CleanerVersion adds a versioning/historizing layer to your relational DB which implements a "Slowly Changing Dimensions Type 2" behavior
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Updated
Feb 7, 2019 - Python
CleanerVersion adds a versioning/historizing layer to your relational DB which implements a "Slowly Changing Dimensions Type 2" behavior
Slowly Changing Dimension type 2 using Hive query language using exclusive join technique with ORC Hive tables, partitioned and clustered hive table performance comparison
Applying data engineering techniques to create data pipeline with Azure Cloud Computing
An ETL Data Pipelines Project that uses AirFlow DAGs to extract accessories and jewelry data from PostgreSQL Schemas and the shoes data from a CSV file, load them in AWS Data Lake, transform them with Python script, and finally load them into SnowFlake Data warehouse using SCD type 2.
Projeto de dbt em um sistema de vendas, nesse caso mostra somente as vendas que foram concluidas, aplicando o SCD na camada Marts
End-to-end sales data warehouse built with Databricks Delta Live Tables. Features automated ETL, change data capture, and medallion architecture. Transforms raw multi-region sales data into analytics-ready dimensional models.
📊 Analyze sales data and forecast future revenue using Python. Gain insights into performance metrics and optimize your business strategies effectively.
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