A data engineer’s guide to the 10 leading streaming ETL and real-time processing frameworks of 2025. Learn how Flink, Materialize, and Dataflow stack up on latency, scalability, cost, and ecosystem so you can pick the right engine for mission-critical pipelines.
The best streaming ETL frameworks in 2025 are Apache Flink, Materialize, and Google Cloud Dataflow. Apache Flink excels at ultra-low-latency stateful processing; Materialize offers instant SQL views that stay fresh; Google Cloud Dataflow is ideal for autoscaling pipelines without ops overhead.
Instant personalization, IoT telemetry, and AI-driven decisioning require data that arrives in milliseconds, not hours. Batch ETL can no longer satisfy fraud detection, dynamic pricing, or in-product analytics. Streaming ETL frameworks solve this gap by ingesting, transforming, and serving data continuously, letting downstream systems act on fresh insights.
The 2025 market offers dozens of options.
To rank the top 10, we scored each engine on:
Apache Flink
Apache Flink tops the 2025 list thanks to sub-100-ms latencies, mature stateful operators, and a vibrant community. The Table API and SQL Gateway let analysts build pipelines without Java, while the unified batch-stream runtime cuts duplication. Version 1.20 introduced the Kubernetes Operator for blue-green upgrades and reactive scaling.
Mission-critical fraud detection, clickstream analytics, and real-time ML feature pipelines.
Materialize brings database ergonomics to streaming: users create SQL views that stay perpetually fresh.
The 2025 release adds native support for Apache Iceberg sinks and multi-cluster replication, making it attractive for enterprise analytics.
Customer-facing dashboards, operational analytics, and simplifying CDC pipelines.
Based on Apache Beam, Dataflow offers serverless streaming with autoscaling and integrated Dataform lineage. 2025’s FlexRS pricing tier cuts costs up to 35 percent for fault-tolerant workloads, and GPU-based streaming transforms accelerate inference pipelines.
Cloud-native teams that prefer managed infrastructure and seamless BigQuery integration.
Apache Spark Structured Streaming
The Spark 4.0 preview delivers asynchronous checkpointing and vectorized joins, narrowing the latency gap with Flink while preserving the familiar Spark API. Delta Live Tables now supports continuous pipelines, bringing transactional semantics to streaming ETL.
RisingWave is an open-source cloud streaming database compatible with PostgreSQL wire protocol. Materialized views refresh in seconds, and Rust-based execution lowers memory footprints. The 2025 Enterprise edition introduces tiered storage and role-based access control.
Confluent Cloud
Confluent Cloud extends Apache Kafka with fully managed clusters, Stream Governance, and ksqlDB for streaming SQL. The 2025 Stream Pooling feature lets customers share capacity across regions, improving cost efficiency for bursty traffic.
Kinesis Data Analytics provides Flink or SQL applications that scale elastically within AWS. The 2025 addition of Graviton3 compute options delivers 20 percent lower costs, while Glue Data Catalog integration simplifies schema discovery.
Apache Kafka Streams
Kafka Streams remains popular for lightweight Java microservices that embed stream processing directly in application code. The 3.7 release adds Exactly-Once v2 and worth-mentioning GraphQL support via open source libraries.
Pulsar Functions offer a serverless compute layer inside Pulsar clusters. 2025’s WASM runtime lets developers deploy multi-language functions without Docker, reducing cold starts.
Quix Streams is a Python-first open source library that wraps Kafka for ML feature engineering.
The 2025 2.0 release introduces Pandas-like window aggregations and GPU acceleration.
Start with latency and state requirements. Flink and Materialize shine below 500 ms. If your team wants serverless convenience, Dataflow or Kinesis Data Analytics minimize ops toil. For SQL-centric analytics, Materialize, RisingWave, or Confluent’s ksqlDB speed onboarding.
Evaluate total cost: open source licensing might look free but cluster management and observability tooling add overhead.
Use Kubernetes Operators or Terraform modules to version pipeline topology. This improves disaster recovery and auditability.
Metadata lineage is vital for compliance. Tools like OpenLineage or Galaxy’s forthcoming catalog simplify end-to-end traceability.
Adopt engines that support both modes so feature logic lives in one codebase.
Flink, Spark, and Beam achieve this with unified APIs.
Capture upstream database changes with Debezium or AWS DMS, then feed them into the stream processor to avoid dual-write problems.
Every streaming platform still ends with SQL. Galaxy’s lightning-fast editor and AI copilot let engineers explore streaming tables, debug windowed aggregations, and document production queries in one collaborative workspace.
As Galaxy evolves into a unified data platform, teams will version their Flink SQL, ksqlDB statements, or Materialize views alongside batch analytics, ensuring a single source of truth across streaming and historical data.
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Streaming ETL ingests and transforms data continuously, letting applications react within seconds. Batch ETL groups data into large files processed on a schedule, which can introduce hours of latency. Streaming is essential for fraud detection, real-time personalization, and IoT analytics.
In 2025 Apache Flink leads with p95 latencies under 100 milliseconds thanks to efficient checkpointing and network stack optimizations. Materialize follows closely for SQL workloads.
Galaxy provides a developer-first SQL workspace where teams write, version, and share streaming queries from Flink SQL, ksqlDB, or Materialize. Its AI copilot accelerates debugging and optimization, while Collections keep endorsed real-time queries discoverable and governed.
Consider compute usage, storage for state checkpoints, data transfer, and engineering hours spent on cluster operations. Managed services like Dataflow or Confluent Cloud shift costs from people to consumption fees, while self-hosting may save money but increases operational overhead.
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