays used to screen the chemical substances contained in the PubChem Substance database, including descriptions of the conditions and the readouts (bioactivity levels) specific to the screening procedure.
Contains unique, validated chemical structures (small molecules) that can be searched using names, synonyms or keywords. The compound records may link to more than one PubChem Substance record if different depositors supplied the same structure. These Compound records reflect validated chemical depiction information provided to describe substances in PubChem Substance. Structures stored within PubChem Compounds are pre-clustered and cross-referenced by identity and similarity groups. Additionally, calculated properties and descriptors are available for searching and filtering of chemical structures.
PubChem Substance records contain substance information electronically submitted to PubChem by depositors. This includes any chemical structure information submitted, as well as chemical names, comments, and links to the depositor's web site.
A database of citations and abstracts for biomedical literature from MEDLINE and additional life science journals. Links are provided when full text versions of the articles are available via PubMed Central (described below) or other websites.
A digital archive of full-text biomedical and life sciences journal literature, including clinical medicine and public health.
A collection of curated, non-redundant genomic DNA, transcript (RNA), and protein sequences produced by NCBI. RefSeqs provide a stable reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. The RefSeq collection is accessed through the Nucleotide and Protein databases.
A collection of resources specifically designed to support the research of retroviruses, including a genotyping tool that uses the BLAST algorithm to identify the genotype of a query sequence; an alignment tool for global alignment of multiple sequences; an HIV-1 automatic sequence annotation tool; and annotated maps of numerous retroviruses viewable in GenBank, FASTA, and graphic formats, with links to associated sequence records.
A summary of data for the SARS coronavirus (CoV), including links to the most recent sequence data and publications, links to other SARS related resources, and a pre-computed alignment of genome sequences from various isolates.
The Sequence Read Archive (SRA) stores sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Life Technologies AB SOLiD System, Helicos Biosciences Heliscope, Complete Genomics, and Pacific Biosciences SMRT.
Contains macromolecular 3D structures derived from the Protein Data Bank, as well as tools for their visualization and comparative analysis.
Contains the names and phylogenetic lineages of more than 160,000 organisms that have molecular data in the NCBI databases. New taxa are added to the Taxonomy database as data are deposited for them.
A database that contains sequences built from the existing primary sequence data in GenBank. The sequences and corresponding annotations are experimentally supported and have been published in a peer-reviewed scientific journal. TPA records are retrieved through the Nucleotide Database.
A repository of DNA sequence chromatograms (traces), base calls, and quality estimates for single-pass reads from various large-scale sequencing projects.
A wide range of resources, including a brief summary of the biology of viruses, links to viral genome sequences in Entrez Genome, and information about viral Reference Sequences, a collection of reference sequences for thousands of viral genomes.
An extension of the Influenza Virus Resource to other organisms, providing an interface to download sequence sets of selected viruses, analysis tools, including virus-specific BLAST pages, and genome annotation pipelines.
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All Resources - Site Guide - NCBI - National Center for Biotechnology ...
Read More..Two years ago, we issued our first draft of that map to take our first steps: our ambitious three-year plan to develop quantum computing technology, called our development roadmap. Since then, our exploration has revealed new discoveries, gaining us insights that have allowed us to refine that map and travel even further than wed planned. Today, were excited to present to you an update to that map: our plan to weave quantum processors, CPUs, and GPUs into a compute fabric capable of solving problems beyond the scope of classical resources alone.
Our goal is to build quantum-centric supercomputers. The quantum-centric supercomputer will incorporate quantum processors, classical processors, quantum communication networks, and classical networks, all working together to completely transform how we compute. In order to do so, we need to solve the challenge of scaling quantum processors, develop a runtime environment for providing quantum calculations with increased speed and quality, and introduce a serverless programming model to allow quantum and classical processors to work together frictionlessly.
But first: where did this journey begin? We put the first quantum computer on the cloud in 2016, and in 2017, we introduced an open source software development kit for programming these quantum computers, called Qiskit. We debuted the first integrated quantum computer system, called the IBM Quantum System One, in 2019, then in 2020 we released our development roadmap showing how we planned to mature quantum computers into a commercial technology.
As part of that roadmap, in 2021 we released our IBM Quantum broke the 100qubit processor barrier in 2021. Read more about Eagle.127-qubit IBM Quantum Eagle processor and launched Qiskit Runtime, a runtime environment of co-located classical systems and quantum systems built to support containerized execution of quantum circuits at speed and scale. The first version gave a In 2021, we demonstrated a 120x speedup in simulating molecules thanks to a host of improvements, including the ability to run quantum programs entirely on the cloud with Qiskit Runtime.120x speedup on a research-grade quantum workload. Earlier this year, we launched the Qiskit Runtime Services with primitives: pre-built programs that allow algorithm developers easy access to the outputs of quantum computations without requiring intricate understanding of the hardware.
Now, our updated map will show us the way forward.
In order to benefit from our world-leading hardware, we need to develop the software and infrastructure so that our users can take advantage of it. Different users have different needs and experiences, and we need to build tools for each persona: kernel developers, algorithm developers, and model developers.
