dagster-example-pipeline
AWS Data Wrangler
| dagster-example-pipeline | AWS Data Wrangler | |
|---|---|---|
| 1 | 9 | |
| 64 | 3,804 | |
| - | 0.7% | |
| 0.0 | 9.4 | |
| about 2 years ago | 3 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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dagster-example-pipeline
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Developing in Dagster
The associated code repo can be found here
AWS Data Wrangler
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Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
I had no problem with awswrangler (https://github.com/aws/aws-sdk-pandas) and it supports reading and writing partitions which was really helpful and a few other optimizations that made it a great tool
- I agree that Arrow Tables are great, but we decided to keep the library focused on the Pandas interface. [wont implement]
- Automate some wrangling and data visualization in Python
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Redshift API vs. other ways to connect?
awslabs has developed their own package for this and given it's for their product, seem likely to maintain it. https://github.com/awslabs/aws-data-wrangler
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Parquet files
AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler
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Reading s3 file data with Python lambda function
you'll find pre-made zips here: https://github.com/awslabs/aws-data-wrangler/releases
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A guide to load (almost) anything into a DataFrame
Don't forget about https://aws-data-wrangler.readthedocs.io/
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Go+: Go designed for data science
Yep, agreed. Go is a great language for AWS Lambda type workflows.
Python isn't as great (Python Lambda Layers built on Macs don't always work). AWS Data Wrangler (https://github.com/awslabs/aws-data-wrangler) provides pre-built layers, which is a work around, but something that's as portable as Go would be the best solution.
- Best way to install pandas and bumpy to AWS Lanbda
What are some alternatives?
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
PyAthena - PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
canarypy - CanaryPy - A light and powerful canary release for Data Pipelines
python-mysql-replication - Pure Python Implementation of MySQL replication protocol build on top of PyMYSQL
portable-data-stack-dagster - A portable Datamart and Business Intelligence suite built with Docker, Dagster, dbt, DuckDB, PostgreSQL and Superset
gonum - Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more
aws-data-wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). [Moved to: https://github.com/aws/aws-sdk-pandas]
zef - Toolkit for graph-relational data across space and time