Numerous tools are available to use in big data applications. Here’s a look at 18 popular open-source technologies, plus additional information on NoSQL databases.
The world of big data is only getting bigger: Organizations of all stripes are producing more data, in various forms, year after year. The ever-increasing volume and variety of data are driving companies to invest more in big data tools and technologies as they use all that data to improve operations, better understand customers, deliver products faster, and gain other business benefits through analytics applications.
Enterprise data leaders have a multitude of choices on big data technologies, 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.
Big data tools, which companies offer as open source, in commercial versions, or as part of big data platforms and managed services, along with others offering similar tools. Here are 18 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.
1. Airflow: Big Data
Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems. Data engineers and other users use Airflow to ensure that they execute each task in a workflow in the designated order and have access to the required system resources. Users promote Airflow as easy to use because they create workflows in the Python programming language and use it for building machine learning models, transferring data, and various other purposes.
In mid-2015, Airbnb officially announced the open-source technology that had originated on its platform in late 2014. it joined the Apache Software Foundation’s incubator program the following year and became an Apache top-level project in 2019. Airflow also includes the following key features:
A modular and scalable architecture built around the concept of directed acyclic graphs (DAGs), which illustrate the dependencies between the different tasks in workflows.
A web application UI to visualize data pipelines, monitor their production status, and troubleshoot problems.
Ready-made integrations with major cloud platforms and other third-party services.
2. Delta Lake: Big Data
Databricks Inc., a software vendor founded by the creators of the Spark processing engine, developed Delta Lake and then open-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; Using Delta Lake can greatly reduce data silos that stymie big data applications. Additionally, it can prevent data corruption, enable faster queries, increase data freshness, and support compliance efforts, as stated by Databricks. The technology also comes with the following features:
Support for ACID transactions, meaning those with atomicity, consistency, isolation, and durability.
The ability to store data in an open Apache Parquet format.
A set of Spark-compatible APIs.
3. Drill: Big Data
The Apache Drill website describes it as “a low latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data.” A 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 do the following:
Access most relational databases through a plugin.
Work with commonly used BI tools, such as Tableau and Qlik.
Run in any distributed cluster environment, although it requires Apache’s ZooKeeper software to maintain information about clusters.
4. Druid: Big Data
Druid is a real-time analytics database that delivers low latency for queries, high concurrency, multi-tenant capabilities, and instant visibility into streaming data. Multiple end users can query the data stored in Druid at the same time with no impact on performance, according to its proponents.
Written in Java and created in 2011, Druid became an Apache technology in 2018. Many people generally view it as a high-performance alternative to traditional data warehouses tailored for event-driven data. Like a data warehouse, it uses column-oriented storage and can load files in batch mode. But it also incorporates features from search systems and time series databases, including the following:
Native inverted search indexes to speed up searches and data filtering.
Time-based data partitioning and querying.
Flexible schemas with native support for semistructured and nested data.
5. Flink: Big Data
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, designed to run in all common cluster environments, also offers the following features:
In-memory computations with the ability to access disk storage when needed.
Three layers of APIs for creating different types of applications.
A set of libraries for complex event processing, machine learning, and other common big data use cases.
6. Hadoop: Big Data
Hadoop developed a pioneering big data technology to help handle the growing volumes of structured, unstructured, and semi-structured data by providing a distributed framework for storing data and running applications on clusters of commodity hardware. First released in 2006, it was almost synonymous with big data early on; Other technologies have partially surpassed it, but people still widely use it.
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.
- Hadoop MapReduce is a built-in batch-processing engine that splits up large computations and runs them on different nodes for speed and load balancing.
- Hadoop Common contains a shared set of utilities and libraries.
Initially, Hadoop limited running MapReduce batch applications, but the addition of YARN in 2013 opened it up to other processing engines and use cases. Despite this expansion, MapReduce still closely associated with the framework. The broader Apache Hadoop ecosystem also includes various big data tools and additional frameworks for processing, managing, and analyzing big data.
7. Hive
Hive is a SQL-based data warehouse infrastructure software that enables users to read, write, and manage large data sets in distributed storage environments. Facebook created it but then open-sourced it to Apache, which continues to develop and maintain the technology.
