The world of big data is only getting bigger. Organizations of all stripes are producing more data year after year, and they're finding more ways to use that data to improve operations, better understand customers, and deliver products faster and at lower costs, among other applications. In addition, business executives looking to get value from data faster are seeking real-time analytics capabilities.
That's all driving significant investments in big data tools and technologies. A report published in January 2021 by IT research and analysis firm Mind Commerce estimated that the global market for big data in business intelligence applications will amount to $50.4 billion by 2026.
The list of big data technologies is long, with numerous commercial products available to help organizations implement a full range of data-driven analytics initiatives -- from real-time reporting to machine learning applications.
However, many big data tools are open source; some of them are also offered in commercial versions or as part of big data platforms and managed services. Here's a look at 15 popular open source tools and technologies for managing and analyzing big data, listed in alphabetical order with a summary of their key features and capabilities.
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1. Delta Lake
Databricks Inc., a software vendor founded by the creators of the Spark processing engine, developed Delta Lake and then opened sourced the Spark-based technology in 2019 through the Linux Foundation. The company describes Delta Lake as "an open format storage layer that delivers reliability, security and performance on your data lake for both streaming and batch operations."
Delta Lake doesn't replace data lakes; rather, it's designed to sit on top of them and create a single home for structured, semistructured and unstructured data, eliminating data silos that can stymie big data applications. Furthermore, using Delta Lake can help prevent data corruption, enable faster queries, increase data freshness and support compliance efforts, according to Databricks. The technology supports ACID transactions, stores data in an open Apache Parquet format and includes Spark-compatible APIs.
The Apache Software Foundation's Drill website describes it as "a low latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data." Drill can scale across thousands of cluster nodes and is capable of querying petabytes of data by using SQL and standard connectivity APIs.
Designed for exploring sets of big data, Drill layers on top of multiple data sources, enabling users to query a wide range of data in different formats, from Hadoop sequence files and server logs to NoSQL databases and cloud object storage. It can also access most relational databases through a plugin, and it works with commonly used BI tools, such as Tableau and Qlik. Although Drill requires Apache's ZooKeeper software to maintain information about clusters, it can run in any distributed cluster environment.
Another Apache open source technology, Flink is a stream processing framework for distributed, high-performing and always-available applications. It supports stateful computations over both bounded and unbounded data streams and can be used for batch, graph and iterative processing.
One of the main benefits touted by Flink's proponents is its speed: It can process millions of events in real time for low latency and high throughput. Flink, which is designed to run in all common cluster environments, provides three layers of APIs and a set of libraries for complex event processing, machine learning and other common big data use cases.
A distributed framework for storing data and running applications on clusters of commodity hardware, Hadoop was developed as a pioneering big data technology to help handle the growing volumes of structured, unstructured and semistructured data. First released in 2006, it was almost synonymous with big data early on; it has since been partially eclipsed by other technologies but is still widely used.
Hadoop has four primary components:
- the Hadoop Distributed File System (HDFS), which splits data into blocks for storage on the nodes in a cluster, uses replication methods to prevent data loss and manages access to the data;
- YARN, short for Yet Another Resource Negotiator, which schedules jobs to run on cluster nodes and allocates system resources to them;
- MapReduce, a built-in batch processing engine that splits up large computations and runs them on different nodes for speed and load balancing; and
- Hadoop Common, a shared set of utilities and libraries.
Initially, Hadoop was limited to running MapReduce batch applications. The addition of YARN in 2013 opened it up to other processing engines and use cases, but the framework is still closely associated with MapReduce. The broader Apache Hadoop ecosystem also includes various big data tools and additional frameworks for processing, managing and analyzing big data.
Hive is SQL-based data warehouse infrastructure software for reading, writing and managing large data sets in distributed storage environments. It was created by Facebook but then open sourced to Apache, which continues to develop and maintain the technology.
Hive runs on top of Hadoop and is used to process structured data; more specifically, it's used for data summarization and analysis, as well as for querying large amounts of data. Although it can't be used for online transaction processing, real-time updates, and queries or jobs that require low-latency data retrieval, Hive is described by its developers as scalable, fast and flexible.
6. HPCC Systems
HPCC Systems is a big data processing platform developed by LexisNexis before being open sourced in 2011. True to its full name -- High-Performance Computing Cluster -- the technology is, at its core, a cluster of computers built from commodity hardware to process, manage and deliver big data.
A production-ready data lake platform that enables rapid development and data exploration, HPCC Systems includes three main components:
- Thor, a data refinery engine that's used to cleanse, merge and transform data, and to profile, analyze and ready it for use in queries;
- Roxie, a data delivery engine used to serve up prepared data from the refinery; and
- Enterprise Control Language (ECL), a programming language for developing applications.
Hudi (pronounced hoodie) is short for Hadoop Upserts Deletes and Incrementals. Another open source technology maintained by Apache, it's used to manage the ingestion and storage of large analytics data sets on Hadoop-compatible file systems, including HDFS and cloud object storage services.
