MEET SARASara is the Director of Data Warehousing

at Acme, Inc. Sara’s job is to deliver analyticsto business users within the constraints of a limited IT budget. On one hand,she has thousands of end users in manydepartments doing all types of businessintelligence. On the other hand, she has a budget that is decreasing each year while the data, queries, processing are allincreasing. Enter Big Data. What was amanageable problem just blew up. Thebusiness users can now access a hundredtimes more data, and want more anddifferent BI on new tools and devices.Great for them. Until Sara has to say no.

Sara was on a treadmill at increasingspeed. BI performance expectations fromusers did not let up. Data sizes keptincreasing. To keep up, Sara was forced to use relational database software on-premise, often with proprietary hardware.Sara needed to use high-end, expensivetechnology to keep up. Sara tried to keepall new data coming into her well managedplatform. But to no avail. Many databasessprung up that business users ended uprelying on Sara to manage and support.

Sara’s story is the norm. Manydatabases surrounding the central datawarehouse. High cost technologiesrequired. Data sizes increasing faster thananyone thought possible when setting thebudgets. Annual budgets decreasing.Users expecting the same or betterperformance.

ENTER HADOOP If the size of data is increasing

exponentially, then Sara needs a technologythat costs exponentially less for storage andprocessing of analytics. That is Hadoop.Hadoop’s open-source delivery modelmakes the software inexpensive. Its abilityto run on commodity processors makes thehardware inexpensive. The beauty for Sarais that she can migrate data and analyticworkloads from her existing expensive

databases to Hadoop.But how?

Sara has heard thatsome workloads don’tlend themselves toHadoop. That Hadoopis not for low latencyor real-time queries.Or that due to its‘schema-on-read’feature of not using adata model, that someof her highly tunedqueries may takelonger to run onHadoop. Sara knowsthat there’s a lot ofdata in her datawarehouses that are not being used—butshe doesn’t know which data it is. She’dlike to get that data over to Hadoop assoon as possible. Too many questions.Sara did not have hard data on who wasusing what, when, how often, how expensiveit was, and where to start. Sara had noplan. And no way of making a plan.

ENTER APPFLUENTAppfluent software is designed to give

Sara a Hadoop migration plan. How?Appfluent answers all Sara’s questionsabout what is happening in her analyticenvironments. Appfluent connects toSara’s existing high-end analytic databases(like Oracle, IBM, Netezza and Teradata)and lets her know:• what data has not been used

or is used infrequently• whose queries are the most

‘Hadoop-able’• which data sets are used in batch• which data loads are exceeding

the batch window• which users are the most

expensive

Now Sara has a tool that can answerher questions about what to move into

Hadoop and when, saving the mostmoney, and not impacting end users.

Appfluent’s engine is a set of distributedprocesses that create a nondisruptiveprocess for continuously watching, storing,and analyzing all queries generated byusers and applications against one or more data warehouses. It includes a webapplication that provides out-of-the boxreports and analytics designed to identifywhat is happening with users, analytics,data, tables, columns, views, and costs.Enabling IT to be smarter in areas likesecurity, auditing and information lifecycle management.

MEET SARA…AGAIN Sara is happy. She’s has left the Hadoop

‘sandbox’ and is using production Hadoopside-by-side with her higher end warehouse.Sara has given Acme options: to pocket the capital saved by optimizing the dataplatform, or redeploy the capital on newways to generate analytic insights. ■

APPFLUENTwww.appfluent.com

Moving to Hadoop, Smart

THOUGHT LEADERSHIP SERIES | AUGUST 2013 13Sponsored Content

Shawn Dolley, VP, Corporate Development & Strategy, Appfluent