Hadoop runs Hive on top of it to process structured data; more specifically, Hive processes data summarization and analysis, as well as queries large amounts of data. Although developers describe Hive as scalable, fast, and flexible, it cannot handle online transaction processing, real-time updates, and queries or jobs requiring low-latency data retrieval.
Other key features include the following:
- Standard SQL functionality for data querying and analytics.
- A built-in mechanism to help users impose structure on different data formats.
- Access to HDFS files and ones stored in other systems, such as the Apache HBase database.
8. HPCC Systems: Big Data
LexisNexis developed HPCC Systems as a big data processing platform before open-sourcing it in 2011. The technology, known by its full name High-Performance Computing Cluster Systems, consists of a cluster of computers built from commodity hardware to process, manage, and deliver big data actively.
A production-ready data lake platform that enables rapid development and data exploration, HPCC Systems includes three main components:
- Thor, a data refinery engine, cleanses, merges, and transforms data, profiles, analyzes, and readies it for use in queries.
- Roxie, a data delivery engine used to serve up prepared data from the refinery.
- Enterprise Control Language, or ECL, is a programming language for developing applications.
9. Hudi
Hudi (pronounced hoodie) is short for Hadoop Upserts Deletes and Incrementals. Apache NiFi, another open-source technology maintained by Apache, manages the ingestion and storage of large analytics data sets on Hadoop-compatible file systems, including HDFS and cloud object storage services.
Uber first developed Hudi to provide efficient and low-latency data ingestion and data preparation capabilities. Moreover, it includes a data management framework that organizations can use to do the following:
- Simplify incremental data processing and data pipeline development.
- Improve data quality in big data systems.
- Manage the lifecycle of data sets.
10. Iceberg
Iceberg utilizes an open table format to manage data in data lakes, tracking individual data files in tables rather than directories. Created by Netflix for use with the company’s petabyte-sized tables, Iceberg is now an Apache project. The project’s website states that Iceberg typically handles production environments where a single table can store tens of petabytes of data.
Designed to improve on the standard layouts that exist within tools such as 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. Other notable features include the following:
- Schema evolution for modifying tables without having to rewrite or migrate data.
- Hidden partitioning of data that avoids the need for users to maintain partitions.
- A time travel capability that supports reproducible queries using the same table snapshot.
11. Kafka
Kafka powers high-performance data pipelines, streaming analytics, data integration, and mission-critical applications for more than 80% of Fortune 100 companies and thousands of other organizations, according to Apache. 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. LinkedIn created Kafka before passing it on to Apache in 2011.
The following are some of the key components in Kafka:
- A set of five core APIs for Java and the Scala programming language.
- Fault tolerance for both servers and clients in Kafka clusters.
- Elastic scalability to up to 1,000 brokers, or storage servers, per cluster.
12. Kylin
Kylin is a distributed data warehouse and analytics platform for big data. It provides an online analytical processing (OLAP) engine designed to support extremely large data sets. Kylin’s backers claim that it easily scales to handle large data loads due to its foundation on other Apache technologies, such as Hadoop, Hive, Parquet, and Spark.
It’s also fast, delivering query responses measured in milliseconds. In addition, Kylin provides an ANSI SQL interface for multidimensional analysis of big data and integrates with Tableau, Microsoft Power BI, and other BI tools. eBay initially developed Kylin and contributed it as an open-source technology in 2014. it became a top-level project within Apache the following year. Other features it provides include the following:
- Precalculation of multidimensional OLAP cubes to accelerate analytics.
- Job management and monitoring functions.
- Support for building customized UIs on top of the Kylin core.
13. Pinot
Pinot is a real-time distributed OLAP data store built to support low-latency querying by analytics users. Its design enables horizontal scaling to deliver that low latency even with large data sets and high throughput. To provide the promised performance, Pinot stores data in a columnar format and uses various indexing techniques to filter, aggregate, and group data. In addition, configuration changes can be done dynamically without affecting query performance or data availability.
