Leaders and Innovators

Chapter 5Bringing It All Together

In Chapter 2, we discussed in-database processing and its value of applying analytics where the data reside. In Chapter 3, we explained in-memory analytics and how data could be lifted in-memory for game-changing results. In Chapter 4, we introduced Hadoop and how it can fit into the data management and analytical landscape. Each of the previous chapters combined is like a running a relay, and we tie it all together in this chapter to help you through the stages—crawl, walk, and sprint. Now, let's bring all of the components together (in-database, in-memory, Hadoop) into what I call a collaborative data architecture so that your organization can effectively run a seamless relay to conquering big data management and analytics.

This chapter will cover the following topics:

• How in-database analytics, in-memory analytics, and Hadoop are complementary in a collaborative data architecture
• Use cases and customer success stories
• The benefits of an integrated data management and analytic architecture

BACKGROUND

Historically, many companies have adopted one data management system to store all their data in the form of an enterprise data warehouse or departmental data mart. This single-system architecture may have met the business requirements when it is mainly structured data. The analytics and reporting applications can access the data and analyze and deliver results to management when requested. As time progressed, three things have evolved:

1. Data ...