delta
Rudderstack
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delta | Rudderstack | |
---|---|---|
69 | 83 | |
6,874 | 3,919 | |
1.8% | 1.3% | |
9.8 | 9.8 | |
about 9 hours ago | 2 days ago | |
Scala | Go | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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[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.
Rudderstack
- Rudderstack Switches to Elastic License
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What is the role of data integration in a Customer Data Platform (CDP)?
If CDP(such as RudderStack) were a restaurant, then Data Integration is the guy that gets all raw ingrediants from different shops and makes it available to Chef that sorts and combines raw ingrediants to make a dish. The chef can't cook anything without raw ingrediamt. Similarly Data Integration is also an important component in CDP that collects customer data from various sources and them other components unify it and activate it.
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Replacing Google Tag Manager with Open-Source alternative
More details on GitHub repository - https://github.com/rudderlabs/rudder-server
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In honor of this sub shutting down, I'm sharing my all-time favorite post.
Are you RudderStack?
- RudderStack v1.8 release - headless customer data platform
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Google Analytics 4 Has Me So Frustrated, We Built Our Own Analytics Service
In bigger setups, all you want is a data collector and router so that you can feed the data into multiple destinations, depending on the use case. Analytics is just one. Example: https://www.rudderstack.com/ & https://www.rudderstack.com/replace-google-analytics-4-guide...
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I want to contribute to open source but don't know where to start
Check out RudderStack, a Go project to build data pipeline. Our slack is quite active. The best way to contribute is by creating a new integration with your favorite tool. You do not need to rely to too much on existing knowledge about inner workings of the project to do so, so it is beginner friendly.
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Hot Takes on the Modern Data Stack
Interesting. About "Redshift need google sheet sync to table", wouldn't this be more aligned with the responsibility of a CDP(such as RudderStack) as opposed to something we expext a warehouse to do?
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Writing few lines of open-source js/python code can get ₹8k-80k. Is it a good reward for an oss challenge? Last day, more prizes than the participants until now :)
The challenge is over. Winners have been announced. When we are ready for the next one, will announce on RudderStack GitHub repo
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Project showcase: sample Data Lakehouse
Super. This is amazing. Sharing your project with the community. If you get a chance, try out RudderStack to build your pipeline.
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
Snowplow - The enterprise-grade behavioral data engine (web, mobile, server-side, webhooks), running cloud-natively on AWS and GCP
Apache Cassandra - Mirror of Apache Cassandra
PostHog - 🦔 PostHog provides open-source product analytics, session recording, feature flagging and A/B testing that you can self-host.
lakeFS - lakeFS - Data version control for your data lake | Git for data
Socioboard - Socioboard is world's first and open source Social Technology Enabler. Socioboard Core is our flagship product.
hudi - Upserts, Deletes And Incremental Processing on Big Data.
unomi - Apache Unomi
delta-rs - A native Rust library for Delta Lake, with bindings into Python
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
iceberg - Apache Iceberg
Apache Kafka - Mirror of Apache Kafka