According to Apache, Pinot can handle trillions of records overall while ingesting millions of data events and processing thousands of queries per second. The system has a fault-tolerant architecture with no single point of failure and assumes all stored data is immutable, although it also works with mutable data. Started in 2013 as an internal project at LinkedIn, Pinot was open-sourced in 2015 and became an Apache top-level project in 2021.
The following features are also part of Pinot:
- Near-real-time data ingestion from streaming sources, plus batch ingestion from HDFS, Spark, and cloud storage services.
- A SQL interface for interactive querying and a REST API for programming queries.
- Support for running machine learning algorithms against stored data sets for anomaly detection.
14. Presto
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 it scales to support analytics applications across multiple petabytes of data in data warehouses and other repositories.
The 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.
Presto also includes the following features:
- Support for querying data in Hive, various databases, and proprietary data stores.
- The ability to combine data from multiple sources in a single query.
- Query response times typically range from less than a second to minutes.
15. Samza
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. Via a unified API, it can also use the same code written for data streaming jobs to run batch applications. Other features include the following:
- Built-in integration with Hadoop, Kafka, and several other data platforms.
- The ability to run as an embedded library in Java and Scala applications.
- Fault-tolerant features designed to enable rapid recovery from system failures.
16. Spark
Apache 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. That’s all supported by the following set of built-in modules and libraries:
- Spark SQL, for optimized processing of structured data via SQL queries.
- Spark Streaming and Structured Streaming, are two stream processing modules.
- MLlib is a machine learning library that includes algorithms and related tools.
- GraphX is an API that adds support for graph applications.
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 its 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.
17. Storm
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 extracting, transforming, and loading 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 Apache Storm site says it can be used with any programming language, message queueing system, and database. Storm also includes the following elements:
A Storm SQL feature that enables SQL queries to be run against streaming data sets.
Trident and Stream API, are two other higher-level interfaces for processing in Storm.
Use of the Apache ZooKeeper technology to coordinate clusters.
18. Trino
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, with support for natively running queries in Hadoop and other data repositories. Like Presto, Trino also is designed for the following:
- Both ad hoc interactive analytics and long-running batch queries.
- Combining data from multiple systems in queries.
- Working with Tableau, Power BI, programming language R, and other BI and analytics tools.
Also available to use in big data systems: NoSQL databases
NoSQL databases are another major type of big data technology. They break with conventional SQL-based relational database design by supporting flexible schemas, which makes them well suited for handling huge volumes of all types of data — particularly unstructured and semistructured data that isn’t a good fit for the strict schemas used in relational systems.
NoSQL software
NoSQL software emerged in the late 2000s to help address the increasing amounts of diverse data that organizations were generating, collecting, and looking to analyze as part of big data initiatives. Databases have been widely adopted since then and are now used in enterprises across industries.
In addition, NoSQL databases themselves come in various types that support different big data applications. These are the four major NoSQL categories, with examples of the available technologies in each one:
- Document databases. They store data elements in document-like structures, using formats such as JSON, BSON, and XML. Examples of document databases include Couchbase Server, CouchDB, and MongoDB.
- Graph databases. They connect data “nodes” in graph-like structures to emphasize the relationships between data elements. Examples of graph databases include AllegroGraph, Amazon Neptune, ArangoDB, Neo4j, and TigerGraph.
- Key-value stores. They pair unique keys and associated values in a relatively simple data model that can scale easily. Examples of key-value stores include Aerospike, Amazon DynamoDB, Redis, and Riak.
- Wide column stores. They store data across tables that can contain very large numbers of columns to handle lots of data elements. Examples of wide-column stores include Accumulo, Bigtable, Cassandra, HBase, and ScyllaDB.
Multimodel databases have also been created with support for different NoSQL approaches, as well as SQL in some cases; MarkLogic Server and Microsoft’s Azure Cosmos DB are examples. Many other NoSQL vendors have added multimodel support to their databases. For example, MongoDB now supports graph, geospatial, and time series data, and Redis offers document and time series modules. Those two technologies and many others also now include vector database capabilities to support vector search functions in generative AI applications.
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