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Long gone are times in business when all the data you needed was in your little black book. In this era of the digital revolution, not even the classical databases are enough.

Handling big data became a critical skill for businesses and, with them, data scientists. Big data is characterized by its volume, velocity, and variety, offering unprecedented insights into patterns and trends.

To handle such data effectively, it requires the usage of specialized tools and techniques.

No, its not simply lots of data.

Big data is most commonly characterized by the three Vs:

All the big data characteristics mentioned impact the tools and techniques we use to handle big data.

When we talk about big data techniques, they are simply methods, algorithms, and approaches we use to process, analyze, and manage big data. On the surface, they are the same as in regular data. However, the big data characteristics we discussed call for different approaches and tools.

Here are some prominent tools and techniques used in the big data domain.

What is it?: Data processing refers to operations and activities that transform raw data into meaningful information. It tasks from cleaning and structuring data to running complex algorithms and analytics.

Big data is sometimes batch processed, but more prevalent is data streaming.

Key Characteristics:

Big Data Tools Used: Apache Hadoop MapReduce, Apache Spark, Apache Tez, Apache Kafka, Apache Storm, Apache Flink, Amazon Kinesis, IBM Streams, Google Cloud Dataflow

Tools Overview:

What is it?: ETL is Extracting data from various sources, Transforming it into a structured and usable format, and Loading it into a data storage system for analysis or other purposes.

Big data characteristics mean that the ETL process needs to handle more data from more sources. Data is usually semi-structured or unstructured, which is transformed and stored differently than structured data.

ETL in big data also usually needs to process data in real time.

Key Characteristics:

Big Data Tools Used: Apache NiFi, Apache Sqoop, Apache Flume, Talend

Tools Overview:

Data provenance tracking

Extensible architecture with processors

Supports data provenance

Extensible with a wide range of processors

Parallel import/export

Compression and direct import features

Parallel import/export

Incremental data transfer capabilities

Reliable and durable data delivery

Native integration with Hadoop ecosystem

Fault-tolerant architecture

Extensible with custom sources, channels, and sinks.

Broad connectivity to databases, apps, and more

Data quality and profiling tools

Graphical interface for designing data integration processes

Supports data quality and master data management

What is it?: Big data storage must store vast amounts of data generated at high velocities and in various formats.

The three most distinct ways to store big data are NoSQL databases, data lakes, and data warehouses.

NoSQL databases are designed for handling large volumes of structured and unstructured data without a fixed schema (NoSQL - Not Only SQL). This makes them adaptable to the evolving data structure.

Unlike traditional, vertically scalable databases, NoSQL databases are horizontally scalable, meaning they can distribute data across multiple servers. Scaling becomes easier by adding more machines to the system. They are fault-tolerant, have low latency (appreciated in applications requiring real-time data access), and are cost-efficient at scale.

Data lakes are storage repositories that store vast amounts of raw data in their native format. This simplifies data access and analytics, as all data is located in one place.

Data lakes are scalable and cost-efficient. They provide flexibility (data is ingested in its raw form, and the structure is defined when reading the data for analysis), support batch and real-time data processing, and can be integrated with data quality tools, leading to more advanced analytics and richer insights.

A data warehouse is a centralized repository optimized for analytical processing that stores data from multiple sources, transforming it into a format suitable for analysis and reporting.

It is designed to store vast amounts of data, integrate it from various sources, and allow for historical analysis since data is stored with a time dimension.

Key Characteristics:

Big Data Tools Used: MongoDB (document-based), Cassandra (column-based), Apache HBase (column-based), Neo4j (graph-based), Redis (key-value store), Amazon S3, Azure Data Lake, Hadoop Distributed File System (HDFS), Google Big Lake, Amazon Redshift, BigQuery

Tools Overview:

What is it?: Its discovering patterns, correlations, anomalies, and statistical relationships in large datasets. It involves disciplines like machine learning, statistics, and using database systems to extract insights from data.

The amount of data mined is vast, and the sheer volume can reveal patterns that might not be apparent in smaller datasets. Big data usually comes from various sources and is often semi-structured or unstructured. This requires more sophisticated preprocessing and integration techniques. Unlike regular data, big data is usually processed in real time.

Tools used for big data mining have to handle all this. To do that, they apply distributed computing, i.e., data processing is spread across multiple computers.

Some algorithms might not be suitable for big data mining, as it requires scalable parallel processing algorithms, e.g., SVM, SGD, or Gradient Boosting.

Big data mining has also adopted Exploratory Data Analysis (EDA) techniques. EDA analyzes datasets to summarize their main characteristics, often using statistical graphics, plots, and information tables. Because of that, well talk about big data mining and EDA tools together.

Key Characteristics:

Big Data Tools Used: Weka, KNIME, RapidMiner, Apache Hive, Apache Pig, Apache Drill, Presto

Tools Overview:

What is it?: Its a graphical representation of information and data extracted from vast datasets. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to understand patterns, outliers, and trends in the data.

Again, the characteristics of big data data, such as size and complexity, make it different from regular data visualization.

Key Characteristics:

Big Data Tools Used: Tableau, PowerBI, D3.js, Kibana

Tools Overview:

Big data is so similar to regular data but also completely different. They share the techniques for handling data. But due to big data characteristics, these techniques are the same only by their name. Otherwise, they require completely different approaches and tools.

If you want to get into big data, youll have to use various big data tools. Our overview of these tools should be a good starting point for you.Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.

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Working with Big Data: Tools and Techniques - KDnuggets