Big Data Imperatives

Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.

This book addresses the following big data characteristics:

• Very large, distributed aggregations of loosely structured data - often incomplete and inaccessible
• Petabytes/Exabytes of data
• Millions/billions of people providing/contributing to the context behind the data
• Flat schema's with few complex interrelationships
• Involves time-stamped events
• Made up of incomplete data
• Includes connections between data elements that must be probabilistically inferred

Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible.

This book can also be used as a handbook for practitioners; helping them on methodology, technical architecture, analytics techniques and best practices. At the same time, this bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

1. The New Information Management Paradigm
2. Big Data's Implication for Businesses
3. Big Data Implications for Information Management
4. Defining Big Data Architecture Characteristics
5. Co-Existent Architectures
6. Data Quality for Big Data
7. Data Security and Privacy Considerations for Big Data
8. Big Data and Analytics
9. Big Data Implications for Practitioners