Data MashupsInnovative Data ManagementInnovative Data Management

The The InetSoft Self-Learning Series

May 2010May 2010

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Data Mashup or Data Warehousing?Data Mashup or Data Warehousing?

The Answer is Often “Both”The Answer is Often “Both”

ETL (Extract, Transform, and Load) is a process that transforms and manipulates data before it is loaded into a Data WarehouseData Warehouse. .

Data Mashup, by contrast, is a process that transforms and manipulates data on , by contrast, is a process that transforms and manipulates data on demand.demand.

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Benefits and Disadvantages

Data WarehousingData Warehousing Data MashupsData Mashups

BenefitsBenefits

Quick RuntimeQuick Runtime

Manages Large Sources Manages Large Sources of Dataof Data

DisadvantagesDisadvantages

Resistance to ChangeResistance to Change

Takes a long time to Takes a long time to Develop / AdaptDevelop / Adapt

BenefitsBenefits

Ad Hoc Changes / Ad Hoc Changes / FlexibilityFlexibility

Quickly Manages Multiple Quickly Manages Multiple Data SourcesData Sources

DisadvantagesDisadvantages

Slow RuntimeSlow Runtime

Less Data Can Be Less Data Can Be ManagedManaged

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Using the Right Tool for the Job Using the Right Tool for the Job

Data mashup as a substitute for ETLData mashup as a substitute for ETL

When the data size does not require does not require ETL or it would take too long to When the data size does not require does not require ETL or it would take too long to create or adaptcreate or adapt..

Data mashup as a precursor to data warehousingData mashup as a precursor to data warehousing

Quickly experiment with different ways of manipulating and combining data, then Quickly experiment with different ways of manipulating and combining data, then implement that logic with ETL into a data warehouse to optimize performance. implement that logic with ETL into a data warehouse to optimize performance.

Data mashup as a complement to data warehousingData mashup as a complement to data warehousing

View external data sets on equal terms with data warehouse results and easily View external data sets on equal terms with data warehouse results and easily manipulate data from both sourcesmanipulate data from both sources..

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Reduce, Reuse, and RecycleReduce, Reuse, and Recycle

InetSoft allows users to do more with fewer resources.InetSoft allows users to do more with fewer resources.

ReduceReduce: The data mashup engine ensures that the majority of processing occurs in a : The data mashup engine ensures that the majority of processing occurs in a database query (database query (SQL). This reduces the amount of post-processing and relieves demands on vital network resources.

ReuseReuse: : Visual exploration can be performed on a cached dataset in order to increase efficiency. This employs the data warehouse as a large cache that can reuse recently processed datasets instead of creating them from scratch.

RecycleRecycle: Data that has been pre-aggregated via ETL can be configured after a mashup : Data that has been pre-aggregated via ETL can be configured after a mashup is defined using a flexible tool called a is defined using a flexible tool called a materialized view.

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Data Mashup SummaryData Mashup Summary

Data Mashup is a powerful data transformation and integration technique that puts Data Mashup is a powerful data transformation and integration technique that puts

control into the hands of the user. Data mashup melds the flexibility of a control into the hands of the user. Data mashup melds the flexibility of a

spreadsheet with enterprise-level security, performance, repeatability, and spreadsheet with enterprise-level security, performance, repeatability, and

collaboration. For more information, please visit collaboration. For more information, please visit www.inetsoft.com