versatile-data-kit
delta
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versatile-data-kit | delta | |
---|---|---|
52 | 69 | |
410 | 6,897 | |
2.4% | 2.5% | |
9.7 | 9.8 | |
6 days ago | 3 days ago | |
Python | Scala | |
Apache License 2.0 | Apache License 2.0 |
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.
versatile-data-kit
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Looking for a data blogger
Here's the project: https://github.com/vmware/versatile-data-kit
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Need advice on ETL tool
I don't really know if this would work for you because the UI is not functional yet, but a very simple REST API ingestion example here, there's one for csv too https://github.com/vmware/versatile-data-kit/wiki/Ingesting-data-from-REST-API-into-Database I can't imagine a simpler way unless it's really drag and drop.
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If dbt is the "T" part of an "ELT", what do you use for "EL"?
I work at VMware and we use one tool for the whole ELT, it was made internally as there was no good alternative at the time and now we opensourced it, here it is: https://github.com/vmware/versatile-data-kit
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Best way to fix errors in my data?
With my team we created csv ingestion plugin described here, maybe you want to try it out: https://github.com/vmware/versatile-data-kit/wiki/Ingesting-local-CSV-file-into-Database
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What Orchestration Tool do you use for batch ETL/ELT?
We use Versatile Data Kit for batch data job orchestration (https://github.com/vmware/versatile-data-kit)
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Dear, pipeline builders! Which step in your role is the most time consuming?
"suggestions on how to reduce the time spent on initially generating and adjusting the code" is using some tools that automate ELT. Here's one open-source tool I'm working on with my team: https://github.com/vmware/versatile-data-kit
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Problem definition / vibe check for a repo
here's the repo: https://github.com/vmware/versatile-data-kit
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Can we take a moment to appreciate how much of dataengineering is open source?
If you wish to contribute, projects usually have good first issues: https://github.com/vmware/versatile-data-kit/labels/good%20first%20issue If you wish to learn, check out examples: https://github.com/vmware/versatile-data-kit/tree/main/examples
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ETL question (noob)
Have you heard about versatile data kit (https://github.com/vmware/versatile-data-kit)? I think it meets your needs perfectly:
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DE Open Source
Versatile Data Kit is a framework to bBuild, run and manage your data pipelines with Python or SQL on any cloud https://github.com/vmware/versatile-data-kit here's a list of good first issues: https://github.com/vmware/versatile-data-kit/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22 Join our slack channel to connect with our team: https://cloud-native.slack.com/archives/C033PSLKCPR
delta
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Delta Lake vs. Parquet: A Comparison
Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it.
I think the website is here: https://delta.io
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Understanding Parquet, Iceberg and Data Lakehouses
I often hear references to Apache Iceberg and Delta Lake as if they’re two peas in the Open Table Formats pod. Yet…
Here’s the Apache Iceberg table format specification:
https://iceberg.apache.org/spec/
As they like to say in patent law, anyone “skilled in the art” of database systems could use this to build and query Iceberg tables without too much difficulty.
This is nominally the Delta Lake equivalent:
https://github.com/delta-io/delta/blob/master/PROTOCOL.md
I defy anyone to even scope out what level of effort would be required to fully implement the current spec, let alone what would be involved in keeping up to date as this beast evolves.
Frankly, the Delta Lake spec reads like a reverse engineering of whatever implementation tradeoffs Databricks is making as they race to build out a lakehouse for every Fortune 1000 company burned by Hadoop (which is to say, most of them).
My point is that I’ve yet to be convinced that buying into Delta Lake is actually buying into an open ecosystem. Would appreciate any reassurance on this front!
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
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[D] Is there other better data format for LLM to generate structured data?
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json.
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Delta vs Iceberg: make love not war
Delta 3.0 extends an olive branch. https://github.com/delta-io/delta/releases/tag/v3.0.0rc1
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through https://delta.io . They integrate pretty seamlessly to other data visualisation tooling if you want to use it for that but their built in graphs are fine for most cases. They also have ML on rails type through menus and models if I recall but I typically don't use it for that. I've typically used it for ETL or ELT type workflows for data that's too big or isn't stored in a database.
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The "Big Three's" Data Storage Offerings
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
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Ideas/Suggestions around setting up a data pipeline from scratch
As the data source, what I have is a gRPC stream. I get data in protobuf encoded format from it. This is a fixed part in the overall system, there is no other way to extract the data. We plan to ingest this data in delta lake, but before we do that there are a few problems.
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Medallion/lakehouse architecture data modelling
Take a look at Delta Lake https://delta.io, it enables a lot of database-like actions on files
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CSV or Parquet File Format
I prefer parquet (or delta for larger datasets. CSV for very small datasets, or the ones that will be later used/edited in Excel or Googke sheets.
What are some alternatives?
data-engineering-zoomcamp - Free Data Engineering course!
dvc - 🦉 ML Experiments and Data Management with Git
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
Apache Cassandra - Mirror of Apache Cassandra
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
lakeFS - lakeFS - Data version control for your data lake | Git for data
pyramid-jsonapi - Auto-build JSON API from sqlalchemy models using the pyramid framework
hudi - Upserts, Deletes And Incremental Processing on Big Data.
dbt-data-reliability - dbt package that is part of Elementary, the dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
delta-rs - A native Rust library for Delta Lake, with bindings into Python
hamilton - A scalable general purpose micro-framework for defining dataflows. THIS REPOSITORY HAS BEEN MOVED TO www.github.com/dagworks-inc/hamilton
iceberg - Apache Iceberg