dagster
chispa
Our great sponsors
| dagster | chispa | |
|---|---|---|
| 46 | 12 | |
| 10,114 | 508 | |
| 4.3% | - | |
| 10.0 | 6.7 | |
| 7 days ago | 4 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
dagster
- Experience with Dagster.io?
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Dagster tutorials
My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.
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The Dagster Master Plan
I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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The Why and How of Dagster User Code Deployment Automation
In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.
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Best Orchestration Tool to run dbt projects?
Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.
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dbt Cloud Alternatives?
Dagster? https://dagster.io
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What's the best thing/library you learned this year ?
One that I haven't seen on here yet: dagster
- Anyone have an example of a project where a handful of the more popular Python tools are used? (E.g. airbyte, airflow, dbt, and pandas)
- Can we take a moment to appreciate how much of dataengineering is open source?
chispa
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Testing spark applications
Unit and e2e tests using a combination of pytest and chispa (https://github.com/MrPowers/chispa). Custom library to create random test data that fits schema with optional hardcoded overrides for relevant fields to test business logic.
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Spark open source community is awesome
here's a little README fix a user pushed to chispa
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Invitation to collaborate on open source PySpark projects
chispa is a library of PySpark testing functions.
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installing pyspark on my m1 mac, getting an env error
The other approach I've used is Poetry, see the chispa project as an example. Poetry is especially nice for projects that you'd like to publish to PyPi because those commands are built-in.
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Spark: local dev environment
- All Spark transformations are tested with pytest + chispa (https://github.com/MrPowers/chispa)
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Pyspark now provides a native Pandas API
Pandas syntax is far inferior to regular PySpark in my opinion. Goes to show how much data analysts value a syntax that they're already familiar with. Pandas syntax makes it harder to reason about queries, abstract DataFrame transformations, etc. I've authored some popular PySpark libraries like quinn and chispa and am not excited to add Pandas syntax support, haha.
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Show dataengineering: beavis, a library for unit testing Pandas/Dask code
I am the author of spark-fast-tests and chispa, libraries for unit testing Scala Spark / PySpark code.
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Tips for building popular open source data engineering projects
Blogging has been the main way I've been able to attract users. Someone searches "testing PySpark", they see this blog, and then they're motivated to try chispa.
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Ask HN: What are some tools / libraries you built yourself?
I built daria (https://github.com/MrPowers/spark-daria) to make it easier to write Spark and spark-fast-tests (https://github.com/MrPowers/spark-fast-tests) to provide a good testing workflow.
quinn (https://github.com/MrPowers/quinn) and chispa (https://github.com/MrPowers/chispa) are the PySpark equivalents.
Built bebe (https://github.com/MrPowers/bebe) to expose the Spark Catalyst expressions that aren't exposed to the Scala / Python APIs.
Also build spark-sbt.g8 to create a Spark project with a single command: https://github.com/MrPowers/spark-sbt.g8
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Open source contributions for a Data Engineer?
I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.
What are some alternatives?
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
spark-fast-tests - Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
spark-daria - Essential Spark extensions and helper methods ✨😲
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
quinn - pyspark methods to enhance developer productivity 📣 👯 🎉
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
lowdefy - The config web stack for business apps - build internal tools, client portals, web apps, admin panels, dashboards, web sites, and CRUD apps with YAML or JSON.
MLflow - Open source platform for the machine learning lifecycle
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
meltano
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.