{"data":[{"slug":"clickhouse","name":"ClickHouse","tagline":"Column-oriented database for analytics","category":"databases","url":"https://clickhouse.com","github":"https://github.com/ClickHouse/ClickHouse","pricing":"open-source","hook":"ClickHouse makes billion-row queries feel instant.","whatsGood":"Analytics queries that would take minutes in Postgres complete in seconds. Column-oriented storage is perfect for time-series and event data. Compression is aggressive. The SQL dialect is familiar.","theCatch":"Not a general-purpose database—updates and deletes are expensive. Operational complexity for self-hosting. The learning curve for optimal schema design is steep.","verdict":"The analytics database when Postgres is too slow.","addedAt":"2026-08-19","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","permissive","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"Apache-2.0","githubStars":50478,"starsPerWeek":93,"access":"free","archived":false,"repo":{"createdAt":"2016-06-02","pushedAt":"2026-10-19","license":"Apache-2.0","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/clickhouse","html":"/gems/clickhouse","markdown":"/gems/clickhouse.md"}},{"slug":"duckdb","name":"DuckDB","tagline":"In-process OLAP database","category":"databases","url":"https://duckdb.org","github":"https://github.com/duckdb/duckdb","pricing":"open-source","hook":"DuckDB is SQLite for analytics—fast, embedded, and serverless.","whatsGood":"Analytics queries run locally without a server. Parquet, CSV, and JSON import just works. The SQL dialect is comprehensive. Integration with Python and R is seamless.","theCatch":"Not for OLTP workloads—it is analytics-focused. Single-node limits apply. Less ecosystem than traditional databases.","verdict":"Local analytics that just works.","addedAt":"2026-08-19","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","local-first","permissive","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"OSS/MIT","githubStars":42253,"starsPerWeek":97,"access":"free","archived":false,"repo":{"createdAt":"2018-06-26","pushedAt":"2026-10-19","license":"MIT","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/duckdb","html":"/gems/duckdb","markdown":"/gems/duckdb.md"}},{"slug":"ducklake","name":"DuckLake","tagline":"MIT lakehouse table format on SQL + Parquet — a DuckDB extension","category":"databases","url":"https://ducklake.select","github":"https://github.com/duckdb/ducklake","pricing":"open-source","hook":"DuckLake is an open lakehouse format from the DuckDB team: table metadata lives in an ordinary SQL database, data lives in Parquet files on disk or object storage — snapshots, time travel, and schema evolution without a custom catalog server.","whatsGood":"MIT (Stichting DuckDB Foundation). DuckLake v1.0 is out; `INSTALL ducklake;` then `ATTACH 'ducklake:metadata.ducklake' AS lake (DATA_PATH 'files/');` and use plain SQL. Catalog can be DuckDB, PostgreSQL, SQLite, or MySQL; Parquet on local disk or S3-compatible storage; multiple clients against one catalog. The first lakehouse table format on Stack Gems. ★~3k.","theCatch":"Needs a separate catalog database you run and back up (DuckDB file for one writer, Postgres/MySQL for multi-client). Engines outside DuckDB support it far less widely than Iceberg or Delta Lake — check your Spark/Trino/warehouse story before committing. Younger format with a shorter production track record. Vs the duckdb gem: DuckDB is the engine; DuckLake is the table format and catalog layer on top.","verdict":"MIT SQL-catalog lakehouse format when DuckDB is your engine and Iceberg’s catalog stack feels heavy — confirm multi-engine support first.","addedAt":"2026-10-08","featured":false,"domain":"Databases","subSpecialty":"Lakehouse","subcategory":"Analytics & Time-series","tags":["self-host","permissive","active"],"capabilities":["lakehouse","parquet","sql"],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"OSS/MIT","githubStars":3316,"starsPerWeek":39,"access":"free","archived":false,"repo":{"createdAt":"2025-03-03","pushedAt":"2026-10-17","license":"MIT","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/ducklake","html":"/gems/ducklake","markdown":"/gems/ducklake.md"}},{"slug":"questdb","name":"QuestDB","tagline":"Apache-2.0 low-latency time-series database with SQL","category":"databases","url":"https://questdb.com","github":"https://github.com/questdb/questdb","pricing":"open-source","hook":"QuestDB is an open-source time-series database for very high ingestion rates: zero-GC Java with C++ and Rust on the hot paths, time-partitioned columnar storage and SQL with time-series extensions.","whatsGood":"Apache-2.0 (questdb/questdb), release 10.0.1 (2026-08-24), developed since 2014. Queries run across all cores with SIMD and JIT-compiled filters, storage is tiered (WAL, native columns, Parquet), and data stays open via Parquet, Iceberg and Arrow. It ingests over the InfluxDB line protocol and its own protocol, and speaks the Postgres wire protocol for queries. The project publishes ingestion benchmarks of 19M rows/sec per instance. ★~17.4k.","theCatch":"Benchmarks are the vendor's own. Automatic tiering of cold partitions to object storage, replication and other features are in QuestDB Enterprise; in open source you convert partitions to Parquet yourself. It is built for append-heavy time-series, not general OLTP with frequent updates.","verdict":"One of the fastest open time-series databases — great for market data and telemetry.","addedAt":"2026-10-18","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","permissive","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"Apache-2.0","githubStars":17449,"starsPerWeek":27,"access":"free","archived":false,"repo":{"createdAt":"2014-04-28","pushedAt":"2026-10-19","license":"Apache-2.0","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/questdb","html":"/gems/questdb","markdown":"/gems/questdb.md"}},{"slug":"trino","name":"Trino","tagline":"Apache-2.0 