Database Insights

Build a real-time analytics pipeline from PostgreSQL to ClickHouse using CDC — architecture, latency, and schema mapping

Understand the differences between OLAP and OLTP systems — query patterns, storage engines, and when to use a hybrid approach

Compare Delta Lake, Apache Iceberg, and Apache Hudi — ACID transactions, time travel, and format interoperability

Use ClickHouse for sub-second analytics on billions of rows. Covers MergeTree table engine, LowCardinality types, AggregatingMergeTree materialized views, and MySQL ingestion.

ClickHouse MergeTree is why ClickHouse queries 1 billion rows in 0.3 seconds. Learn how parts, merges, and sparse indexes work, and how to choose the ORDER BY key, secondary indexes, and MergeTree variants.

DuckDB brings OLAP query performance to your Python process without a server or cluster. Learn how to query Parquet files, replace heavy ETL jobs, and run analytics directly against PostgreSQL.

Use TimescaleDB for time-series data in PostgreSQL. Covers hypertable creation, continuous aggregates with refresh policies, retention policies, and 10-20x compression.

ClickHouse Cloud is convenient but can cost 3-5x more than a well-tuned self-hosted cluster at scale. This breakdown shows exactly where the cost lines cross.

Two years after the Elastic license change, Elasticsearch and OpenSearch have diverged significantly. Both are mature forks but the right choice depends on your use case, vendor lock-in tolerance, and team expertise.

A detailed comparison of ClickHouse and BigQuery for analytical workloads — MergeTree engine vs Capacitor storage, self-hosted vs serverless cost models, query latency, real-time ingestion, and when to choose each.

Compare Snowflake, ClickHouse, and BigQuery on performance, cost, ecosystem, and real-world use cases

Use StarRocks for high-performance vector search with HNSW indexes. Learn real-time vector ingestion, hybrid queries combining vectors with analytics, and scaling strategies.