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Real-time analytics platforms: a practical comparison for 2026
A practical 2026 comparison of real-time analytics platforms across ingestion, query latency, freshness, and concurrency — managed and open source.
Last updated: Apr 17, 2026
Columnar storage formats: Parquet, ORC, and Arrow explained
How Apache Parquet, ORC, and Arrow lay out columnar data, and why they coexist instead of converging on one format.
Last updated: May 8, 2026
Row-oriented vs column-oriented databases: a head-to-head comparison
Row stores win point lookups and per-row updates; column stores win wide aggregations and compress 5–10× tighter. A measured comparison with a decision rule.
Last updated: May 8, 2026
What is vectorized query execution?
Vectorized query execution processes batches of values per operator call instead of one row at a time. It is the dominant execution model for analytical databases.
Last updated: May 15, 2026
How columnar storage works
A mechanism-level look at how columnar storage works on disk (per-column blocks, zone maps, and predicate pushdown), with ClickHouse MergeTree and Apache Parquet internals side by side.
Last updated: Jun 1, 2026
Why columnar databases are fast
Columnar databases are 10-1000× faster than row stores on analytical queries because two principles compound: efficient execution (storage layout, vectorisation) and smart pruning (data skipping, late materialisation).
Last updated: May 8, 2026
Best columnar databases in 2026
Nine columnar databases in 2026, ranked using measured benchmark latency and compression rather than marketing copy.
Last updated: May 8, 2026
When should you use a columnar database?
Use a columnar database when queries scan many rows but few columns and the workload is aggregation-heavy. Skip it for point lookups, per-row updates, and 10K+ TPS write workloads.
Last updated: May 8, 2026
How to build real-time customer-facing analytics on Postgres (without slowing down OLTP)
Build real-time customer-facing analytics on Postgres without slowing OLTP. Learn the 2-stage path to sub-100ms dashboards with CDC + OLAP.
Last updated: May 11, 2026
How to architect multi-tenant SaaS on Postgres
Architect multi-tenant SaaS on Postgres with secure isolation, scaling patterns, and compliance-ready design. Get the blueprint and ship safely today.
Last updated: May 25, 2026
What is an OLAP cube?
An OLAP cube is a multidimensional data structure that pre-aggregates measures across hierarchical dimensions. Cubes were the standard analytics implementation from the 1990s through the early 2010s; columnar OLAP engines run the same workloads without the pre-aggregation step.
Last updated: May 27, 2026
What are OLAP operations?
OLAP operations are the canonical analytical actions performed on multidimensional data: drill-down, roll-up, slice, dice, and pivot. Each maps to a specific SQL pattern.
Last updated: May 27, 2026
What is an OLAP database?
OLAP databases are optimised for analytical queries over millions to billions of rows. Here is how they work, the five categories on the market in 2026, and how to choose between them.
Last updated: May 27, 2026
Is Postgres an OLAP database?
Postgres is an OLTP database, not OLAP. Its row-oriented storage and B-tree indexes are built for point lookups and small writes, not the wide aggregations OLAP workloads run. Here is how each Postgres-for-analytics extension actually performs, and when CDC to a columnar engine is the right call.
Last updated: May 27, 2026
The fastest OLAP databases in 2026 (ranked by ClickBench)
The fastest OLAP engines deliver sub-second median query latency on a 100 GB analytical dataset. Here are the OLAP databases ranked by their results on ClickBench, the public benchmark for analytical query latency.
Last updated: May 27, 2026