MapReduce for the Business Analyst: Simplifying Big Data Analytics for the Business
MapReduce has been praised for its unique ability to map analytic processing to multi-structured big data that’s distributed across large numbers of nodes, then reduce the multiple returns into a single analytic result set. Yet, some forms of MapReduce have been criticized as low-level methods that require arcane hand coding in non-standard languages to access only file-based information.
In this Webinar, we’ll show how recent advances in MapReduce have addressed these criticisms while maintaining its inherent strengths with big data analytics. In particular, MapReduce must integrate with standard structured query language (SQL), if it’s to play well with the great host of tools and applications that assume data access via SQL. Likewise, MapReduce was originally designed for distributed file systems (as in Hadoop), but in larger enterprises it needs to work with relational databases, since they are by far the most common database type in use in BI today. Furthermore, business analysts, BI professionals, and millions of other information workers need to leverage their skills and tools with SQL and relational databases, but expanded by the additional capabilities of MapReduce.
What You Will Learn:
- Definitions of MapReduce and how these are changing
- Business reasons, such as digital marketing optimization, for adopting MapReduce as part of your BI arsenal
- The role MapReduce can play in your BI/DW technology stack
- Real-world use cases of MapReduce for social network analysis, digital marketing optimization, and fraud detection and prevention
- How you can leverage MapReduce code via SQL without writing such code from the ground up
Philip Russom, Ph.D.