slowly-changing-dimensions

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

Spark implementation of Slowly Changing Dimension type 2

ETL process using Pentaho Data Integration (Kettle), for Sales and Purchases Datamarts from Adventureworks, as the final project from the Data Management course from the Big Data & Analytics Masters @ EAE class of 2021

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.

Dive deep into Slowly Changing Dimensions (SCD) ETL in action with this comprehensive tutorial! In this video, we'll explore how to effectively manage changing data using SQL Server Management Studio (SSMS) and SQL Server Integration Services (SSIS).

Complete materials for "Data Warehousing and Modeling" uni course: lectures (Inmon, Kimball, Dimensional Modeling, NoSQL), PostgreSQL labs (Star Schema), exam questions, and practical SQL solutions.

This repository contains materials from all disciplines of the fourth semester

DW://master is an interactive educational platform for mastering data warehousing concepts — from core architecture to advanced slowly changing dimensions (SCD), schema design, IBM Watsonx.data lakehouse technology, and SQL aggregation analytics. Features an AI tutor powered by Claude Sonnet 4 that answers questions about each topic in real-time📊.

PostgreSQL service-desk DB for a residential complex: SCD2 dimension, validation triggers, partitioning, EXPLAIN demos and a seeded Faker data generator

Building my first ETL pipelines using Python to learn more about data engineering. Learning by doing.

This project documentation was developed as a free project, as a data analyst from the fictional company Sell Boardgames would do in his daily work.

This project involves helping a company's HR department analyze the effectiveness of its employee gender diversity program.

Incremental ELT into a star schema: watermarks with lookback, SCD Type 2 history, idempotent fact loading, and history validation

Production-style Slowly Changing Dimension (SCD Type 2) pipeline built with Snowflake, dbt, and AWS S3. Demonstrates secure S3 ingestion, layered bronze/silver/gold modeling, dbt snapshots for historical tracking, and analytics-ready views identifying active vs historical records.

Exploring Data Engineering concepts

Projeto de dbt em um sistema de vendas, nesse caso mostra somente as vendas que foram concluidas, aplicando o SCD na camada Marts

My first data warehousing project in Databricks SQL a layered pipeline (staging → transformation → core) modeled into a star schema, with incremental loading and SCD Type 1 using MERGE INTO.

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.