delta
lakeFS
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delta | lakeFS | |
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69 | 48 | |
6,782 | 4,022 | |
1.9% | 3.0% | |
9.8 | 9.8 | |
6 days ago | 6 days ago | |
Scala | Go | |
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.
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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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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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.
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How to build a data pipeline using Delta Lake
This sounds like a new trending destination to take selfies in front of, but it’s even better than that. Delta Lake is an “open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance.” (source). It let’s you interact with an object storage system like you would with a database.
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Delta.io/deltalake self hosting
I mean the different between using the delta.io framework to let it run on your own machines/ vms vs using databricks and have clusters defined.
You are right, delta.io is just a framework. Sorry for the unclear question. Another try: when you host spark on your own with delta as table format compared to usage of Databricks, what are the differences?
lakeFS
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Jujutsu: A Git-compatible DVCS that is both simple and powerful
Might want to look at purpose built tools for that such as lakeFS (https://github.com/treeverse/lakeFS/)
* Disclaimer: I'm one of the creators/maintainers of the project.
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Data diffs: Algorithms for explaining what changed in a dataset (2022)
Might want to checkout lakeFS: https://github.com/treeverse/lakeFS
(full disclosure: I'm one of the creators)
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Dolt Is Git for Data
Also in the same vein, check out https://lakefs.io/
- [P] ArtiV: Version control system for large files
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Data Science Workflows — Notebook to Production
Git was designed for managing software development projects and for versioning text/code files. Therefore, Git doesn’t handle large files. Git released Git LFS (Large File System) to overcome large file versioning, which is better than Git, but fails when scaling. Also, both Git and Git LFS are not optimized for data science workflow. To overcome this challenge, many powerful tools emerged in recent years, such as DVC, Delta Lake, LakeFS, and more.
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Unstructured Data Governance for ML
LakeFS: https://lakefs.io/
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LakeFS Turns 1 and Raises 15M in a Week: (Enable Git for Large-Scale Data Lakes)
Hello HN!
We're Oz and Einat, co-founders of lakeFS (https://lakefs.io/), an open-source project that allows the creation of performant git-like repositories over an object store (i.e. S3).
Prior to starting lakeFS we were VP of R&D and CTO at SimilarWeb, a (now-public) Israeli web analytics company whose business model is based on the collection and analysis of the internet's activity.
Recovering from a pernicious error in a million S3 files shouldn't require a full day or even week of work to fix… instead let's make it an instantaneous revert operation to a previous commit.
The challenge to implement this type of functionality is a technical one, one we took it upon ourselves to solve. It's been 1 year since the first public commit on lakeFS and we've now raised a $15M Series A to continue building and improving our vision.
We've evolved a ton in the past year, completely refactoring the data model to remove dependency on Postgres. Fittingly, we now use rocksDB on the object store to persist the metadata lakeFS manages (with the added benefit of simplifying the installation process). Check out the roadmap to follow our progress on building out native integrations with other important technologies in the open data stack such as Spark, Hive Metastore, and Delta Lake.
We encourage you to check out our Github repo: (https://github.com/treeverse/lakeFS) and documentation pages: (https://docs.lakefs.io/).
We're proud of how far we've come, but know there's lots more work to do. Please do let us know your thoughts!
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Gopher Gold #14 - Wed Oct 07 2020
treeverse/lakeFS (Go): An open source platform that delivers resilience and manageability to object-storage based data lakes
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
Apache Cassandra - Mirror of Apache Cassandra
hudi - Upserts, Deletes And Incremental Processing on Big Data.
delta-rs - A native Rust library for Delta Lake, with bindings into Python
iceberg - Apache Iceberg
git-lfs - Git extension for versioning large files
Ory Kratos - Next-gen identity server replacing your Auth0, Okta, Firebase with hardened security and PassKeys, SMS, OIDC, Social Sign In, MFA, FIDO, TOTP and OTP, WebAuthn, passwordless and much more. Golang, headless, API-first. Available as a worry-free SaaS with the fairest pricing on the market!
Apache Avro - Apache Avro is a data serialization system.
delta-oss
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
MLflow - Open source platform for the machine learning lifecycle