What Apache Spark does

Apache Spark is an open-source unified analytics engine for large-scale data processing, originally developed at UC Berkeley's AMPLab beginning in 2009 and stewarded by the Apache Software Foundation since graduating from incubation in February 2014. The engine is built on an advanced distributed SQL engine and provides batch processing, real-time streaming (including millisecond-latency Real-Time Mode introduced in Spark 4.1), distributed ANSI SQL analytics, and the MLlib machine learning library. It supports five programming languages (Python, SQL, Scala, Java, R) and scales from single-node machines to fault-tolerant clusters of thousands of nodes, with native integrations into table formats (Delta Lake, Apache Iceberg, Apache Hudi), streaming systems (Apache Kafka), ML frameworks (TensorFlow, PyTorch, MLflow), BI tools (Tableau, Power BI, Apache Superset), and Kubernetes-based deployment.

The product surface comprises core modules including Spark SQL and DataFrames, Structured Streaming, MLlib, pandas on Spark, and Spark Connect (a client-server protocol with Go, Rust, and Swift bindings). Spark is cited as the most widely used engine for scalable computing, adopted by thousands of organizations including 80% of the Fortune 500 and contributed to by over 2,000 developers from industry and academia. Adaptive Query Execution accelerates TPC-DS queries by up to 8x.

The business model is purely open source: Spark is distributed free of charge under the Apache License 2.0 with no commercial license tiers, paid support products, or proprietary extensions sold by the project itself. The trademark is held by the Apache Software Foundation, a 501(c)(3) nonprofit, and there is no parent company, subsidiaries, or funding rounds. Commercial value capture around Spark occurs through adjacent ecosystems — most notably Databricks, co-founded by Spark creator Matei Zaharia — rather than the project itself. Go-to-market is community-led and API-first: awareness and adoption are driven by documentation, GitHub, mailing lists, the Spark Summit conference, and ecosystem integrations rather than a traditional sales motion.

Name

Apache Spark

Legal name

Apache Spark (trademark of Apache Software Foundation)

Website

https://spark.apache.org

Company type

Private

Founded year

2010

Operating status

Operating

Headcount range

251–500 employees

Short description

Apache Spark is an open-source unified analytics engine under the Apache Software Foundation that provides distributed batch processing, streaming, SQL, and machine learning at petabyte scale, used by thousands of organizations including 80% of the Fortune 500.

Ownership category

akta.pro rank

Product category

Distributed Data Processing Engine

NAICS

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518), Custom Computer Programming Services (541511)

SIC

Services-Prepackaged Software (7372), Services-Computer Processing & Data Preparation (7374), Services-Computer Programming, Data Processing, Etc. (7370)

akta.pro primary industry

Query Engines & SQL Analytics Layers for Warehouses/Lakes (HDAEABAI)

akta.pro secondary industry

Developer Tools & DevOps Platform Services (CI/CD, Artifacts, IaC) (HDABAAAI)

Keywords

• Distributed data processing
• Big data analytics
• Stream processing engine
• Machine learning platform
• Data engineering framework

Headquarters

HQ city

Berkeley

HQ country

United States

HQ region

North America

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Infrastructure, Marketing or Sales, Operations

Revenue model

1. Open Source Distribution: Apache Spark is freely distributed under the Apache License 2.0. There is no commercial revenue model; the project is maintained by volunteers and contributors from companies that use Spark.

Pricing tiers

Go-to-market motion2 records

Distribution channels5 records

Marketing channels6 records

Core offering

Apache Spark is a multi-language unified engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It provides batch processing, real-time streaming, distributed ANSI SQL analytics, and MLlib machine learning at petabyte scale, distributed freely under the Apache License 2.0.

