18 top big data tools and technologies to know about in 2026

1. Airflow

Apache 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 can access the required system resources and is executed in the designated order. Airflow is most commonly used to orchestrate data integration and transformation processes, machine learning (ML) operations, business applications and IT infrastructure management tasks, but it also supports other types of workflows.

The platform has a modular architecture built around directed acyclic graphs that illustrate the dependencies between workflow tasks. Airflow pipelines are defined in Python and can be generated dynamically. Airbnb initially created Airflow for internal use, and the technology became a top-level project within the Apache Software Foundation in 2019.

Airflow also includes the following features:

• Time- and dependency-based scheduling of workflows, plus an event-driven scheduling option.
• 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

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. The Delta Lake framework creates a unified format for structured, semistructured and unstructured data, eliminating data silos that often stymie big data applications. It also provides common semantics for both batch and stream processing of table reads and writes.

To ensure data integrity, Delta Lake supports transactions that adhere to the four ACID properties: atomicity, consistency, isolation and durability. A liquid clustering capability optimizes how data is stored based on query patterns, offering an alternative to traditional data partitioning. Databricks, a software vendor founded by the creators of the Apache Spark processing engine, developed Delta Lake and made the Spark-compatible technology open source in 2019 through the Linux Foundation.

Delta Lake also includes the following features:

• Support for storing data in an open Apache Parquet format.
• Delta Universal Format, a function commonly known as UniForm that enables Delta Lake tables to be read in Iceberg and Hudi, two other Parquet-based table formats.
• A time-travel capability that provides access to earlier versions of data sets for audits and rollbacks.

3. Drill

Apache Drill is a low-latency distributed query engine best suited for workloads involving large, complex data sets with diverse types of records and fields. The Drill website claims it can scale across thousands of cluster nodes and query petabytes of data using SQL and standard connectivity APIs. It handles a combination of structured and semistructured data, including nested data types such as JSON and Parquet files.

Drill is built on a schema-free JSON document model and layers on top of multiple data sources, enabling users to query a wide range of data in different formats. It supports various file types and sources, including Hadoop SequenceFiles and event logs, NoSQL databases and cloud object storage. Drill users can store multiple files in a directory and query them as a single entity.

First released in 2015, the software can also do the following:

• Query data in most relational databases through a plugin.
• Work with commonly used BI tools, such as Tableau and Qlik Sense.
• Run in any distributed cluster environment, although Apache ZooKeeper must be installed along with it to maintain information about cluster configurations.

4. Druid

Apache Druid is a real-time analytics database with an interactive query engine that provides low query latency, high user concurrency, multi-tenant capabilities and instant visibility into streaming data. Hundreds or thousands of end users can simultaneously query data stored in Druid with no effect on performance, according to its developers.

Written in Java and created in 2011, Druid became an Apache technology in 2018. Best suited for storing event-driven data, it's considered a high-performance alternative to traditional data warehouses. Like a data warehouse, Druid uses column-oriented storage and can load files in batch mode. However, it also incorporates features from search systems and time series databases, including the following:

• Compressed bitmap indexes to speed up searches and data filtering.
• Time-based data partitioning and querying.
• Flexible schemas with native support for semistructured data and nested data structures.

5. Flink

Another Apache technology, Flink is a stream processing framework for high-performance distributed applications, including always-available ones. 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: The software processes millions of events in real time with low latency and high throughput.

Flink began as a university research initiative in Germany and became an Apache project in 2014. In addition to event-driven applications -- such as fraud or anomaly detection -- potential use cases include continuous data pipelines and both streaming and batch analytics. Flink runs in all common cluster environments and 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, ML and other common big data use cases.

6. Hadoop

Apache Hadoop is a distributed framework for storing data and running applications on commodity hardware clusters. First released in 2006 as a pioneering big data technology, it helps users handle large volumes of structured, unstructured and semistructured data. Hadoop is also at the center of a broader technology ecosystem that includes various related tools and frameworks for processing, managing and analyzing big data. While Hadoop has been partially eclipsed by Spark and other technologies, it's still used by many organizations.

Hadoop includes these primary components:

• The Hadoop Distributed File System (HDFS) splits data into blocks for storage on cluster nodes, uses replication methods to prevent data loss and manages access to the data.
• Hadoop YARN schedules data processing jobs to run on cluster nodes and allocates system resources to them.
• Hadoop MapReduce, a built-in batch processing engine, splits up large computations and runs them on different nodes for speed and load balancing.
• Hadoop Common is 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 most commonly used with MapReduce.

7. Hive

Also an Apache technology, Hive is SQL-based data warehouse infrastructure software for reading, writing and managing large data sets in distributed Hadoop storage environments. It runs on top of Hadoop and processes structured data for summarization, querying and analysis. Hive supports ACID transactions, low-latency analytical processing and cost-based query optimization, the latter through integration with the Apache Calcite tool.

