Open source drives almost every modern data platform. Community innovation, transparent roadmaps and flexible licensing let teams evolve stacks without vendor lock-in. In 2025, the leading projects focus on three themes: cloud-native scalability, declarative governance and AI-friendly extensibility. Below we rank the ten projects every data engineer should evaluate first.
Apache Airflow remains the de-facto standard for scheduling data pipelines in 2025.
Version 3.0 introduced the TaskFlow API, enabling Pythonic DAGs that compile down to efficient DAG files. With provider packages for Snowflake, BigQuery and 50+ others, engineers integrate any service quickly.
• Daily ELT jobs
• Machine-learning retraining
• Complex dependency management
Keep DAG definitions in Git and deploy via CI/CD so that every change is auditable.
dbt Core 1.8 (released Q1 2025) adds native incremental streaming models, closing the batch gap. Its Jinja templating and tests turn warehouses into maintainable codebases.
Spark 4.0 now ships with ANSI SQL compliance and fine-grained adaptive query execution, cutting costs on cloud runtimes.
Kafka 4.1 (April 2025) brings KRaft consensus to general availability, removing ZooKeeper and simplifying ops.
Delta Lake 3.2 introduces column-level time travel and compatibility with Apache Iceberg manifests, easing interoperability.
Iceberg 1.5 (2025) focuses on high-concurrency writes and native REST catalog, allowing multi-engine access.
Dagster 2.2 blends software-defined assets with built-in data quality checks, giving smaller teams an integrated alternative to Airflow.
Flink 2.0 unifies runtime APIs, letting developers move between low-latency streams and large batch jobs without code rewrites.
Prefect 3.0 (2025) embraces serverless orchestration. Work pools spin up on-demand in Fargate or Cloud Run, cutting idle costs to zero.
Great Expectations Cloud remains closed source, but the OSS core gained Rule-Based Profilers in 2025, automating expectation creation for thousands of tables.
Mixing these projects yields a best-of-breed stack. A common 2025 pattern pairs Airflow for orchestration, dbt for warehouse transforms, Iceberg or Delta Lake for storage governance and Great Expectations for validation.
Spark or Flink tackle heavy compute while Kafka streams events in real time.
Even with the best OSS stack, engineers still need a fast, collaborative SQL workspace. Galaxy connects to every project above, surfaces metadata and lets teams version and endorse queries. Its context-aware AI copilot writes dbt models, Airflow DAG snippets and Iceberg DDL, speeding up integration with your 2025 data platform.
Apache Airflow still leads thanks to its mature ecosystem and TaskFlow API. Dagster and Prefect follow closely for teams that prefer opinionated data-aware assets or serverless execution.
Delta Lake originated in the Spark ecosystem and offers tight ACID guarantees plus native time travel. Iceberg is engine agnostic, supports hidden partitioning and a REST catalog, making it ideal for multi-compute lakehouses.
Pipelines break silently without validation. Great Expectations lets engineers codify expectations so bad data never lands in production tables, protecting downstream analytics.
Galaxy provides a lightning-fast SQL IDE that connects to Airflow, dbt, Iceberg catalogs and more. Engineers write, version and endorse queries, then share them across the company without copying SQL into chat tools.
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