In the final chapter of Cory Isaacson’s data modelling series, he explains why it’s necessary to disrupt your beautifully normalized data model for web-scale performance.
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For the next instalment of Cory Isaacson’s Data Modelling series, we’re keeping our ‘Angry Shards’ database normalised while adding complexity.
In the second installment of Cory Isaacson’s crash-course guide to Data Modelling, we learn how to design a data model good enough to last forever.
Your data, whether big or small, needs a home: but with more choice in DBMS engines than ever before, it’s important to consider their strengths and weaknesses.
A look at the pros and cons of the big data processing framework that took the industry by storm.
How to successfully integrate Column Databases into Big Data architecture.
In the first of a three part series, Cory Isaacson gives us a crash-course guide to Data Modelling.
What’s the key to creating a successful database? And what’s standing in your way from achieving it? Cory Isaacson lays it out for us.
Learn how to take on the three enemies of database performance head-on, and stop your data flows congealing into quagmire.
Why data relationships are critical to any successful database design.
MapDB, a data engine grounded in Java, has just reached 1.0 status as an Apache 2.0-licensed project. Cory Isaacson runs through the key features, and underlines what makes it so darn agile.
In the second part of his series, Scaling for Big Data, Cory Isaacson gets into the nuts and bolts of Big Data and attempts to pin down a concrete definition.
Database tech can make or break an app – but getting the fundamentals can be daunting. In this new series, Cory Isaacson breaks it all down in easily digestible chunks.