In addition to HDFS files, Hive can access ones stored in the Apache HBase database and other systems. It also enables users to create and read Iceberg tables. Hive Metastore Server, its central metadata repository, provides data abstraction and data discovery features similar to those in traditional data warehouses. Facebook created Hive for internal use, and it became an Apache top-level project in 2010.

Other key features include the following:

• HiveQL, a language with standard SQL functionality for data querying and analytics.
• Native support for cloud object storage services.
• MapReduce, Spark and Apache Tez as execution back-end options.

8. HPCC Systems

HPCC Systems is a big data processing platform that LexisNexis Risk Solutions developed as an alternative to Hadoop and Spark. Befitting its full name -- High-Performance Computing Cluster Systems -- the technology supports data-intensive applications requiring speed and scalability on clusters built from commodity hardware. Its primary use case is enabling rapid data engineering for analytics applications in data lake environments.

The platform includes these main components:

• Thor, a data refinery engine used to cleanse, merge and transform data for use in queries.
• Roxie, a data delivery engine that serves prepared data from the refinery to end users for querying.
• Enterprise Control Language, a programming language commonly known as ECL that's used for data management and query processing.

HPCC Systems also includes a library of ML algorithms, plus tools for monitoring clusters and profiling, curating and governing data. While still primarily overseen by LexisNexis, it became open source in 2011 and is freely available to download under the Apache 2.0 license. The current release is a cloud-native platform that runs in Docker containers on Kubernetes in both the AWS and Microsoft Azure clouds. Deployments of the original bare-metal platform are also still supported.

9. Hudi

Apache Hudi -- pronounced hoodie -- is a platform for managing large analytics data sets stored in HDFS and other Hadoop-compatible file systems deployed in cloud object storage services. Short for "Hadoop upserts, deletes and incrementals," Hudi provides database-like functionality for ingesting and updating data to support real-time analytics in data lakes and lakehouses.

First developed by Uber and an Apache top-level project since 2020, Hudi is built on an open table format that supports both Parquet and Apache ORC as the base file format. The platform integrates with Spark, Flink and other data processing and query engines. It supports ACID transactions, multimodal indexing to boost query performance and historical data analysis through a time-travel feature.

Hudi also 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 data set lifecycles.

10. Iceberg

Another Apache technology, Iceberg is an open table format for managing large analytics data sets stored in data lakes and lakehouses. According to the project's website, Iceberg is typically used in applications where individual tables contain tens of petabytes of data. The tables can be read from a single cluster node, without requiring a distributed SQL engine to sort through metadata and find the files needed for queries.

To boost query performance, Iceberg tracks individual data files in tables rather than directories, using metadata files to maintain a snapshot log of changes to a table. It supports SQL commands to update, merge or delete data and enables multiple query engines to simultaneously read and write data in a single table. Created by Netflix for internal use, Iceberg became an Apache top-level project in 2020.

Other notable features include the following:

• Schema evolution for modifying tables without rewriting or migrating data.
• Hidden partitioning that frees users from maintaining partitions and automatically updates table layouts as data or queries change.
• A time-travel capability, plus version rollback for resetting tables to a known good state.

11. Kafka

Apache Kafka is a distributed event streaming platform that supports data pipelines, data integration, streaming analytics and critical business applications. Created by LinkedIn and handed over to Apache in 2011, Kafka handles petabytes of data and trillions of event messages per day. It uses a publish-subscribe model to transmit messages and enables users to store event streams in distributed, fault-tolerant clusters for long-term use. Data streams can similarly be processed on the fly or later.

To boost scalability, Kafka decouples applications that produce and consume event data and partitions the data across multiple storage servers, which are called brokers. It can be deployed on bare-metal hardware or in VMs and containers, both on-premises and in the cloud.

The following are some of Kafka's other key components:

• A set of six core APIs for Java and the Scala programming language.
• Built-in stream processing capabilities for joining, aggregating, filtering and transforming data.
• Elastic scalability to up to 1,000 brokers per cluster.

12. Kylin

Apache Kylin is a distributed data warehouse and online analytical processing (OLAP) platform designed to support large data sets and queries involving trillions of records. Kylin's storage layer is built on top of Delta Lake and Parquet. The platform includes a native compute engine added in 2024 that's based on Spark and Apache Gluten, a performance accelerator plugin for Spark.

Internal data tables that Kylin manages directly were added along with the native engine. Kylin also still supports tables imported from data sources such as Hive, Kafka and Iceberg, but the internal tables offer greater flexibility for querying data. It provides a SQL interface for querying data and connects to Excel and BI tools such as Tableau and Microsoft Power BI. Initially developed by eBay, Kylin became an Apache top-level project in 2015.

Kylin also offers the following features:

• Precalculation of multidimensional OLAP cubes to improve query performance.
• A data modeling and indexing recommendation engine.
• Combined analysis of streaming and batch data.

13. Pinot

Also an Apache project, Pinot is a real-time distributed OLAP data store that supports low-latency querying in analytics applications. According to its developers, Pinot handles petabytes of data containing trillions of records and concurrently processes hundreds of thousands of queries per second. To deliver the promised performance, Pinot has a fault-tolerant architecture with no single point of failure and supports horizontal scaling of clusters. Other configuration changes can also be done dynamically without affecting data availability or query performance.

