Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Awesome Aws | 11,773 | 6 months ago | 1 | December 21, 2015 | 65 | other | Python | |||
A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome. | ||||||||||
Aws Sdk Pandas | 3,779 | 65 | 22 days ago | 143 | November 13, 2023 | 34 | apache-2.0 | Python | ||
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). | ||||||||||
Awsls | 735 | 4 | 2 years ago | 24 | February 13, 2022 | 21 | mit | Go | ||
A list command for AWS resources | ||||||||||
Aws Lambda Redshift Loader | 596 | 2 | 9 months ago | 4 | April 20, 2022 | 59 | other | JavaScript | ||
Amazon Redshift Database Loader implemented in AWS Lambda | ||||||||||
Spark Redshift | 514 | 4 | 1 | 4 years ago | 10 | November 01, 2016 | 134 | apache-2.0 | Scala | |
Redshift data source for Apache Spark | ||||||||||
Awsweeper | 381 | 2 years ago | 21 | January 16, 2022 | 25 | mpl-2.0 | Go | |||
A tool for cleaning your AWS account | ||||||||||
Data Engineering Projects | 322 | a year ago | 5 | Jupyter Notebook | ||||||
Personal Data Engineering Projects | ||||||||||
Yuniql | 292 | 1 | 7 | 2 years ago | 25 | May 25, 2022 | 65 | apache-2.0 | C# | |
Free and open source schema versioning and database migration made natively with .NET/6. NEW THIS MAY 2022! v1.3.15 released! | ||||||||||
Beginner_de_project | 276 | a year ago | 1 | mit | HCL | |||||
Beginner data engineering project - batch edition | ||||||||||
Dataall | 196 | 3 months ago | 130 | apache-2.0 | Python | |||||
A modern data marketplace that makes collaboration among diverse users (like business, analysts and engineers) easier, increasing efficiency and agility in data projects on AWS. |