ballista
superset
ballista | superset | |
---|---|---|
20 | 137 | |
2,238 | 58,956 | |
- | 1.7% | |
9.3 | 9.9 | |
about 3 years ago | 1 day ago | |
Rust | TypeScript | |
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.
ballista
- Ballista: Distributed compute platform implemented in Rust using Apache Arrow.
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Open source contributions for a Data Engineer?
His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.
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Best format to use for DataFrames in Rust and Python?
https://github.com/ballista-compute/ballista/blob/main/rust/executor/src/flight_service.rs#L193-L228
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I wrote one of the fastest DataFrame libraries
I'm guessing Polars and Ballista (https://github.com/ballista-compute/ballista) have different goals, but I don't know enough about either to say what those might be. Does anyone know enough about either to explain the differences?
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Introducing Kamu - World's first global collaborative data pipeline
In your article you mention looking for a faster data engine, have you looked at Ballista https://github.com/ballista-compute/ballista? It’s pretty young but it uses the Apache Arrow memory model and the maintainer did a bunch of work on Apache Spark I believe.
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Rust for DE?
https://github.com/ballista-compute/ballista is also a cool project worth checking out.
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Julia: A Post-Mortem
It’s mostly a personal favourite, but once Ballista [1] gets a bit more developed, I expect we’ll tear out our Java/Spark pipelines and replace them with that.
The ML ecosystem in Rust is a bit underdeveloped at the moment, but work is ticking along on packages like Linfa and SmartCore, so maybe it’ll get there? In my field I’m mostly about it’s potential for correct, high-performance data pipelines that are straightforward to write in reasonable time, and hopefully a model-serving framework: I hate that so many of the current tools require annotating and shipping Python when really model-serving shouldn’t really need any Python code.
[1] https://github.com/ballista-compute/ballista
- Ballista 0.4.0
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Why isn't differential dataflow more popular?
I've looked at this and thought it looked amazing, but also haven't used it for anything. Some thoughts...
Rust is a blessing and curse. I seems like the obvious choice for data pipelines, but everything big currently exists in Java and the small stuff is in Javascript, Python or R. Maybe this will slowly change, but it's a big ship to turn. I'm hopeful that tools like this and Balista [1] will eventually get things moving.
Since the Rust community is relatively small, language bindings would be very helpful. Being able to configure pipelines from Java or Typescript(!) would be great.
Or maybe it's just that this form of computation is too foreign. By the time you need it, the project is so large that it's too late to redesign it to use it. I'm also unclear on how it would handle changing requirements and recomputing new aggregations over old data. Better docs with more convincing examples would be helpful here. The GitHub page showing counting isn't very compelling.
[1] https://github.com/ballista-compute/ballista
- ballista-compute/ballista proof-of-concept distributed compute platform primarily implemented in Rust, using Apache Arrow as the memory model.
superset
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Apache Superset
Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.
https://www.youtube.com/watch?v=RY0SSvSUkMA
https://github.com/apache/superset/discussions/20094
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A modern data stack for startups
I recently ran a little shootout between Superset, Metabase, and Lightdash. All have nontrivial weaknesses but I ended up picking Lightdash.
Superset the best of them at _data visualization_ but I honestly found it almost useless for self-serve _BI_ by business users. This issue on how to do joins in Superset (with stalebot making a mess XD) is everything difficult about Superset for BI in a nutshell. https://github.com/apache/superset/issues/8645
Metabase is pretty great and it's definitely the right choice for a startup looking to get low cost BI set up. It still has a very table centric view, but feels built for _BI_ rather than visualization alone.
Lightdash has significant warts (YAML, pivoting being done in the frontend, no symmetric aggregates) but the Looker inspiration is obvious and it makes it easy to present _groups of tables_ to business users ready to rock. I liked Looker before Google acquired it. My business users are comfortable with star and snowflake schemas (not that they know those words) and it was easy to drop Lightdash on top of our existing data warehouse.
- FLaNK Stack Weekly for 20 Nov 2023
- Hiding tokens retrieved via API from the html source?
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Yandex open sourced it's BI tool DataLens
Or like not being able to delete a user without running some SQL:
https://github.com/apache/superset/issues/13345
Almostl instantly run into this issue setting up a test instance of Superset. And the issue has been around for years.
- Apache Superset Is a Data Visualization and Data Exploration Platform
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Apache Superset: Installing locally is easy using the makefile
Are you interested in trying out Superset, but you're intimidated by the local setup process? Worry not! Superset needs some initial setup to install locally, but I've got a streamlined way to get started - using the makefile! This file contains a set of scripts to simplify the setup process.
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More public SQL-queryable databases?
Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.
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How useful is SQL for managers?
if they don't want to pay for powerbi, can try something like https://superset.apache.org/
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Real-time data analytics with Apache Superset, Redpanda, and RisingWave
In today's fast-paced data-driven world, organizations must analyze data in real-time to make timely and informed decisions. Real-time data analytics enables businesses to gain valuable insights, respond to real-time events, and stay ahead of the competition. Also, the analytics engine must be capable of running analytical queries and returning results in real-time. In this article, we will explore how you can build a real-time data analytics solution using the open-source tools Redpanda a distributed streaming platform, Apache Superset, a data visualization, and a business intelligence platform, combined with RisingWave a streaming database.
What are some alternatives?
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
streamlit - Streamlit — A faster way to build and share data apps.
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
delta-rs - A native Rust library for Delta Lake, with bindings into Python
Apache Hive - Apache Hive
dagster - An orchestration platform for the development, production, and observation of data assets.
lightdash - Self-serve BI to 10x your data team ⚡️
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
roapi - Create full-fledged APIs for slowly moving datasets without writing a single line of code.
django-project-template - The Django project template I use, for installation with django-admin.