Product overview

Apache Spark is a unified, multi-language open-source engine for large-scale data analytics, consisting of a core distributed computing engine with built-in libraries for SQL/DataFrames, streaming (Structured Streaming and legacy DStreams), and machine learning (MLlib). The product supports five programming languages (Python, SQL, Scala, Java, R) and scales from single machines to fault-tolerant clusters of thousands of nodes. Key modules include Spark SQL for ANSI-compliant distributed SQL queries, Structured Streaming for real-time data processing with micro-batch and continuous modes, MLlib for scalable machine learning, and pandas on Spark for Python compatibility. Spark Connect provides a client-server architecture for remote connectivity. The ecosystem integrates with major ML frameworks (TensorFlow, PyTorch, scikit-learn), BI tools (Tableau, PowerBI, Apache Superset), and storage formats (Delta Lake, Apache Iceberg, Parquet, ORC).

Differentiator

Problem solved

Functional benefit

Brands

• Spark SQL: Built-in SQL module for Spark providing ANSI SQL support and distributed SQL engine capabilities.
• Spark Streaming
• MLlib
• GraphX
• pandas on Spark
• Spark Connect

Products and services

• Apache Spark A multi-language unified engine for executing data engineering, data science, and machine learning on single-node machines or clusters. Supports batch processing, streaming, SQL analytics, and ML at petabyte scale. Distributed under the Apache License 2.0.
• Spark SQL and DataFrames Built-in library for distributed ANSI SQL queries and tabular data processing using the DataFrame API, supporting multiple data sources including JSON, Parquet, ORC, CSV, Avro, and JDBC, and featuring Adaptive Query Execution that accelerates TPC-DS queries up to 8x.
• Structured Streaming Stream processing API built on the Spark SQL engine providing both micro-batch and continuous processing modes for low-latency streaming applications, including the new Real-Time Mode in Spark 4.1 enabling millisecond-level latency.
• Spark Connect Client-server architecture enabling remote connectivity to Spark clusters via a defined protocol, with official clients in Python (PySpark) and Scala and community-contributed connectors in Go, Rust, and Swift.
• pandas on Spark Pandas API implementation on Spark enabling pandas users to leverage Spark's distributed processing capabilities without changing their existing pandas code.
• MLlib Scalable machine learning library providing distributed implementations of common ML algorithms including classification, regression, clustering, collaborative filtering, and dimensionality reduction.

Quantifiable outcome

• Accelerates TPC-DS queries up to 8x with Adaptive Query Execution
• +3 more outcomes

Named customers7 records

Segments1 record

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

Integration17 records

AI capability4 records

Feature7 records

Partnerships

Twelve partnerships are on record, tiered core and adjacent.

• Delta Lake (Databricks)coreTechnology or IntegrationDelta Lake, co-created by Databricks, is a storage layer that brings ACID transactions to Apache Spark. It is fully integrated into Spark's ecosystem and supports Unity Catalog interoperability. Spark 4.1+ supports Delta Lake tables with first-class interoperability features.
• Apache IcebergcoreTechnology or IntegrationApache Iceberg, originally developed at Netflix, is an open table format supported by Spark. Spark can read and write Iceberg tables, and Iceberg REST catalogs integration enables lightweight analytics on cloud-stored tables. Major cloud providers including Snowflake, Google BigQuery, Databricks, Trino, and Apache Flink have all adopted Iceberg support.
• Apache HudicoreTechnology or IntegrationApache Hudi (originally at Uber) is another major open-source table format supported by Spark for incremental data pipelines and merge-on-read capabilities. Used at scale by Uber, Amazon, ByteDance, and Walmart.
• Apache KafkacoreTechnology or IntegrationApache Kafka is integrated as a data source and sink for Spark Streaming and Structured Streaming, enabling real-time data pipeline processing.
• MLflowcoreTechnology or IntegrationMLflow, co-created by Databricks, is integrated with Spark for machine learning lifecycle management including experiment tracking, model registry, and deployment. Both were created by Matei Zaharia and are part of the unified data and ML ecosystem.
• TensorFlowadjacentTechnology or IntegrationTensorFlow integrates with Spark through spark-tensorflow-connector and can be used alongside Spark's MLlib for distributed ML workflows.
• PyTorchadjacentTechnology or IntegrationPyTorch integrates with Spark for distributed deep learning, often used with Spark for data preprocessing and model training at scale.
• KubernetescoreTechnology or IntegrationSpark supports native Kubernetes deployment allowing Spark applications to run on Kubernetes clusters. Also includes spark-kubernetes-operator for managing Spark applications on K8s.
• Apache BeamadjacentTechnology or IntegrationApache Beam provides a unified programming model that can execute on multiple backends including Apache Spark. Beam programs can run on Spark execution backends.
• scikit-learn, pandas, NumPy, RadjacentTechnology or IntegrationPython data science ecosystem tools that integrate with Spark via PySpark. pandas API on Spark provides pandas-like functionality on Spark's distributed engine.
• Apache Superset, Tableau, Power BI, Looker, dbtadjacentTechnology or IntegrationBusiness intelligence and SQL analytics tools that connect to Spark as a SQL engine for visualization and dashboarding.
• DatabrickscoreTechnology or IntegrationDatabricks, co-founded by Spark's creators including Matei Zaharia, is the primary commercial provider of managed Spark services. Databricks drives much of Spark's development and integrates Spark with Unity Catalog for open lakehouse interoperability.

