Traditional relational databases work well until the data, or the number of users, grows past what one machine can handle. NoSQL databases were built for that situation. They give up some of the strict rules of SQL in exchange for the ability to scale out across many servers and to store data in shapes that tables handle poorly.
You'll start with what NoSQL means and how it compares to SQL, then look at two popular examples: MongoDB, which stores flexible documents, and Apache Cassandra, a distributed database designed to keep running even when machines fail. Along the way you'll run into the trade-off behind many of these designs, the CAP theorem: when a network splits, a system has to choose between staying consistent and staying available.
There is no single best database, only the right one for the job. Once you've finished this module, move on to Data Warehousing & Modelling.
What NoSQL databases are, how they work, and when they are a good fit, including their advantages and drawbacks.
A side-by-side comparison of SQL and NoSQL databases across the key parameters, and when each one performs better.
An introduction to MongoDB, the open-source document database, and how its data and application layers work together.
The basics of Cassandra, a distributed NoSQL database, including how to model data and how it is used in practice.