mlcourse.ai
dremio-oss
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mlcourse.ai | dremio-oss | |
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85 | 8 | |
9,390 | 1,298 | |
- | 1.2% | |
3.4 | 4.0 | |
4 months ago | 4 days ago | |
Python | Java | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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mlcourse.ai
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.
What are some alternatives?
attractors - Package for simulation and visualization of strange attractors.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
napari - napari: a fast, interactive, multi-dimensional image viewer for python
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]
concrete-numpy - Concrete-Numpy: A library to turn programs into their homomorphic equivalent.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
GreyNSights - Privacy-Preserving Data Analysis using Pandas
Greenplum - Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
hiitpi - A workout trainer Dash/Flask app that helps track your HIIT workouts by analyzing real-time video streaming from your sweet Pi using machine learning and Edge TPU..
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.
quaternion - Add built-in support for quaternions to numpy
Rakam - 📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)