Best distributed compute engine for data

Summary

• No single engine is best for every data workload. Match each project's documented design to your workload: batch and SQL, stateful streams, single-machine analytics, or Python and AI at scale.
• Apache Spark is a unified analytics engine for large-scale data processing, with Spark SQL, Structured Streaming and MLlib. It runs on one machine or on standalone, YARN or Kubernetes.
• Apache Flink is a distributed processing engine for stateful computations over unbounded and bounded data streams.
• DuckDB is an analytical SQL database that runs in-process, from edge devices to servers with hundreds of cores.
• Ray is an open source framework for scaling AI and Python applications from a laptop to a cluster.

What is the best distributed compute engine for data?

There is no single best engine, because each open-source project documents a different center of gravity. Start from the workload: batch and SQL pipelines, continuous stateful stream processing, analytics on one machine, or scaling Python and AI code. The sections below stick to each project's own documentation.

When does Apache Spark fit?

Spark is a unified analytics engine for large-scale data processing, with APIs in Java, Scala, Python and R, plus Spark SQL, the pandas API on Spark, MLlib and Structured Streaming. The docs describe running it locally on one machine, and it also runs on its standalone manager, YARN or Kubernetes.
Structured Streaming ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. By default it processes streams as a series of small batch jobs (micro-batch). Lower-latency options exist. Continuous Processing, introduced in Spark 2.3, comes with at-least-once guarantees. Real-Time Mode arrived in Spark 4.1.0 with Scala stateless support, and Spark 4.2.0 added its trigger to PySpark. Spark 4.1.0 also added Spark Declarative Pipelines for batch and streaming pipelines.

When does Apache Flink fit?

Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Its docs say stateful applications are optimized for local state access, which yields very low processing latencies, and that exactly-once state consistency comes from periodic, asynchronous checkpointing to durable storage. Processing bounded streams is what Flink calls batch processing.
Flink offers layered APIs: Process Function for stateful and timely processing, the DataStream API, the Table API, and SQL. The DataStream API includes tumbling, sliding, session and global windows, and FlinkCEP covers complex event processing. Typical event-driven applications in the docs include fraud detection, anomaly detection and rule-based alerting. Flink deploys on YARN, Kubernetes or a stand-alone cluster, and its web interface has a tab for monitoring back pressure.

When does DuckDB fit?

DuckDB is an analytical SQL database that can run in-process. It has no DBMS server software to install, update or maintain, and it runs embedded in a host process. Its docs describe deployment from edge devices to servers with hundreds of cores, so it scales up one machine. For work that must spread across many machines, look at the other engines here. DuckDB reads CSV, JSON, Parquet and Iceberg locally or in object storage, its delta extension reads and writes Delta tables, and it is MIT-licensed. The Quack protocol adds client-server access.

When does Ray fit?

Ray is an open source unified framework for scaling AI and Python applications, and it can scale from a laptop to a cluster. It provides tasks and actors as compute primitives. Its libraries cover Ray Data for data ingest and preprocessing, Ray Train for machine learning training, Ray Tune for hyperparameter tuning, Ray Serve for model serving and Ray RLlib for reinforcement learning.

How do I pick between them?

Workload	Engine to evaluate first	Documented basis
Batch and SQL pipelines, with streaming in the same engine	Spark	Unified engine with Spark SQL and Structured Streaming
Continuous stateful processing of event streams	Flink	Stateful computations over unbounded and bounded streams
Analytics on one machine or inside an application	DuckDB	In-process analytical SQL database
Scaling Python, ML training, tuning or serving	Ray	Libraries for each stage of the ML workflow
Pipelines declared as datasets and queries	Spark Declarative Pipelines	Added in Spark 4.1.0

What does Databricks add for Spark workloads?

Databricks Runtime runs Apache Spark. Lakeflow Pipelines extend and are interoperable with Spark Declarative Pipelines, and a pipeline runs in triggered mode, which refreshes available data and stops, or continuous mode, which keeps tables fresh as new data arrives. Photon accelerates SQL workloads, DataFrame API calls, ETL pipelines and stateless streaming workloads, and it does not support stateful streaming.

FAQs

Can Spark do real-time streaming?

Micro-batch is the default Structured Streaming engine. Spark 4.1.0 added Real-Time Mode, described in its release notes as first official support for queries running in real-time mode for continuous, sub-second latency processing. It covers Scala stateless queries in 4.1, and 4.2.0 added the PySpark trigger, so check the release notes for your version before relying on a given operator.

Can I combine engines on the same data?

Yes, when they share a table format. Delta Lake's Flink connector enables reading from and writing to Delta tables from Apache Flink, and Apache Iceberg supports Flink's DataStream and Table APIs. DuckDB reads Iceberg and has the delta extension, so one engine can ingest while another queries.

Is DuckDB a substitute for a cluster engine?

Its docs say it is designed for analytical workloads of complex, relatively long-running queries that process significant portions of the stored dataset. If your data and queries fit on one machine, DuckDB's single-process design may be enough. If they do not, evaluate Spark, Flink or Ray.