dremio-oss
q
| dremio-oss | q | |
|---|---|---|
| 8 | 46 | |
| 1,301 | 10,126 | |
| 0.8% | - | |
| 4.0 | 2.1 | |
| 14 days ago | 13 days ago | |
| Java | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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
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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.
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What is dremio query engine
Dremio core is actually fully open source: https://github.com/dremio/dremio-oss
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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
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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.
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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.
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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.
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Data Science Competition
Dremio
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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.
q
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I wrote this iCalendar (.ics) command-line utility to turn common calendar exports into more broadly compatible CSV files.
CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller
- Segítség kérés Excel automatizáláshoz
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Show HN: ClickHouse-local – a small tool for serverless data analytics
I think they're talking about https://github.com/harelba/q, which is not very fast.
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sqly - execute SQL against CSV / JSON with shell
Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.
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Q – Run SQL Directly on CSV or TSV Files
Hi, author of q here.
Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.
In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.
Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.
http://harelba.github.io/q/#auto-caching-examples
- PostgreSQL alternative for Large amounts of data
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q VS trdsql - a user suggested alternative
2 projects | 25 Jun 2022
- One-liner for running queries against CSV files with SQLite
What are some alternatives?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
textql - Execute SQL against structured text like CSV or TSV
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]
csvq - SQL-like query language for csv
ClickHouse - ClickHouse® is a free analytics DBMS for big data
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
Greenplum - Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
InquirerPy - :snake: Python port of Inquirer.js (A collection of common interactive command-line user interfaces)
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.
xsv - A fast CSV command line toolkit written in Rust.
Rakam - 📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
ledger - Double-entry accounting system with a command-line reporting interface