NoSQL schema-optional or non-relational databases support Big Data by providing scalability, high availability, clustering, efficient storage and easy access to huge amounts of semi-structured data.

This introductory course gives you a hands-on overview of the two most popular NoSQL databases—MongoDB and Cassandra—as well as an overview of core concepts and other types of open-source NoSQL databases.

We will cover installation, database shell usage, programmatic access, data modeling, scaling/clustering, categories of NoSQL databases (column-based, document-based, key value-based, or graphic-based), the CAP theorem, and BASE semantics.

You will have the opportunity to gain hands-on experience via homework assignments and a course project that involves setting up, populating (with publicly available datasets), and using a NoSQL of your choice (subject to instructor approval).

Learning Outcomes
At the conclusion of the course, you should be able to

• Describe the need for NoSQL databases and the types of problems NoSQL databases help solve
• Discuss the “foundation concepts” of Mongo DB and Cassandra including semi-structured datasets and wide rows
• Explain how Mongo DB and Cassandra databases support horizontal scaling to support Big Data sharding and clustering
• Demonstrate how Mongo DB and Cassandra support fault tolerance - replication, clustering
• Describe the relationship between cloud computing platforms and the databases we study
• Write code to work with Mongo DB and Cassandra database APIs
• Apply basic administration of Mongo DB and Cassandra

Topics Include

• Why use NoSQL databases?
• Types of NoSQL databases: Key value, document, column, graph
• MongoDB basics
• MongoDB data modeling, GeoData
• MongoDB sharding, replication, clustering
• Cassandra basics
• Cassandra data modeling, real-time data with wide rows
• Cassandra rings, replication
• Other NoSQL databases: Neo4J, DynamoDB
• Accessing NoSQL database APIs: Coding
• Deploying NoSQL databases in the cloud: Amazon AWS
• Using publically available datasets
• CAP theorem, BASE

Skills Needed:

You will need experience using a programming language such as Python, Ruby, or Java and the ability to set up open-source software, databases, tools, and development environments on personal computers.