It would take Arjun’s team of five data engineers three days to answer that. By Thursday, the answer was already wrong, because new data had arrived. The spreadsheet they manually stitched together was a house of cards. They called it "Report Frankenstein."
Lena’s first lecture hit him like a bucket of cold water. "You do not have a data problem," she said. "You have a schema problem. You are trying to serve a gourmet meal from a garbage disposal."
In this guide, we have covered the fundamentals of data warehousing, including its architecture, ETL processes, data modeling, and data governance. We have also discussed best practices for building a data warehouse and popular tools and technologies used in the industry. By following this guide, you will be well on your way to designing and implementing a data warehouse that meets the needs of your organization. udemy - data warehouse - the ultimate guide
Data modeling is the process of designing the structure of the data in the data warehouse. It involves:
Designing central metric tables with clear grain definitions. It would take Arjun’s team of five data
The Basement wasn't a physical place. It was a state of mind. It was the tangled web of 17 different operational databases—sales in PostgreSQL, user logins in MongoDB, customer support tickets in a messy MySQL dump, and marketing clickstream data living in a CSV file on an intern's laptop.
Some popular tools and technologies used in data warehousing include: They called it "Report Frankenstein
Designing resilient Extract, Transform, Load workflows.