For our kernel developers those who focus on making faster and better quantum circuits on real hardware well be delivering and maturing Qiskit Runtime. First, we will add dynamic circuits, which allow for feedback and feedforward of quantum measurements to change or steer the course of future operations. Dynamic circuits extend what the hardware can do by reducing circuit depth, by allowing for alternative models of constructing circuits, and by enabling parity checks of the fundamental operations at the heart of quantum error correction.
To continue to increase the speed of quantum programs in 2023, we plan to bring threads to the Qiskit Runtime, allowing us to operate parallelized quantum processors, including automatically distributing work that is trivially parallelizable. In 2024 and 2025, well introduce error mitigation and suppression techniques into Qiskit Runtime so that users can focus on improving the quality of the results obtained from quantum hardware. These techniques will help lay the groundwork for quantum error correction in the future.
However, we have work to do if we want quantum will find broader use, such as among our algorithm developers those who use quantum circuits within classical routines in order to make applications that demonstrate quantum advantage.
For our algorithm developers, well be maturing the Qiskit Runtime Services primitives. The unique power of quantum computers is their ability to generate non-classical probability distributions at their outputs. Consequently, much of quantum algorithm development is related to sampling from, or estimating properties of these distributions. The primitives are a collection of core functions to easily and efficiently work with these distributions.
Typically, algorithm developers require breaking problems into a series of smaller quantum and classical programs, with an orchestration layer to stitch the data streams together into an overall workflow. We call the infrastructure responsible for this stitching To bring value to our users, we need our programing model to fit seamlessly into their workflows, where they can focus on their code and not have to worry about the deployment and infrastructure. We need a serverless architecture.Quantum Serverless. Quantum Serverless centers around enabling flexible quantum-classical resource combinations without requiring developers to be hardware and infrastructure experts, while allocating just those computing resources a developer needs when they need them. In 2023, we plan to integrate Quantum Serverless into our core software stack in order to enable core functionality such as circuit knitting.
What is circuit knitting? Circuit knitting techniques break larger circuits into smaller pieces to run on a quantum computer, and then knit the results back together using a classical computer.
Earlier this year, we demonstrated a circuit knitting method called entanglement forging to double the size of the quantum systems we could address with the same number of qubits. However, circuit knitting requires that we can run lots of circuits split across quantum resources and orchestrated with classical resources. We think that parallelized quantum processors with classical communication will be able to bring about quantum advantage even sooner, and a recent paper suggests a path forward.
With all of these pieces in place, well soon have quantum computing ready for our model developers those who develop quantum applications to find solutions to complex problems in their specific domains. We think by next year, well begin prototyping quantum software applications for specific use cases. Well begin to define these services with our first test case machine learning working with partners to accelerate the path toward useful quantum software applications. By 2025, we think model developers will be able to explore quantum applications in machine learning, optimization, natural sciences, and beyond.
Of course, we know that central to quantum computing is the hardware that makes running quantum programs possible. We also know that a quantum computer capable of reaching its full potential could require hundreds of thousands, maybe millions of high-quality qubits, so we must figure out how to scale these processors up. With the 433-qubit Osprey processor and the 1,121-qubit Condor processors slated for release in 2022 and 2023, respectively we will test the limits of single-chip processors and controlling large-scale quantum systems integrated into the IBM Quantum System Two. But we dont plan to realize large-scale quantum computers on a giant chip. Instead, were developing ways to link processors together into a modular system capable of scaling without physics limitations.
To tackle scale, we are going to introduce three distinct approaches. First, in 2023, we are introducing Heron: a 133-qubit processor with control hardware that allows for real-time classical communication between separate processors, enabling the knitting techniques described above. The second approach is to extend the size of quantum processors by enabling multi-chip processors. Crossbill, a 408 qubit processor, will be made from three chips connected by chip-to-chip couplers that allow for a continuous realization of the heavy-hex lattices across multiple chips. The goal of this architecture is to make users feel as if theyre just using just one, larger processor.
Along with scaling through modular connection of multi-chip processors, in 2024, we also plan to introduce our third approach: quantum communication between processors to support quantum parallelization. We will introduce the 462-qubit Flamingo processor with a built-in quantum communication link, and then release a demonstration of this architecture by linking together at least three Flamingo processors into a 1,386-qubit system. We expect that this link will result in slower and lower-fidelity gates across processors. Our software needs to be aware of this architecture consideration in order for our users to best take advantage of this system.
Our learning about scale will bring all of these advances together in order to realize their full potential. So, in 2025, well introduce the Kookaburra processor. Kookaburra will be a 1,386 qubit multi-chip processor with a quantum communication link. As a demonstration, we will connect three Kookaburra chips into a 4,158-qubit system connected by quantum communication for our users.
The combination of these technologies classical parallelization, multi-chip quantum processors, and quantum parallelization gives us all the ingredients we need to scale our computers to wherever our roadmap takes. By 2025, we will have effectively removed the main boundaries in the way of scaling quantum processors up with modular quantum hardware and the accompanying control electronics and cryogenic infrastructure. Pushing modularity in both our software and our hardware will be key to achieving scales well ahead of our competitors, and were excited to deliver it to you.
