Apache Parquet
ploomber
Apache Parquet | ploomber | |
---|---|---|
4 | 121 | |
2,412 | 3,380 | |
1.6% | 0.5% | |
9.2 | 7.4 | |
7 days ago | 30 days ago | |
Java | Python | |
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.
Apache Parquet
-
How-to-Guide: Contributing to Open Source
Apache Parquet
-
parquet-tools
This go implementation, other than common advantages from go itself (small single executable, support multiple platforms, speed, etc.), has some neat features compare with Java parquet tool and Python one like:
-
Writing Apache Parquet Files
Hi, I've been trying to write parquet files on android for the past couple of days, and have really been struggling to find a solution. My original hypothesis was to just use the java parquet implementation (https://github.com/apache/parquet-mr), but I've since realized that not all java libraries play well with Android. I've gone through essentially dependency hell trying to franken-fit the library into my project, and imported as much as i could before hitting walls such as this one (https://github.com/mockito/mockito/issues/841).
-
pqrs: A parquet-tools replacement in Rust using Apache Arrow
Like many of you probably do, I tend to work with Parquet files a lot. parquet-tools has been my tool of choice for inspecting parquet files, but that has been deprecated recently. So, I created a replacement for it using Rust and Apache Arrow.
ploomber
-
Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
Documentation: https://jupysql.ploomber.io
Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).
We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!
-
Runme – Interactive Runbooks Built with Markdown
For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel
And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber
-
Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
-
Who needs MLflow when you have SQLite?
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
-
New to large SW projects in Python, best practices to organize code
I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.
-
A three-part series on deploying a Data Science Platform on AWS
Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
-
Is Colab still the place to go?
If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!
-
Alternatives to nextflow?
It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2
-
Saving log files
That's what we do for lineage with https://ploomber.io/
What are some alternatives?
Protobuf - Protocol Buffers - Google's data interchange format
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Apache Thrift - Apache Thrift
papermill - 📚 Parameterize, execute, and analyze notebooks
Apache Avro - Apache Avro is a data serialization system.
dagster - An orchestration platform for the development, production, and observation of data assets.
Apache Orc - Apache ORC - the smallest, fastest columnar storage for Hadoop workloads
dvc - 🦉 ML Experiments and Data Management with Git
Big Queue - A big, fast and persistent queue based on memory mapped file.
argo - Workflow Engine for Kubernetes
Wire - gRPC and protocol buffers for Android, Kotlin, Swift and Java.
MLflow - Open source platform for the machine learning lifecycle