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PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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meltano
Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)
I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)
I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)