Our updated roadmap takes us as far as 2025 but development wont stop there. By then, we will have removed some of the biggest roadblocks in the way of scaling quantum hardware, while developing the tools and techniques capable of integrating quantum into computing workflows. This sea change will be the equivalent of replacing paper maps with GPS satellites as we navigate into the quantum future.
This sea change will be the equivalent of replacing paper maps with GPS satellites.
We arent just thinking about quantum computers, though. Were trying to induce a paradigm shift in computing overall. For many years, CPU-centric supercomputers were societys processing workhorse, with IBM serving as a key developer of these systems. In the last few years, weve seen the emergence of AI-centric supercomputers, where CPUs and GPUs work together in giant systems to tackle AI-heavy workloads.
Now, IBM is ushering in the age of the quantum-centric supercomputer, where quantum resources QPUs will be woven together with CPUs and GPUs into a compute fabric. We think that the quantum-centric supercomputer will serve as an essential technology for those solving the toughest problems, those doing the most ground-breaking research, and those developing the most cutting-edge technology.
We may be on track, but exploring uncharted territory isnt easy. Were attempting to rewrite the rules of computing in just a few years. Following our roadmap will require us to solve some incredibly tough engineering and physics problems.
But were feeling pretty confident weve gotten this far, after all, with the new help of our world-leading team of researchers, the IBM Quantum Network, the Qiskit open source community, and our growing community of kernel, algorithm, and model developers. Were glad to have you all along for the ride as we continue onward.
Quantum Chemistry: Few fields will get value from quantum computing as quickly as chemistry. Even todays supercomputers struggle to model a single molecule in its full complexity. We study algorithms designed to do what those machines cant.
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IBM Quantum roadmap to build quantum-centric supercomputers | IBM ...
June 25, 2022Quantum computing, an emerging technology that uses the laws of quantum mechanics to produce exponentially higher performance for certain types of calculations, offers the possibility of major breakthroughs across sectors. Investors also see these possibilities: Funding of start-ups focused on quantum technologies more than doubled to $1.4 billion in 2021 from 2020. Quantum computing now has the potential to capture nearly $700 billion in value as early as 2035, with that market estimated to exceed $90 billion annually by 2040. That said, quantum computings more powerful computers could also one day pose a cybersecurity risk. To learn more, dive deeper into these topics:
Quantum computing funding remains strong, but talent gap raises concern
Quantum computing use cases are getting realwhat you need to know
Quantum computing just might save the planet
How quantum computing can help tackle global warming
How quantum computing could change financial services
Pharmas digital Rx: Quantum computing in drug research and development
Will quantum computing drive the automotive future?
A quantum wake-up call for European CEOs
Whenand howto prepare for post-quantum cryptography
Leading the way in quantum computing
Redefine your career at QuantumBlack
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How quantum computing could change the world | McKinsey & Company
This page discusses concepts related to uploading and downloading objects. Youcan upload and store any MIME type of data up to 5 TiB in size.
You can send upload requests to Cloud Storage in the following ways:
Single-request upload. An upload method where an object is uploadedas a single request. Use this if the file is small enough toupload in its entirety if the connection fails.
Resumable upload. An upload method that provides a more reliabletransfer, which is especially important with large files. Resumable uploadsare a good choice for most applications, since they also work for small filesat the cost of one additional HTTP request per upload. You can also useresumable uploads to perform streaming transfers, which allows you to uploadan object of unknown size.
XML API multipart upload. An upload method that is compatible withAmazon S3 multipart uploads. Files are uploaded in parts and assembled intoa single object with the final request. XML API multipart uploads allow you toupload the parts in parallel, potentially reducing the time to complete theoverall upload.
Using these basic upload types, more advanced upload strategies are possible:
Parallel composite upload. An upload strategy in which you chunk afile and upload the chunks in parallel. Unlike XML API multipart uploads,parallel composite uploads use the compose operation, and thefinal object is stored as a composite object.
Streaming upload. An upload method that lets you upload data withoutrequiring that the data first be saved to a file, which is useful when youdon't know the final size at the start of the upload.
When choosing whether to use a single-request upload instead of a resumableupload or XML API multipart upload, consider the amount of time that you'rewilling to lose should a network failure occur and you need to restart theupload from the beginning. For faster connections, your cutoff size cantypically be larger.
For example, say you're willing to tolerate 30 seconds of lost time:
If you upload from a local system with an average upload speed of 8 Mbps, youcan use single-request uploads for files as large as 30 MB.
If you upload from an in-region service that averages 500 Mbps for its uploadspeed, the cutoff size for files is almost 2 GB.
All downloads from Cloud Storage have the same basic behavior: anHTTP or HTTPS GET request that can include an optional Range header, whichdefines a specific portion of the object to download.
Using this basic download behavior, you can resume interrupted downloads, andyou can utilize more advanced download strategies, such assliced object downloads and streaming downloads.
If you use REST APIs to upload and download, see Request endpoints fora complete discussion on the request endpoints you can use.
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Uploads and downloads | Cloud Storage | Google Cloud