Scale indicators6 records

Recent moves7 records

Expansion highlights5 records

Direct peers

• Apache Beam: Apache Beam provides a unified batch/streaming programming model that can execute on Spark as a backend. Both target portable, large-scale data processing with multi-language SDKs and overlapping use cases.
• Apache Flink: Apache Flink is the primary open-source competitor to Spark Structured Streaming, specializing in low-latency stream processing. Both target large-scale distributed data processing and increasingly compete for the same real-time workloads.
• Trino (formerly Presto): Trino is a distributed SQL query engine that competes directly with Spark SQL for interactive analytics on data lakes. Both support ANSI SQL, federation across data sources, and integration with Iceberg/Hive tables.
• Apache Hudi: Apache Hudi is an open table format used at scale by Uber, Amazon, ByteDance, and Walmart for incremental data pipelines. Spark is Hudi's primary compute engine, making it a tightly coupled peer in the lakehouse stack.
• Apache Iceberg: Apache Iceberg is an open table format that Spark reads/writes natively, but it is also positioned by some as an alternative to Spark-managed Delta pipelines. Both are core layers in the modern lakehouse architecture.

Broad incumbents

• Databricks: Databricks is the primary commercial steward and dominant managed-Spark provider, founded by Spark's creators. While Spark itself is open source, Databricks packages it into a commercial lakehouse platform with proprietary optimization, governance, and AI features.
• Snowflake: Snowflake is a cloud-native data warehouse that competes with Spark on SQL analytics workloads and now supports Iceberg tables, intersecting with Spark's open lakehouse play. Customers often choose between running Spark SQL or Snowflake for large-scale analytics.
• Google Cloud Dataflow: Google Cloud Dataflow is a managed stream and batch processing service based on Apache Beam that competes with Databricks/managed Spark for large-scale data pipelines on Google Cloud.

Emerging players

• DuckDB: DuckDB is an embedded analytical database that outperforms Spark on single-node analytics workloads. It represents the emerging trend of lightweight, local-first analytics engines challenging Spark's position for ad-hoc and exploratory workloads.
• Dremio: Dremio is a lakehouse query engine built on Apache Arrow that competes with Spark SQL for interactive analytics on Iceberg and other open table formats. It targets SQL-first use cases that often sit alongside Spark pipelines.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat6 records

Key risks5 records

Key highlights7 records

Customer concentration

• GitHubgithub.com/apache/spark
• Xx.com/apachespark
• Facebookfacebook.com/ApacheSparkSummit
• LinkedInlinkedin.com/company/groups

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Apache Spark