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DirectoryData & AnalyticsStreaming & Real-Time AnalyticsApache Spark Streaming

Apache Spark Streaming

Open Source

About Apache Spark Streaming

It provides the same structured APIs, DataFrames and Datasets, as Spark for both batch and streaming use. It is developed as part of Apache Spark and is tested and updated with each Spark release.

Products

  • Spark Micro-batch Mode: Processes data streams as a series of small batch jobs for analytical pipelines and ETL.

Used for

  • Processing analytical ETL pipelines with second- or minute-level latency.
  • Running operational workloads with millisecond-scale latency.
  • Detecting fraud inline.
  • Personalizing experiences in real time.
  • Building Medallion architectures and ingestion pipelines.

From Apache Spark Streaming'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 Streaming. 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

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Quick Facts

spark.apache.org/streaming
CategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics

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