Pinned Repositories
Cloud-Data-Warehouses
Will build an ETL pipeline that extracts data from S3, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team.
Data-Lakes-with-Spark
Will build a data lake and an ETL pipeline in Spark that loads data from S3, processes the data into analytics tables using Spark cluster hosted on AWS, and loads them back into S3 as Parquet format.
Data-Modeling-with-Cassandra
Will model event data to create a non-relational database and ETL pipeline for a music streaming app. They will define queries and tables for a database built using Apache Cassandra.
Data-Modeling-with-Postgres
Will model user activity data to create a database and ETL pipeline in Postgres for a music streaming app. They will define Fact and Dimension tables and insert data into new tables.
Data-Pipelines-with-Airflow
Continue to work on the music streaming company’s data infrastructure by creating and automating a set of data pipelines with Airflow, monitoring and debugging production pipelines
version-control
for version control test purposes
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