4 posts tagged with "SQL"

Apache Spark 4.0 is the biggest leap for the project in years — and it's squarely aimed at the people who build and operate big data pipelines. The release sharpens four areas at once: SQL and workflow authoring, data types and observability, the Python/PySpark experience, and how clients connect to Spark. If you spin up a cluster on Databricks Runtime 17.0, these capabilities are available out of the box.

This article is an original, engineer-focused tour of what changed in Spark 4.0 and why each change matters in practice. If you want the fundamentals first, see our primers on Spark's key components and how Spark supports big data processing.

Apache Hadoop and Snowflake both store and process large datasets at scale — but they sit at opposite ends of the modern data architecture spectrum. Hadoop is a self-managed open-source stack where storage and compute live on the same cluster. Snowflake is a fully managed cloud data warehouse that separates storage from compute and bills per second of query time.

In 2026, the question rarely is "which one is better?". It is "which workload belongs on which platform, and what does each cost over five years?". Many enterprises run both: Hadoop (or its successor S3-based lakehouse) for cheap raw storage and large-scale ETL, Snowflake for governed analytics and BI on top.

This guide compares Hadoop vs Snowflake across architecture, query performance, total cost of ownership (TCO), and use cases — with a decision matrix and FAQ at the end.

Every organization building a big data platform eventually faces the same wall. The cluster is running. The pipelines are flowing. The dashboards are rendering. And yet the business doesn't trust the numbers.

Data engineers spend their days rebuilding queries that produce subtly wrong results. Analysts add footnotes to every report. Leadership qualifies every AI-generated recommendation with "take this with a grain of salt." The infrastructure investment is real, but the returns are phantom.

The root cause is almost always the same: data quality was treated as a downstream concern when it should have been an upstream strategy.

Running SQL queries directly over petabytes of Hadoop data — without moving it into a separate warehouse — is one of the defining capabilities of a mature data platform. But the landscape of SQL-on-Hadoop engines is crowded and fragmented. Choosing the wrong one means slow analyst queries, wasted infrastructure spend, or painful migration later.

This guide reviews 10 SQL-on-Hadoop tools available in 2025, covering architecture, strengths, limitations, and the workloads each one is best suited for.