analytics
Airflow
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.
analytics
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I'm not getting it...what's the point of DBT?
Take a look at gitlab's dbt project: https://gitlab.com/gitlab-data/analytics/-/blob/master/transform/snowflake-dbt/models/common/schema.yml
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How would you structure a repo with 10+ ETL pipelines and shared code?
A good reference is the Gitlab data team repo. https://gitlab.com/gitlab-data/analytics
- What are your favourite GitHub repos that shows how data engineering should be done?
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Are there any open corporate Data Team repositories / projects besides GitLab?
For example, their Data Team have a public repository, with a bunch of information on how they organize DAGs, machine learning projects, system configuration, etc.
- Kimball Dim Modelling Code Examples
- Can someone help me, an absolute newbie, understand the usage and benefit of dbt with practical example ?
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Is jinja templating right for DBT?
So I've run through the DBT tutorial stuff and looked over some fairly complex uses of it i.e. GitLab Data and I was wondering if anyone has any opinions or insights into the use of jinja templating in the sql?
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Where can I find free data engineering ( big data) projects online?
Gitlab has their DBT repo open source and is very useful for seeing how to structure a project at scale. https://gitlab.com/gitlab-data/analytics/-/tree/master/transform/snowflake-dbt
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Gitlab's Data Team Platform (in depth look at their stack)
Currently the team is working hard on this: https://gitlab.com/gitlab-data/analytics/-/issues/9508
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Can someone explain the big deal with dbt?
GitLab's dbt project is an excellent example of a mature project at scale. They also have a comprehensive guide to their methodology.
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
What are some alternatives?
dbt-synapse - dbt adapter for Azure Synapse Dedicated SQL Pools
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.
castled - Castled is an open source reverse ETL solution that helps you to periodically sync the data in your db/warehouse into sales, marketing, support or custom apps without any help from engineering teams
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
datahub - The Metadata Platform for your Data Stack
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.
AdvancedSQLPuzzles - Welcome to my GitHub repository. I hope you enjoy solving these puzzles as much as I have enjoyed creating them.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
lightdash - Self-serve BI to 10x your data team ⚡️
Dask - Parallel computing with task scheduling