Chapter 1, Enterprise Data Architecture Principles, shows how to store and model data in Hadoop clusters.
Chapter 2, Hadoop Life Cycle Management, covers various data life cycle stages, including when the data is created, shared, maintained, archived, retained, and deleted. It also further details data security tools and patterns.
Chapter 3, Hadoop Design Considerations, covers key data architecture principles and practices. The reader will learn how modern data architects adapt to big data architect use cases.
Chapter 4, Data Movement Techniques, covers different methods to transfer data to and from our Hadoop cluster to utilize its real power.
Chapter 5, Data Modeling in Hadoop, shows how to build enterprise applications using cloud infrastructure.
Chapter 6, Designing Real-Time Streaming Data Pipelines, covers different tools and techniques of designing real-time data analytics.
Chapter 7, Large-Scale Data Processing Frameworks, describes the architecture principles of enterprise data and the importance of governing and securing that data.
Chapter 8, Building an Enterprise Search Platform, gives a detailed architecture design to build search solutions using Elasticsearch.
Chapter 9, Designing Data Visualization Solutions, shows how to deploy your Hadoop cluster using Apache Ambari.
Chapter 10, Developing Applications Using the Cloud, covers different ways to visualize your data and the factors involved in choosing the correct visualization method.
Chapter 11, Production Hadoop Cluster Deployment, covers different data processing solutions to derive value out of our data.
