Airflow
Apache Spark
Airflow | Apache Spark | |
---|---|---|
169 | 101 | |
34,570 | 38,414 | |
1.4% | 0.7% | |
10.0 | 10.0 | |
5 days ago | 4 days ago | |
Python | 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.
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
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.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
dagster - An orchestration platform for the development, production, and observation of data assets.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
Scalding - A Scala API for Cascading
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
Dask - Parallel computing with task scheduling
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing