Big Data: Concepts, Warehousing, and Analytics

A practical guide to big data systems and warehouse design

Big Data: Concepts, Warehousing, and Analytics brings together the main ideas, technologies, and design questions that shape modern big data environments. Rather than treating analytics, storage, and architecture as separate concerns, this book shows how they connect in real systems—especially where data must be collected, organized, modeled, and queried at scale.

The book begins with core big data concepts, including the challenges of the data life cycle, architectural requirements, and the technologies used to support large-scale processing. From there, it moves into the database side of the field, covering NoSQL and NewSQL approaches alongside key-value, column-oriented, document-oriented, and graph databases.

From OLTP to OLAP thinking

A substantial part of the book is devoted to OLAP-oriented environments and the tools used for analytical workloads. Topics include Hive as a SQL-on-Hadoop engine, data storage formats, dimensional and tabular models, and workload optimization with Druid. The result is a broad but structured look at the systems that make large-scale analysis possible.

Big data warehousing, explained with structure

The later chapters focus on the design and implementation of Big Data Warehouses. Here the book addresses logical components, data flows, technological infrastructure, and methods for data modeling. It also includes practical modeling examples that move the discussion beyond theory and into the kinds of decisions practitioners face when building analytical data platforms.

Who will find it useful?

• Students and advanced readers building a foundation in big data and warehousing
• Data engineers and analysts working with warehouse architecture
• Readers comparing OLTP and OLAP approaches in big data environments
• Professionals interested in NoSQL, NewSQL, Hadoop, Hive, and related technologies

Clear, technical, and wide-ranging, this title is a strong fit for readers who want a serious overview of big data warehousing and analytics rather than a quick survey. It is especially useful when you want the conceptual framework and the implementation concerns in one place.