business_closures_de_pipeline
AWS Data Wrangler
| business_closures_de_pipeline | AWS Data Wrangler | |
|---|---|---|
| 1 | 9 | |
| 14 | 3,804 | |
| - | 0.7% | |
| 0.0 | 9.4 | |
| over 2 years ago | 3 days ago | |
| Python | Python | |
| - | 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.
business_closures_de_pipeline
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Almost done with my DE Project. Mind Taking a look?
Unit tests typically don't test if a service is available because if this were used in CI/CD it could prevent you from merging pipelines just because a service is down temporarily.
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?
terraform-aws-slackbot - Serverless Extensible Slackbot
PyAthena - PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
django-s3file - A lightweight file upload input for Django and Amazon S3
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
terraform-aws-notify-slack - Terraform module to create AWS resources for sending notifications to Slack 🇺🇦
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
python-lambda - A toolkit for developing and deploying serverless Python code in AWS Lambda.
python-mysql-replication - Pure Python Implementation of MySQL replication protocol build on top of PyMYSQL
chalice - Python Serverless Microframework for AWS
gonum - Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more
data-kinesis-s3-recovery - Recovers Kinesis Firehose source records from S3, replaying them into the stream
zef - Toolkit for graph-relational data across space and time