Apache Spark
About Apache Spark
Apache Spark is a multi-language engine for data engineering, data science and machine learning on single machines or clusters. It processes batch and streaming data in Python, SQL, Scala, Java or R.
Used for
- Distributed SQL queries for dashboards and ad-hoc reporting
- Exploratory data analysis on petabyte-scale data
- Training machine learning models on a laptop and running the same code on clusters
From Apache Spark's own website, read 2026-10-23. How we build listings.
How to evaluate Streaming & Real-Time Analytics
This is how the CIOPages Research Team evaluates this category. It is not an assessment of Apache Spark. It comes from our Streaming Data & Event Processing buyer guide.
25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow
Apache Spark alternatives
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Quick Facts
spark.apache.orgCategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
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