What are people replacing Spark with in 2026?

Summary

• This guide doesn't estimate adoption. It maps workloads to the open-source engines whose own documentation says they're built for them.
• DuckDB and Polars target data that fits on one machine, Apache Flink targets stateful processing of unbounded event streams, and Ray and Dask target scaling Python code.
• Apache Spark keeps shipping. Spark 4.2.0, released July 14, 2026, added GEOMETRY and GEOGRAPHY types, a SQL CHANGES clause for reading row-level changes, and Arrow-optimized Python UDFs enabled by default.
• Before you split a Spark workload, check four things: how large the data will get, how results must arrive, what language the team writes, and whether the tables stay in an open format every engine can read.
• On Databricks, Ray clusters run in the same execution environment as a Spark cluster, and Unity Catalog's Iceberg REST catalog lets engines such as Apache Flink read and write its Iceberg tables.

What are people replacing Spark with in 2026?

This guide doesn't estimate how many teams have moved off Apache Spark. It maps each kind of workload to the open-source engines built for it, using their own docs: single-machine SQL and DataFrame engines, a dedicated stream processor, and frameworks for parallel Python. Spark covers distributed batch, SQL, streaming and machine learning in one engine, and its 4.2.0 release shipped on July 14, 2026. The right pick depends on data size, how results must arrive and the language your team writes, so decide workload by workload.

Which engine fits which workload

Each cell quotes or paraphrases that project's own documentation.

Workload	Open-source engine built for it	Spark's own option
SQL analytics on one machine	DuckDB, "a single-node database system" for analytical (OLAP) queries that runs inside the host process	Spark SQL, in local mode or on a cluster
DataFrame transformations on one machine	Polars, a DataFrame library with a Rust core that divides work among the machine's CPU cores	PySpark DataFrames and the pandas API on Spark
Stateful processing of unbounded event streams	Apache Flink, "a framework and distributed processing engine for stateful computations over unbounded and bounded data streams"	Structured Streaming, micro-batch by default
Distributed Python and AI workloads	Ray, "an open-source unified framework for scaling AI and Python applications"; Ray Data covers batch inference, preprocessing and training data loading	MLlib, "Apache Spark's scalable machine learning library"
Scaling existing pandas and NumPy code	Dask, "a Python library for parallel and distributed computing," whose DataFrames parallelize pandas	The pandas API on Spark, which scales to multiple machines in a cluster

Questions to ask before moving a workload

• Will the data fit on one machine for the life of the job? Single-machine engines need no cluster, and they grow by moving to a larger machine (DuckDB's FAQ calls this vertical scalability). Dask, Ray, Flink and Spark spread work across nodes instead.
• How must results arrive? Flink's docs say unbounded streams "must be continuously processed," with events handled promptly after ingestion, and they credit "precise control of time and state" for running any kind of application on unbounded streams. Spark's Structured Streaming treats "a live data stream as a table that is being continuously appended" and runs the query incrementally. Match the model to your latency and state requirements.
• What does the team write? SQL fits DuckDB and Spark SQL. DataFrames fit Polars and PySpark. pandas-style code fits Dask and the pandas API on Spark. General Python functions fit Ray's distributed computing primitives.
• Do the tables stay open? DuckDB and Polars read Delta Lake and Iceberg tables and both write Delta Lake tables, Iceberg ships Flink runtime JARs, and Delta Lake documents an Apache Flink connector. When the table format and catalog stay the same, changing engines doesn't require migrating data.
• How many engines will you run? Each added engine brings its own deployment, monitoring and upgrade cycle, so count that work before splitting one pipeline across several engines.

What Spark 4.2.0 added

Compare any move against the current release. The Spark 4.2.0 release notes list:

• Geospatial types. New GEOMETRY and GEOGRAPHY types with ST_* functions, enabled by default.
• Change data capture. A SQL CHANGES clause plus DataFrame, PySpark and Spark Connect APIs read row-level changes in batch and streaming. In Spark Declarative Pipelines (SDP), Auto CDC handles declarative SCD Type 1 upserts.
• Python execution. Arrow-optimized Python UDFs and Arrow-based PySpark IPC are now on by default.

Spark Connect, introduced in Spark 3.4, also decouples clients from the cluster: applications connect remotely through the DataFrame API.

How Databricks approaches mixed-engine workloads

Databricks runs other engines next to Spark. Ray on Databricks runs Ray applications with the platform's features, and its Ray clusters are managed in the same execution environment as a running Apache Spark cluster. Databricks' docs describe the two as complementary: Ray for logical parallelism such as machine learning tasks, Spark for data parallelism such as ETL and analytics. Flink jobs outside the platform can share governed tables too: Apache Flink is one of the clients that Unity Catalog's Iceberg REST catalog allows to read and write its Iceberg tables.

FAQs

Is Apache Spark still actively developed?

Yes. Apache Spark 4.2.0 was released on July 14, 2026, and its release notes describe it as the third release of the Spark 4 line. Spark has been an Apache Software Foundation top-level project since 2014.

Can Flink and Spark work on the same tables?

Yes, through an open table format. Iceberg's site says it lets engines including Spark and Flink "safely work with the same tables, at the same time," and Delta Lake documents connectors for both engines.

Do I need Spark to read Delta Lake or Iceberg tables?

No. DuckDB and Polars read both formats, PyIceberg reads Iceberg tables, and the delta-rs Python package reads Delta tables, all without a Spark cluster.

Can Ray and Spark run together?

Yes. On Databricks, Ray 2.3.0 and above can create Ray clusters and run Ray applications on Apache Spark clusters.