Jupyter Scala
delta
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Jupyter Scala | delta | |
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6 | 69 | |
1,561 | 6,847 | |
0.2% | 1.8% | |
9.0 | 9.8 | |
1 day ago | 6 days ago | |
Scala | Scala | |
BSD 3-clause "New" or "Revised" License | 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.
Jupyter Scala
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💐 Making VSCode itself a Java REPL 🔁
Checkout almond
- A Python-compatible statically typed language erg-lang/erg
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EDA libraries for Scala and Spark?
What about https://github.com/alexarchambault/plotly-scala and https://almond.sh/
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Is there any editor or IDE that supports Ammonite with inline dependencies?
I use Almond in JupyterLab, which has pretty solid code completion. In IntelliJ, you can create a scratch sc file and run lines of it in the Scala REPL. That's really convenient for code completion and I normally will use that when I'm testing something from a specific project.
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Recommended option for "Java with different syntax"?
The UI part. There's only the scala REPL. I think the closest is a scala kernel for Jupyter notebooks, check this out: https://almond.sh/
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An SQL Solution for Jupyter
We have used https://almond.sh/ to create a Spark SQL interpreter using Jupyter Notebooks - plus a whole lot more which you can see here: https://arc.tripl.ai/tutorial
After seeing many companies writing ETL using code we decided it was too hard to manage at scale so provided this abstraction layer - which is heavily centered around expressing business logic in SQL - to standardise development (JupyterLab) and allow rapid deployments.
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?
sparkmagic - Jupyter magics and kernels for working with remote Spark clusters
dvc - 🦉 ML Experiments and Data Management with Git
Metals - Scala language server with rich IDE features 🚀
Apache Cassandra - Mirror of Apache Cassandra
Apache Flink - Apache Flink
lakeFS - lakeFS - Data version control for your data lake | Git for data
Vegas - The missing MatPlotLib for Scala + Spark
hudi - Upserts, Deletes And Incremental Processing on Big Data.
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
delta-rs - A native Rust library for Delta Lake, with bindings into Python
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
iceberg - Apache Iceberg