distributed SQL query engine for data lakes","category":"databases","url":"https://trino.io","github":"https://github.com/trinodb/trino","pricing":"open-source","hook":"Trino is a fast distributed SQL query engine for analytics: point it at data lakes (Iceberg, Delta, Hive), databases and object storage, and query them all with ANSI SQL, even joining across sources.","whatsGood":"Apache-2.0 (trinodb/trino), release 484 (2026-10-16), the community fork of PrestoSQL since 2019. Dozens of connectors, a cost-based optimizer, fault-tolerant execution for long queries, and reproducible builds since version 449. Ships as a Java server and an official Docker image. ★~13.3k.","theCatch":"It is a query engine, not storage: you still need a catalog and files or databases underneath. Running a coordinator plus workers on the JVM needs memory tuning, and building from source requires Java 25 and Docker. Starburst sells the commercial distribution; plain Trino is fully open.","verdict":"The standard open engine for SQL over a lakehouse — bring your own storage and catalog.","addedAt":"2026-10-18","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","permissive","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"Apache-2.0","githubStars":13345,"starsPerWeek":33,"access":"free","archived":false,"repo":{"createdAt":"2019-01-19","pushedAt":"2026-10-19","license":"Apache-2.0","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/trino","html":"/gems/trino","markdown":"/gems/trino.md"}},{"slug":"databend","name":"Databend","tagline":"Apache-2.0 Rust cloud data warehouse on object storage","category":"databases","url":"https://docs.databend.com","github":"https://github.com/databendlabs/databend","pricing":"open-source","hook":"Databend is an open-source data warehouse written in Rust that stores data on S3, Azure or GCS and runs analytics, vector search and full-text search in one SQL engine.","whatsGood":"Apache-2.0 (databendlabs/databend), developed since 2020. Elastic compute over object storage, transactions, automatic schema evolution, git-like data branching for safe experiments, and sandboxed Python UDFs. Try it locally from Python with the databend-driver package or run the full warehouse in Docker. ★~9.5k.","theCatch":"The README now positions it as an enterprise warehouse for AI agents, and the recommended start is Databend Cloud (free to start, commercial beyond that); self-hosting a cluster is more work. Smaller ecosystem than ClickHouse or DuckDB. Releases are frequent nightly-style builds, so pin versions.","verdict":"A Snowflake-style warehouse you can run yourself — compare with ClickHouse before choosing.","addedAt":"2026-10-18","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","permissive","vector","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"Apache-2.0","githubStars":9471,"starsPerWeek":30,"access":"free","archived":false,"repo":{"createdAt":"2020-10-10","pushedAt":"2026-10-19","license":"Apache-2.0","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/databend","html":"/gems/databend","markdown":"/gems/databend.md"}},{"slug":"starrocks","name":"StarRocks","tagline":"Apache-2.0 MPP SQL engine for lakehouse analytics","category":"databases","url":"https://www.starrocks.io","github":"https://github.com/StarRocks/starrocks","pricing":"open-source","hook":"StarRocks is a Linux Foundation query engine for fast, ad-hoc analytics, both on its own storage and directly on lakehouse formats, with a vectorized engine and MySQL-protocol compatibility.","whatsGood":"Apache-2.0 (StarRocks/starrocks), release 4.1.3 (2026-09-17). Vectorized execution, a cost-based optimizer, full TPC-H and TPC-DS SQL support, and real-time ingestion. It queries Iceberg, Hudi, Delta and Hive in place, and MySQL clients and BI tools connect without special drivers. ★~12.2k.","theCatch":"Performance claims (3x faster on average) are the project's own. A cluster is frontend and backend nodes to size and operate. It is an analytics engine, not a transactional database. CelerData sells the commercial version.","verdict":"A strong choice for customer-facing dashboards on big data — benchmark it on your own queries.","addedAt":"2026-10-18","featured":false,"domain":"Databases","subSpecialty":"Data","subcategory":"Analytics & Time-series","tags":["self-host","permissive","active"],"capabilities":[],"surfaces":["Self-host"],"ecosystem":["Multi-platform"],"licenseModel":"Apache-2.0","githubStars":12188,"starsPerWeek":46,"access":"free","archived":false,"repo":{"createdAt":"2021-09-04","pushedAt":"2026-10-19","license":"Apache-2.0","archived":false,"checkedAt":"2026-10-19"},"links":{"self":"/api/gems/starrocks","html":"/gems/starrocks","markdown":"/gems/starrocks.md"}}],"meta":{"total":7,"count":7,"offset":0,"limit":null,"sort":"curated","sorts":["curated","catalog","new","stars","trending","name"],"filters":{"subcategory":"analytics-and-time-series"},"next":null,"categories":["agents","nextjs","python","shopify","databases","devops","design","observability","rust","payments","comms","notifications","libraries"],"tags":["mcp","cli","tui","api","sdk","desktop-app","web-ui","editor-plugin","self-host","local-first","free-tier","paid-only","paid-led","permissive","copyleft","source-available","license-unclear","coding-agents","local-llm","rag","memory","evals","browser-automation","scraping","sandbox","skills","voice","security","auth","payments","realtime","postgres","vector","kubernetes","containers","observability","testing","workflows","graphics-3d","git","active","stale","archived","young-repo","fast-stars"]}}