nessie
delta
nessie | delta | |
---|---|---|
13 | 69 | |
834 | 6,897 | |
3.6% | 1.3% | |
9.9 | 9.8 | |
3 days ago | 6 days ago | |
Java | 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.
nessie
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A deep dive into the concept and world of Apache Iceberg Catalogs
Nessie is an innovative open-source catalog that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing git-like features to data management. This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development.
- FLaNK Stack Weekly 22 January 2024
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Why is Hive Metastore everywhere? (Especially Iceberg)
Try Nessie https://github.com/projectnessie/nessie - it recently got trino support as well ..
- What are the main things I need to know to be hired as a Java developer?
- Is learning and mastering Spring & Spring boot worth it in 2023 ?
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Which lakehouse table format do you expect your organization will be using by the end of 2023?
Project Nessie (https://projectnessie.org/) will be the catalog that eventually decouples Iceberg from Hive. At that point, I think it will be a no brainer to go Iceberg over Delta.
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5 Reasons Your Data Lakehouse should Embrace Dremio Cloud
The Dremio Sonar query engine can query your data where it exists whether it's AWS Glue, S3, Nessie Catalogs, MySQL, Postgres, RedShift and an ever growing list of sources.
- Project Nessie: Transactional Catalog for Data Lakes with Git-Like Semantics
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Introduction to The World of Data - (OLTP, OLAP, Data Warehouses, Data Lakes and more)
We will also need a catalog to track all of these tables, with the open source Project Nessie we can do just that, and also get great versioning features similar to using Git when developing applications allowing data engineers to practice "data as code" and "write-audit-publish" patterns on their data.
- DoltLab v0.2.0
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?
git-bug - Distributed, offline-first bug tracker embedded in git, with bridges
dvc - 🦉 ML Experiments and Data Management with Git
Apache Cassandra - Mirror of Apache Cassandra
hiveberg - Demonstration of a Hive Input Format for Iceberg
lakeFS - lakeFS - Data version control for your data lake | Git for data
dremio-oss - Dremio - the missing link in modern data
hudi - Upserts, Deletes And Incremental Processing on Big Data.
noms - The versioned, forkable, syncable database
delta-rs - A native Rust library for Delta Lake, with bindings into Python
dolt - Dolt – Git for Data
iceberg - Apache Iceberg