Why Apache Spark is Not the Only Way Forward for Data Teams

The data processing landscape is evolving rapidly, and Apache Spark, though dominant for a long time, is not the only option data teams should consider today. With the growing need for efficient, flexible, and cost-effective processing solutions, emerging tools like ??????, ????????????, ????????, ????????????, Dask, ????????, and others are challenging the status quo.

Spark's strengths lie in distributed computing and scaling for large datasets, which has made it a go-to for big data tasks. However, it often comes with a significant setup complexity and overhead, especially for small to medium workloads. For these scenarios, newer frameworks offer compelling alternatives:

• ???????????? - Ideal for in-memory analytics, DuckDB excels at local queries and easily integrates with Python. For datasets that don’t need distributed computing, it significantly outperforms Spark. Benchmarks show that DuckDB is faster than Spark for smaller datasets and simpler ETL jobs.
• ?????? - Unlike Spark, Ray provides distributed computing support for Python-based applications with a flexible and simpler API. It is well-suited for data science and machine learning use cases, providing native support for libraries like Dask and Modin, and enabling parallelism across complex workflows without the JVM overhead.
• ???????????? - A highly performant DataFrame library in Rust, Polars provides excellent local performance for in-memory operations and can handle complex transformations more efficiently than Pandas. It has shown performance benefits on smaller datasets and offers Pythonic syntax that is more accessible for developers. But struggles with large volume of data and may not be an ideal choice for such cases.
• Dask and Ibis - These frameworks bring ease of use and flexibility, focusing on extending the familiar Pandas API to larger-than-memory data. Dask offers a lighter alternative to Spark for parallel processing in Python ecosystems, while Ibis serves as a bridge, enabling data wrangling across backends like DuckDB, BigQuery, and more. Dask is quite reliable and excels in distributed processing.
• Daft - A unified engine for data analytics, engineering, and ML/AI, DAFT offers both SQL and Python DataFrame interfaces as first-class citizens. Built in Rust, it delivers a snappy and efficient local interactive experience, while effortlessly scaling to handle petabyte-scale distributed workloads. DAFT's flexibility and performance make it ideal for both small-scale and large-scale data processing needs.

Benchmarks to Consider:

Benchmarks from DataCouncil (full article here: https://docs.coiled.io/blog/tpch.html) have shown that no tool is a consistent winner across scenarios.

Key Takeaways for Data Teams

• ???????????????? ?????? ??????????:Distributed systems like Spark are overkill for medium-scale data, and the extra coordination overhead can slow down performance.
• ???????????? ?????? ?????????? ???????? ?????? ?????? ????????: For ETL jobs or SQL-based analytics, DuckDB is a great choice. For in-memory operations, Polars might be preferable. When it comes to building ML pipelines or handling multi-node parallelism, Ray is more versatile than Spark.
• ???????????????? ??????????????????: Tools like Ibis and Daft integrate seamlessly with other libraries and have a lighter setup, enabling smoother experimentation and integration into modern data workflows.

Why should data teams take notice?

Data teams need to rethink the "one-size-fits-all" approach. Spark might still be the go-to for massive ETL jobs, but new tools like Ray, DuckDB, and Polars provide a more efficient path for specific use cases, from interactive analytics to lightweight distributed processing. Modern data workloads are more diverse, and using the right tool for the job can significantly cut down on complexity, costs, and runtime.

Is your team still relying solely on Spark? Maybe it’s time to explore these newer frameworks for a faster and more adaptable data architecture!