In this section, we dive into the crucial topic of loading data into a cloud data warehouse. At least the theory behind it. Perfect for marketers leveling up their analytics stack, this lesson demystifies ETL (extract, transform, load) processes from start to finish.
We compare old-school ETL order with more modern ELT approaches, highlighting why it’s preferential to extract and load data before transforming. Covering top data pipeline tools, Google import options, and transformation best practices, this video equips you to start consolidating marketing data for analysis.
❓ Specific Questions I answer in the video:
Q: What does ETL stand for in the context of data warehousing?
A: ETL stands for Extract, Transform, and Load. It is a process used in databases and data warehousing where data is extracted from different sources, transformed to fit operational needs, and loaded into a target database or data warehouse.
Q: How has the ETL process changed in recent times?
A: The traditional ETL process, where transformation was done before loading data into the data warehouse, has been evolving. The more recent approach is ELT (Extract, Load, Transform), where data is extracted, loaded into the data warehouse or data lake immediately, and then transformed.
**Q: Why is the ELT process preferred over the traditional ETL process?
A: The ELT process is preferred because it allows data teams to own the transformation logic and apply business-specific rules more flexibly. With ELT, transformations are done after loading data into the data warehouse, making the system less reliant on third-party tools and more scalable. It also makes it easier to revert changes and redo computations if needed.
🔸 Who Should Watch:
This course is indispensable for marketers eager to harness the power of SQL in their strategies. Whether you're in SEO, paid advertising, social media, or management, this lesson is a must-watch to elevate your data-driven marketing skills.
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🗣️ Connect with Me:
Website: flipstream.io/platform/sql-course & michelkant.nl
LinkedIn: www.linkedin.com/in/michelkant/
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