Pinot uses a columnar storage format and offers various indexing techniques to filter, aggregate and group data. To simplify data storage and replication, the system assumes all stored data is immutable. However, it supports upserts to keep streaming data sets up to date, as well as background purges of sensitive data to comply with privacy laws. Created by LinkedIn for internal use, Pinot became an Apache top-level project in 2021.

The following features are also included:

• 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.
• Integration with ZooKeeper for distributed metadata storage and Apache Helix for cluster management.

14. Presto

Presto is a SQL query engine optimized for low-latency querying of large data sets. It supports analytics applications across multiple petabytes of data in data lakes, data lakehouses and other repositories. To further boost performance and reliability, Presto's developers are converting its core execution engine from Java to a C++ version based on Velox, an open source acceleration library. An early version of Presto C++ is available, but it has a limited set of connectors and doesn't support some of Presto's built-in query functions.

Presto's development began at Facebook. When its creators left the company in 2018, the technology split into two branches: PrestoDB, which Facebook still led, and PrestoSQL, led by the original developers. In 2020, PrestoDB reverted to Presto, and PrestoSQL was renamed Trino. The Presto open source project is now overseen by the Presto Foundation, which is part of the Linux Foundation.

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

Apache Samza is a distributed stream processing system that enables users to build stateful applications for real-time processing of data from Kafka, HDFS and several other sources. It then writes the processed data back to some of the same systems. Use cases for Samza include event-based applications, real-time analytics and extract, transform and load (ETL) processes on streaming data.

The Samza website says it can handle "several terabytes" of state data, with low latency and high throughput for data analysis. The system also supports stateless stream processing. It runs on top of Hadoop YARN or in a standalone deployment mode; the latter option enables Samza to be a component of larger applications and lets users implement Kubernetes or another cluster manager instead of YARN. Originally developed by LinkedIn, Samza has been an Apache top-level project since 2015.

Other features include the following:

• A pair of high- and low-level APIs for different use cases, plus a declarative SQL interface.
• The ability to run as a lightweight embedded library in Java and Scala applications.
• Fault-tolerant features for migrating tasks in the event of system failures and rapidly recovering from them.

16. Spark

Apache Spark is a unified data processing and analytics engine used for data engineering in both batch and streaming applications, as well as for interactive querying, ML and exploratory data analysis. Spark often outperforms MapReduce on batch processing, making it the top choice for such tasks in many big data environments. It's also widely used as a large-scale analytics platform.

Spark includes the following core modules and libraries to support its various use cases:

• Spark SQL, for processing structured and unstructured data via SQL queries.
• Spark Structured Streaming, a module for building streaming applications and data pipelines.
• MLlib, a machine learning library that includes various algorithms and related utilities.
• Dataset and DataFrame APIs, which are used to organize distributed data sets for processing.

Spark runs on clusters managed by Hadoop YARN, Kubernetes or a standalone clustering tool built into the platform. It handles data from various sources, including HDFS, flat files and both relational and NoSQL databases. In addition to SQL, Spark supports Python, Scala, Java and R for programming. A Spark Connect feature enables client applications to connect to remote servers, simplifying development and deployment. Spark was created at the University of California, Berkeley, in 2009 and became an Apache top-level project in 2014.

17. Storm

Storm, another Apache technology, is a distributed real-time computation system for processing unbounded data streams. Its use cases include real-time analytics, ML, continuous computation and ETL procedures on streaming data. The fault-tolerant system guarantees data processing, with multiple guarantee levels available to meet different application needs.

The Apache Storm website says it can integrate with any message queueing system or database to access streaming data. Storm also supports any programming language for application development, and the system's out-of-the-box cluster configurations are suitable for production use. ZooKeeper is integrated to coordinate Storm clusters.

Storm became an Apache top-level project in 2014 and also includes the following elements:

• A basic API and Trident, a higher-level interface for processing data in Storm
• Inherent parallelism that supports high data throughput with low latency.
• An experimental Storm SQL feature that enables SQL queries to run against streaming data sets.

18. Trino

As mentioned above, Trino branched off from the Presto query engine and was originally named PrestoSQL. Like Presto, it's a distributed SQL engine for use in big data analytics applications. According to the Trino website, it supports low-latency analytics in exabyte-scale data lakes and lakehouses, as well as large data warehouses.

Trino includes built-in connectors to 25 data sources, and seven external connectors are also available. It provides an interactive CLI for querying data, plus 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 and other BI and analytics tools, as well as Apache Superset and R.

Trino is overseen by the Trino Software Foundation and also supports the following capabilities:

• Both ad hoc interactive analytics and long-running batch queries.
• Queries that combine data from multiple sources through a federation feature.
• Deployment in Kubernetes clusters and Docker containers.

Editor's note: TechTarget editors updated this article in February 2026 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.