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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The transformed data is then placed into the destination data warehouse or data lake.

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ETL vs. ELT and the Evolution of Data Integration Techniques

Ascend.io

As data became the backbone of most businesses, data integration emerged as one of the most significant challenges. Today, a good part of the job of a data engineer is to move data from one place to another by creating pipelines that can be either ETL vs. ELT. This causes two issues. This is when ELT came in.

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Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

Cleaning Bad data can derail an entire company, and the foundation of bad data is unclean data. Therefore it’s of immense importance that the data that enters a data warehouse needs to be cleaned. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.