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Database Design and Modeling with Google Cloud

Database Design and Modeling with Google Cloud

By : Sukumaran
4.9 (7)
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Database Design and Modeling with Google Cloud

Database Design and Modeling with Google Cloud

4.9 (7)
By: Sukumaran

Overview of this book

In the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you’ll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You’ll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.
Table of Contents (18 chapters)
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1
Part 1:Database Model: Business and Technical Design Considerations
4
Part 2:Structured Data
8
Part 3:Semi-Structured, Unstructured Data, and NoSQL Design
11
Part 4:DevOps and Databases
13
Part 5:Data to AI

Considerations for a good database design

Why is it important to take a lot of possible scenarios and probabilities into account while making design decisions? Let me tell you a story that I read in a book called How Not to Be Wrong, by Jordan Ellenberg, an interesting read that talks about real-life applications of mathematics.

Like many other things, our story dates back to World War II, when things happened frequently. In this case, the US fighter planes entered combat with loaded machine guns. They didn’t want their fighter planes to get shot down by the enemy fighters. So, a statistical research group (SRG) of extraordinary statisticians was organized to aid the war effort, where equations were developed (and not explosives) to find a solution. Here’s the question that was posed to them: “You don’t want the fighter planes to get shot down, so you need to armor them, but armor makes the plane heavier, and heavier planes are less maneuverable and consume more fuel. Not too much, not too little, but we need to armor the planes somewhere at the optimum level.

The military gave some useful data to the SRG: when American planes came back from a mission, they were covered in bullet holes. However, the damage wasn’t uniformly distributed across the aircraft. There were more bullet holes in the fuselage than in the engines. The bullet-holes-per-square-foot distribution was as follows:

  • 1.11 in the engine
  • 1.73 in the fuselage
  • 1.55 in the fuel system
  • 1.8 on the rest of the plane

The officers saw an opportunity to quickly conclude that the armor concentration needed to be in the area with the most bullet holes per square foot. One specific answer from the smartest statistician in that room contradicted this popular opinion, yet it was very insightful. Abraham Wald stated that the armor doesn’t go where the bullet holes are the most concentrated, but where they aren’t. Wald’s insight was simply to ask: where are the missing holes? The missing holes were the ones that would have been all over the engine casing if the bullet hole distribution had been spread equally all over the plane. Wald was sure that the missing holes were on the planes that had been shot down. The officers simply observed the planes that had returned from the mission but the ones that got hit in the engine had not returned at all!

I am sure by now you have already connected the dots as to why it is important to assess all the “missing holes” before you settle on the choice of database and the database model – it is because you don’t want to armor the wrong part of your business and end up spending more effort in the long run. If you’re wondering whether there is a quick and dirty solution, the short answer is no. However, a good design comes with a set of considerations around business attributes and technical aspects while designing the database model.

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