ibis
duckdb
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ibis | duckdb | |
---|---|---|
23 | 52 | |
4,208 | 16,749 | |
10.9% | 11.6% | |
10.0 | 10.0 | |
3 days ago | 1 day ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ibis
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Show HN: Hashquery, a Python library for defining reusable analysis
I really don't understand the appeal of dbt vs a proper programming language. The templating approach leads to massive spaghetti. I look forward to trying out something like Ibis [0]
0: https://ibis-project.org/
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This Week In Python
ibis – portable Python dataframe library
- Ibis: The portable Python dataframe library
- FLaNK Stack 26 February 2024
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Quarto
The main benefit is that you get a Python (or R, Julia or Rust) interpreter. So you can evaluate code. A good example of the value of this is the Ibis docs which use Quarto: https://ibis-project.org/
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Polars – A bird's eye view of Polars
Ive found polars quite intuitive, though for python, I lean more towards [ibis](https://ibis-project.org/). The interface is nearly identical, but ibis has the benefit if building sql queries before pulling any actual data (like dbplyr) — whereas polars requires the data to be in-memory (at least for rdb’s, though correct me if Im wrong)
this to me seems like a good argument for only using ibis, but Im happy to be convinced otherwise
- Ibis – Universal Interface for Data Wrangling
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Vanna.ai: Chat with your SQL database
Please add Ibis Birdbrain https://ibis-project.github.io/ibis-birdbrain/ to the list. Birdbrain is an AI-powered data bot, built on Ibis and Marvin, supporting more than 18 database backends.
See https://github.com/ibis-project/ibis and https://ibis-project.org for more details.
- Ibis
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?
snowflake-connector-python - Snowflake Connector for Python
ClickHouse - ClickHouse® is a free analytics DBMS for big data
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
Apache Impala - Apache Impala
datasette - An open source multi-tool for exploring and publishing data
pangres - SQL upsert using pandas DataFrames for PostgreSQL, SQlite and MySQL with extra features
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
sqlite_scanner - DuckDB extension to read and write to SQLite databases
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end
katacoda
datafusion - Apache DataFusion SQL Query Engine