18 top big data tools and technologies to know about in 2025

1. Airflow

Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems. It enables data engineers and other users to ensure each task in a workflow is executed in the designated order and has access to the required system resources. Workflows are created in the Python programming language, and Airflow can be used for building machine learning models, transferring data and various other purposes.

The platform originated at Airbnb in late 2014 and was officially announced as an open source technology in mid-2015; 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, 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

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. Delta Lake is a table storage layer that can be used to build a data lakehouse architecture combining elements of data lakes and data warehouses for both streaming and batch processing applications.

It's designed to sit on top of a data lake and create a single home for structured, semistructured and unstructured data, eliminating data silos that can stymie big data applications. Delta Lake supports ACID transactions that adhere to the principles of atomicity, consistency, isolation and durability. It also includes a liquid clustering capability to optimize how data is stored based on query patterns, as well as the following features:

• The ability to store data in an open Apache Parquet format.
• Uniform Format, or UniForm for short, a function that enables Delta Lake tables to be read in Iceberg and Hudi, two other Parquet-based table formats.
• Compatibility with Spark APIs.

3. Drill

The Apache Drill website describes it as a low-latency distributed query engine best suited for workloads that involve large sets of complex data with different types of records and fields. Drill can scale across thousands of cluster nodes and query petabytes of data through the use of SQL and standard connectivity APIs. It can handle a combination of structured, semistructured and nested data, the latter including things such as JSON and Parquet files.

Drill layers on top of multiple data sources, enabling users to query a wide range of data in different formats. That includes Hadoop sequence files and event logs, NoSQL databases, cloud object storage and various file types. Multiple files can be stored in a directory and queried as if they were a single entity.

The software 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

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. It's generally considered a high-performance alternative to traditional data warehouses that's best suited to 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

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. Other potential use cases include data pipelines and both streaming and batch analytics. Flink, which is designed to run in all common cluster environments, also includes 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

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 used by many organizations.

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, a built-in batch processing engine that splits up large computations and runs them on different nodes for speed and load balancing.
• 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.

7. Hive

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 or real-time updates, Hive is described by its developers as scalable, fast and flexible. A central metadata repository can be used as a building block for data lakes, and Hive supports ACID, low-latency analytical processing and the ability to read and write Iceberg tables.

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

HPCC Systems is a big data processing platform developed by LexisNexis Risk Solutions and then open sourced in 2011. While still primarily overseen by the company, it's freely available to download under the Apache 2.0 license. True to its full name -- High-Performance Computing Cluster Systems -- the technology is, at its core, a cluster of computers built from commodity hardware.

A production-ready data lake environment that's designed to enable fast data engineering for analytics applications, HPCC Systems includes three main components:

• Thor, a data refinery engine used to cleanse, merge and transform data for use in queries.
• Roxie, a data delivery engine used to serve up prepared data from the refinery.
• Enterprise Control Language, or ECL, a programming language for developing applications.

In its latest releases, HPCC Systems is a cloud-native platform that can be run in Docker containers on Kubernetes in both the AWS and Microsoft Azure clouds. Deployments of the original bare-metal platform are also still supported, though.

9. Hudi

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. Hudi combines an open table format with a software stack built to underpin data lakes and data lakehouses.

First developed by Uber, Hudi is designed to provide efficient and low-latency data ingestion and data preparation capabilities. The technology also supports ACID transactions, multimodal indexing to boost query performance and a time travel feature for analyzing historical data. 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 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 such as Hive, Presto, Spark and Trino, the Iceberg table format has functions similar to SQL tables in relational databases. It also accommodates multiple query engines operating on the same data set, including the ones listed above. 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 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, which was created by LinkedIn before being passed on to Apache in 2011, is designed to handle petabytes of data and trillions of messages per day.

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. Because Kylin is built on top of other Apache technologies -- including Delta Lake, Parquet and the Gluten compute engine -- it can easily scale to handle those large data loads, according to its backers. The platform supports Hive, Kafka, Iceberg and other external data sources as well as an internal table added in December 2024.

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. Kylin was initially developed by eBay, which contributed it as an open source technology in 2014; it became a top-level project within Apache the following year.

Other features it offers 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 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, data lakes and other repositories. To further boost performance and reliability, Presto's developers are converting the core execution engine from Java to a C++ version based on Velox, an open source acceleration library. Presto C++ is available to use but still has limitations that need to be addressed.

Presto's development 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:

• Connectors to 36 data sources, including Delta Lake, Druid, Hive, Hudi, Iceberg, Pinot and various databases.
• The ability to combine data from multiple sources in a single query.
• A web-based UI and a CLI for querying, plus support for the Apache Superset data exploration tool.

15. Samza

Samza is a distributed stream processing system that was built by LinkedIn and has been an open source project managed by Apache since 2013. According to the project website, Samza enables users to build stateful applications for real-time processing of data from Kafka, HDFS and several other sources, with built-in integration to them.

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. Through a unified API, it can also use the same code written for data streaming jobs to run batch applications. Other features include the following:

• Both high- and low-level APIs for different use cases, plus a declarative SQL interface.
• 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 engineering and analytics 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 Structured Streaming, a stream processing module.
• MLlib, a machine learning library that includes algorithms and related tools.
• GraphX, an API that adds support for graph applications.

Data can be accessed from various sources, including HDFS, flat files and both relational and NoSQL databases. Spark also supports various file formats and offers developers a diverse set of APIs. Spark's performance edge over traditional counterpart MapReduce made it the top choice for batch applications in many big data environments, but it also functions as a general-purpose analytics 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, continuous computation and machine learning on streaming data, as well as extract, transform and load 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, 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. Like the current Presto, it's a distributed SQL engine for use in big data analytics applications. Trino supports low-latency analytics in exabyte-scale data lakes and large data warehouses, according to 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 2020 rebranding of PrestoSQL.

Trino enables users to query data regardless of where it's stored, with support for natively running queries in Hadoop and other data repositories. It includes a CLI and a plugin that lets users run queries in Grafana, an open source data visualization and dashboard design tool. In addition, Trino works with Tableau, Power BI, Apache Superset, the R programming language, and various other BI and analytics tools.

As with Presto, Trino also is designed for the following:

• Both ad hoc interactive analytics and long-running batch queries.
• Queries that combine data from multiple systems through a federation feature.
• Built-in links to data sources through a set of 38 connectors.

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 large 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 emerged in the late 2000s to help address the increasing amounts of diverse data that organizations were generating and collecting as part of big data initiatives. Since then, NoSQL databases have been widely adopted and are now used in enterprises across industries. Many are open source or source available technologies that are also offered in commercial versions by vendors, while some are proprietary products controlled by a single vendor. Despite the name, many NoSQL technologies do support some SQL capabilities. As a result, NoSQL more commonly means "not only SQL" now.

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 Amazon DocumentDB, 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; MarkLogic Server and Microsoft's Azure Cosmos DB are examples. Many other NoSQL vendors have added multimodel support to their databases. For example, MongoDB supports graph, geospatial and time series data, and Redis offers document and time series modules. Those technologies and numerous others now also include vector database capabilities to support vector search functions in generative AI applications.

Editor's note: Informa TechTarget editors updated this article in January 2025 for timeliness and to add new information.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.