Inetsoft Self Learning Data Mashups May 2010
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Transcript of Inetsoft Self Learning Data Mashups May 2010
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