First developed by Uber, Hudi is designed to provide efficient and low-latency data ingestion and data preparation capabilities. Moreover, it includes a data management framework that organizations can use to simplify incremental data processing and data pipeline development, improve data quality and manage the data lifecycle.
Iceberg is an open table format used to manage data in data lakes, which it does partly by tracking individual data files in tables rather than by tracking directories. Created by Netflix for use with the company's petabyte-sized tables, Iceberg is now an Apache project. According to the project's website, Iceberg typically "is used in production where a single table can contain tens of petabytes of data."
Designed to improve on the standard layouts that exist within tools like Hive, Presto, Spark and Trino, the Iceberg table format has functions similar to SQL tables in relational databases. However, it also accommodates multiple engines operating on the same data set.
Kafka is a distributed event streaming platform that, according to Apache, is used by more than 80% of Fortune 100 companies and thousands of other organizations for high-performance data pipelines, streaming analytics, data integration and mission-critical applications. In simpler terms, Kafka is a framework for storing, reading and analyzing streaming data.
The technology decouples data streams and systems, holding the data streams so they can then be used elsewhere. It runs in a distributed environment and uses a high-performance TCP network protocol to communicate with systems and applications. Kafka was created by LinkedIn before being passed on to Apache in 2011.
Kylin is a distributed data warehouse and analytics platform for big data. It provides an online analytical processing, or OLAP, engine designed to support extremely large data sets. Because Kylin is built on top of other Apache technologies -- including Hadoop, Hive, Parquet and Spark -- it can easily scale to handle those large data loads, according to its backers.
It's also fast, delivering query responses measured in milliseconds. In addition, Kylin provides a simple interface for multidimensional analysis of big data and integrates with Tableau, Microsoft Power BI and other BI tools. Kylin was developed by eBay, which contributed it as an open source technology in 2015.
Formerly known as PrestoDB, this open source SQL query engine can simultaneously handle both fast queries and large data volumes in distributed data sets. Presto is optimized for low-latency interactive querying and scales to support analytics applications across multiple petabytes of data in data warehouses and other repositories.
Development of Presto began at Facebook in 2012. When its creators left the company in 2018, the technology split into two branches: PrestoDB, which was still led by Facebook, and PrestoSQL, which the original developers launched. That continued until December 2020, when PrestoSQL was renamed Trino and PrestoDB reverted to the Presto name. The Presto open source project is now overseen by the Presto Foundation, which was set up as part of the Linux Foundation in 2019.
Samza is a distributed stream processing system that was built by LinkedIn and is now an open source project managed by Apache. According to the project website, Samza enables users to build stateful applications that can do real-time processing of data from Kafka, HDFS and other sources.
The system can run on top of Hadoop YARN or Kubernetes and also offers a standalone deployment option. The Samza site says it can handle "several terabytes" of state data, with low latency and high throughput for fast data analysis. It can also use the same code written for data streaming jobs to run batch applications. LinkedIn open sourced Samza in 2013.
Spark is an in-memory data processing and analytics engine that can run on clusters managed by Hadoop YARN, Mesos and Kubernetes or in a standalone mode. It enables large-scale data transformations and analysis and can be used for both batch and streaming applications, as well as machine learning and graph processing use cases, all supported by a set of built-in modules and libraries.
Data can be accessed from various sources, including HDFS, relational and NoSQL databases, and flat-file data sets. Spark also supports various file formats and offers a diverse set of APIs for developers.
But its biggest calling card is speed: Spark's developers claim it can perform up to 100 times faster than traditional counterpart MapReduce on batch jobs when processing in memory. As a result, Spark has become the top choice for many batch applications in big data environments, while also functioning as a general-purpose engine. First developed at the University of California, Berkeley and now maintained by Apache, it can also process on disk when data sets are too large to fit into the available memory.
Another Apache open source technology, Storm is a distributed real-time computation system that's designed to reliably process unbounded streams of data. According to the project website, it can be used for applications that include real-time analytics, online machine learning and continuous computation, as well as extract, transform and load (ETL) jobs.
Storm clusters are akin to Hadoop ones, but applications continue to run on an ongoing basis unless they're stopped. The system is fault-tolerant and guarantees that data will be processed. In addition, the Storm site says it can be used with any programming language, message queueing system and database.
As mentioned above, Trino is one of the two branches of the Presto query engine. Known as PrestoSQL until it was rebranded in December 2020, Trino "runs at ludicrous speed," in the words of the Trino Software Foundation. That group, which oversees Trino's development, was originally formed in 2019 as the Presto Software Foundation; its name was also changed as part of the rebranding.
Trino enables users to query data regardless of where it's stored, and is built for both ad hoc interactive analytics and long-running batch queries. Data from multiple systems can be combined in queries, and the software works with Tableau, Power BI, R and other BI and analytics tools.