dremio-oss
duckdb
dremio-oss | duckdb | |
---|---|---|
8 | 52 | |
1,301 | 16,749 | |
0.8% | 4.5% | |
4.0 | 10.0 | |
14 days ago | 7 days ago | |
Java | 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.
dremio-oss
-
What is the separation of storage and compute in data platforms and why does it matter?
Dremio - Dremio is a data lakehouse based on the open-source Apache Iceberg table format. It offers different compute instances to process data that lives in your S3 bucket. You pay for S3 storage independently.
-
What is dremio query engine
Dremio core is actually fully open source: https://github.com/dremio/dremio-oss
-
Q – Run SQL Directly on CSV or TSV Files
I have been using Dremio to query large volume of CSV files: https://docs.dremio.com/software/data-sources/files-and-dire...
Although having them in some columnar format is much better for fast responses.
GitHub: https://github.com/dremio/dremio-oss
-
Hands-On Introduction to Apache Iceberg - Data Lakehouse Engineering
As a Developer Advocate for Dremio I spend a lot of time doing research on technology and best practices around engineering Data Lakehouses and sharing what I learn through content for Subsurface - The Data Lakehouse Community. One of the major topics I've been diving deep into is the topic of Data Lakehouse Table Formats, these allow you to take the files on your data lake and group them into tables data processing engines like Dremio can operate on.
-
Introduction to The World of Data - (OLTP, OLAP, Data Warehouses, Data Lakes and more)
Hearing about all these components sounds great, but what everyone wants isn't to have to setup and configure all these components but instead have a platform and tool that brings this all together in an easy to use package, and that platform is Dremio. With Dremio you can work with the data directly from your data lake. No copies, easy access, high performance.
-
Data Lakehouse and Delta Lake
And as u/pych_phd said, it's not just Databricks, Snowflake and Azure who make these claims, even AWS, GCP, Dremio and I'm sure many others are too.
-
Data Science Competition
Dremio
-
Build your own “data lake” for reporting purposes
For my home projects I generate parquet (columnar and very well suited for DW like queries) files with pyarrow and use https://github.com/dremio/dremio-oss (https://www.dremio.com/on-prem/) to query them on lake (minio or just local disk or s3) and use Apache Superset for quick charts or dashboards.
duckdb
- 🪄 DuckDB sql hack : get things SORTED w/ constraint CHECK
- DuckDB: Move to push-based execution model (2021)
-
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
-
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
-
Push or Pull, is this a question?
[4] Switch to Push-Based Execution Model by Mytherin · Pull Request #2393 · duckdb/duckdb (github.com)
-
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
-
🦆 Effortless Data Quality w/duckdb on GitHub ♾️
This action installs duckdb with the version provided in input.
-
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.
-
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?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
ClickHouse - ClickHouse® is a free analytics DBMS for big data
presto - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io) [Moved to: https://github.com/trinodb/trino]
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
datasette - An open source multi-tool for exploring and publishing data
Greenplum - Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end
Rakam - 📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
datafusion - Apache DataFusion SQL Query Engine