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:
Teradata Aster, Aster Data Systems
In-Depth Education and Research on All Things Data.
For 20 years, TDWI has been helping data professionals get smarter, so the companies they work for can innovate and grow faster.
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