metabase-clickhouse-driver
duckdb
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| metabase-clickhouse-driver | duckdb | |
|---|---|---|
| 1 | 52 | |
| 447 | 16,576 | |
| 2.5% | 10.7% | |
| 6.9 | 10.0 | |
| 7 days ago | 6 days ago | |
| Clojure | C++ | |
| Apache License 2.0 | MIT License |
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metabase-clickhouse-driver
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ClickHouse, Inc
There’s a community connector for metabase https://github.com/enqueue/metabase-clickhouse-driver
duckdb
- 🪄 DuckDB sql hack : get things SORTED w/ constraint CHECK
- DuckDB: Move to push-based execution model (2021)
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DuckDB performance improvements with the latest release
I'm not sure if the fix is reassuring or not: https://github.com/duckdb/duckdb/pull/9411/files
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Building a Distributed Data Warehouse Without Data Lakes
It's an interesting question!
The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.
It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.
[1] https://github.com/bacalhau-project/bacalhau
[2] https://github.com/duckdb/duckdb
- DuckDB 0.9.0
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Push or Pull, is this a question?
[4] Switch to Push-Based Execution Model by Mytherin · Pull Request #2393 · duckdb/duckdb (github.com)
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Show HN: Hydra 1.0 – open-source column-oriented Postgres
it depends on your query obviously.
In general, I did very deep benchmarking of pg, clickhouse and duckdb, and I sure didn't make stupid mistakes like this: https://news.ycombinator.com/item?id=36990831
My dataset has 50B rows and 2tb of data, and I think columnar dbs are very overhiped and I chose pg because:
- pg performance is acceptable, maybe 2-3x times slower than clickhouse and duckdb on some queries if pg is configured correctly and run on compressed storage
- clickhouse and duckdb start falling apart very fast because they specialized on very narrow type of queries: https://github.com/ClickHouse/ClickHouse/issues/47520 https://github.com/ClickHouse/ClickHouse/issues/47521 https://github.com/duckdb/duckdb/discussions/6696
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🦆 Effortless Data Quality w/duckdb on GitHub ♾️
This action installs duckdb with the version provided in input.
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Using SQL inside Python pipelines with Duckdb, Glaredb (and others?)
Duckdb: https://github.com/duckdb/duckdb - seems pretty popular, been keeping an eye on this for close to a year now.
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CSV or Parquet File Format
The Parquet-Go library is very complex, not yet success to use it. So I ask whether DuckDB can provide API https://github.com/duckdb/duckdb/issues/7776
What are some alternatives?
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
datasette - An open source multi-tool for exploring and publishing data
metabase-school - A Metabase-integrated, real-time collaborative tool for writing SQL
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
tsbs - Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data
datafusion - Apache DataFusion SQL Query Engine
toucan - A classy high-level Clojure library for defining application models and retrieving them from a DB
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.