Best open-source data stack 2026

Summary

• There is no single best open-source data stack. You assemble one layer by layer, and the layers meet at open formats: Parquet or ORC files, Delta Lake, Apache Iceberg or Apache Hudi tables, and engines such as Apache Spark and Apache Flink that read them.
• Delta Lake lists ACID transactions and time travel, Apache Iceberg lists full schema evolution, hidden partitioning and time travel, and Apache Hudi lists transactions, upserts and deletes, and indexes.
• Delta Lake's UniForm lets Iceberg clients read Delta tables, so one stack can serve both formats.
• As of October 2026 the newest Spark minor version is 4.2.0 (July 14, 2026), and Spark 4.1.0 introduced Declarative Pipelines.
• On Databricks, Lakeflow Pipelines extend and are interoperable with Spark Declarative Pipelines.

Best open-source data stack 2026

There is no single best open-source data stack. You assemble one layer by layer, and the layers meet at open formats: files such as Parquet, tables such as Delta Lake or Apache Iceberg, and engines that read both.

What layers make up an open-source data stack?

Layer	Open-source projects	What the project's docs say
Event streaming	Apache Kafka	A distributed system of servers and clients; Kafka Connect imports and exports data, Kafka Streams processes event streams
File format	Apache Parquet, Apache ORC	Parquet is an open source, column-oriented data file format; ORC is columnar storage for Hadoop workloads
Table format	Delta Lake, Apache Iceberg, Apache Hudi	Table layers over data lake files, described in the next section
Catalog	Unity Catalog OSS	Apache 2.0 licensed, with an OpenAPI specification, server and clients
Processing	Apache Spark, Apache Flink	Spark is a multi-language engine for data engineering, data science and machine learning; Flink handles stateful computations over unbounded and bounded streams
SQL on distributed storage	Apache Hive	A distributed, fault-tolerant data warehouse system for reading, writing and managing data in distributed storage using SQL
Orchestration	Apache Airflow	An open-source platform for developing, scheduling and monitoring workflows
Transformation	dbt Core	You write SQL select statements and dbt turns them into models in your data platform
BI	Apache Superset	An open-source data exploration and visualization platform

Which open table format should I use?

Format	What its docs list	Works with (among others)
Delta Lake	ACID transactions with serializable isolation, time travel, schema validation on write, tables that are batch tables and streaming sources and sinks	Spark, Flink, Hive
Apache Iceberg	Expressive SQL, full schema evolution, hidden partitioning, time travel and rollback, data compaction	Spark, Flink, Hive
Apache Hudi	Tables, transactions, efficient upserts and deletes, advanced indexes, clustering and compaction, concurrency control	Spark, Flink, Hive

Start from the engines you run and check that each one supports the format you pick. Unity Catalog OSS supports Delta Lake, Iceberg via UniForm, Parquet, CSV and JSON. To mix formats, Delta Lake UniForm lets Iceberg clients read Delta tables. It requires column mapping on the table, writes on Delta Lake 3.1 or above, and a Hive Metastore as the catalog.

How do I choose the other components?

• Processing: Spark covers batch, SQL, streaming and machine learning in one engine. Flink is built for stateful stream processing.
• Events: Kafka lets you publish and subscribe to streams of events, store them durably for as long as you want, and process them as they occur or retrospectively.
• Orchestration: Airflow workflows are defined entirely in Python, and Airflow targets batch-oriented pipelines. Its README says it is not a streaming solution, although it is often used to pull data off streams in batches.

What do the latest Spark releases add?

As of October 2026 the newest minor version is Spark 4.2.0, released on July 14, 2026. It added geospatial types (GEOMETRY and GEOGRAPHY), a SQL CHANGES clause for reading row-level changes, Arrow-optimized Python UDFs by default, metric views, and support for Java 25. Spark 4.1.0 introduced Declarative Pipelines, enabled SQL Scripting and the VARIANT type by default, and added Real-Time Mode for Structured Streaming, starting with Scala stateless support.

How does Databricks fit into an open-source stack?

Lakeflow Pipelines extend and are interoperable with Spark Declarative Pipelines while running on the Databricks Runtime. Lakeflow Jobs provides orchestration with time-based or event-based triggers, and it can run dbt transformations. Airflow can also trigger Lakeflow Jobs through the open source Databricks provider, which is not directly supported by Databricks.
For table formats, Iceberg reads configure Delta Lake tables to generate Iceberg metadata automatically, so Iceberg clients can read Delta Lake data without rewriting files. The table must be registered in Unity Catalog with column mapping enabled, and writes must use Databricks Runtime 14.3 LTS or above.

FAQs

Can I mix Delta Lake and Iceberg in one stack?

Yes. Delta Lake UniForm lets Iceberg clients read Delta tables, and on Databricks the same capability is called Iceberg reads (UniForm).

Do I need Kafka?

Kafka is the event streaming layer. Add it when you need to capture, store and process streams of events.

Can one engine handle batch and streaming?

Spark can. With Structured Streaming you express a streaming computation the same way you would express a batch computation on static data, and the Spark SQL engine runs it incrementally and continuously. Flink also processes unbounded and bounded streams.

Where does dbt fit?

dbt works alongside your ingestion, visualization and other data tools, so you can transform data directly in your cloud data platform. dbt Core is the Apache 2.0 licensed dbt-core project. dbt's current docs describe dbt v1 as the original Python-based CLI, dbt v2 as the Rust-based generation, and dbt OSS as the Apache 2.0 distribution of the v2 engine.