Airflow
airbyte
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Airflow | airbyte | |
---|---|---|
169 | 139 | |
34,317 | 13,821 | |
1.8% | 4.0% | |
10.0 | 10.0 | |
7 days ago | 5 days ago | |
Python | Python | |
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.
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://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
airbyte
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Launch HN: Bracket (YC W22) – Two-Way Sync Between Salesforce and Postgres
I'l also give a shout-out to Airbyte (https://airbyte.com/), with which I've had some limited success with integrating Salesforce to a local database. The particular pull for Airbyte is that we can self-host the open source version, rather than pay Fivetran a significant sum to do this for us.
It's an immature tool, so I don't yet know that I can claim we've spent _less_ than Fivetran on the additional engineering and ops time, but it feels like it has potential to do so once stabilized.
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
- All the ways to capture changes in Postgres
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Airbyte API and Terraform Provider – available in open source
When it says "available in open source", is that under the main airbyte repo's licensing [1], hence primarily licensed under the Elastic License v2 and therefore not typically considered open source by many?
Airbyte has previous of advertising their offering as open source while not really being as per the OSD[2]. This has been raised with them previously but without response [3][4]. They've also been extending their use of ELv2, recently moving many of their existing MIT licensed connectors to be ELv2 [5].
[1] https://github.com/airbytehq/airbyte/blob/master/LICENSE
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Need help moving 16gb of mongodb data to tableau
As possible solution, I can suggest Airbyte(https://airbyte.com/). it's more performant than generic python script.
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Connecting data sources to Xata with Airbyte and Zapier integrations
Airbyte, an open-source data integration engine that offers hundreds of connectors with data warehouses and databases, has gained popularity for its seamless integration and data syncing capabilities. Xata's integration with Airbyte offers a streamlined data ingestion process from any Airbyte input source directly into your Xata database.
- Data replication from postgresql to MSSQL
- Testing
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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
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airbyte VS cloudquery - a user suggested alternative
2 projects | 2 Jun 2023
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.
dagster - An orchestration platform for the development, production, and observation of data assets.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
meltano
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.
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
Dask - Parallel computing with task scheduling
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.