12 articles found
DuckDB v2.0 adds server mode, triggers, async I/O, and a 40x recursive CTE speedup. The embedded analytics revolution isn’t coming. It’s here.
A transpiler converts DuckDB’s buttery-smooth SQL into whatever crusty dialect your enterprise warehouse speaks. No server installs, no schema migration, just better queries.
An open-source SDK liberates DuckLake’s streamlined SQL+Parquet architecture from its native client, inviting Polars and others to the lakehouse party.
How DuckDB, Polars, and friends are dismantling the distributed dogma, proving you don’t need a Spark cluster to crunch terabytes.
DuckLake’s data inlining promises 926x faster queries than Iceberg by storing streaming data directly in the catalog database. We dissect the architecture, benchmarks, and whether this is genuine innovation or just moving the bottleneck.
How to convert massive 80GB CSV files to Parquet without melting your RAM, and why DuckDB has become the default tool for data engineers fighting memory constraints.
Evaluating the technical cost-benefit of replacing Pandas memory-intensive operations with DuckDB direct-to-cloud queries for large-scale validation tasks on S3.
Why data engineers are abandoning Spark clusters for lightweight tools that run on laptops, and why your ‘big data’ pipeline probably doesn’t need to be big at all.
Real engineering teams are running DuckDB in production, cutting costs by 70% and outperforming Spark clusters. But is an embedded analytics database really ready for enterprise workloads, or are we just tired of overcomplicated data stacks?
MotherDuck’s elimination of its $25 Lite plan in favor of a $250 Business tier represents more than a price increase, it’s a fundamental shift away from serving small teams and solo analysts. Here’s what the data reveals about the alternatives and the future of affordable cloud analytics.
Benchmark results show DuckDB significantly outperforming BigQuery and Athena in speed and cost on a 20GB dataset, sparking questions about the future of cloud data warehouses.
A brutal case study comparing Java streaming approaches against modern tools like DuckDB and Spark for massive data ingestion, revealing why traditional methods are costing you time and sanity.
