This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "big data," from the usage of NoSQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable big data architecture covers real-world, concrete industry use cases that leverage complex distributed applications, which involve web applications, RESTful APIs, and high throughput of large amounts of data stored in highly scalable NoSQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale, from the usage of NoSQL datastores to the combination of big data distribution. When the data processing is too complex and involves different processing topologies like long-running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary.