big data analytics platform- beyond traditional enterprise dw

Big Data Analytics Platform

Beyond Traditional Enterprise Data Warehouse

1Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=45

http://www.impetus.com/webinar_registration?event=archived&eid=45


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Outline

What Is A Traditional Enterprise Data Warehouse? What Is Required From A Big Data Warehouse? Building Big Data Analytics Platform How To Re-use Existing Investments? Real-world Examples

Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=45



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The Answers We Seek

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The kind of customer who will

spend most with us next year ?The kind of customer who will

spend most with us next year ?

What kind of products my customers are interested in ?

What kind of products my customers are interested in ?

Customers that we are

likely to lose ?Customers that we are

likely to lose ?How much does my service impact my margin?

How much does my service impact my margin?

In which area should we open our new store next year?

In which area should we open our new store next year?

What is the most effective

Distribution channel?What is the most effective

Distribution channel?




Traditional EDW

4Recorded version available at




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EDW Components

Extraction, Transformation and Loading - ETL Data is extracted from a heterogeneous data sources Transformed to match the data warehouse schema Loaded into the data warehouse database

Analyze and Query - OLAP Tools Active analysis - user queries User guided data analysis OLAP

Automated Analysis - Data Mining Machine learning / NLP Recommendations & forecasting




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Enter Big Data




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The Gap Area- Big Data v/s EDW

Large data volumes Complex unstructured data Deeper insights Storing images, videos The bottom-line - $/TB




Big Data in EDW





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Key Characteristics - Big Data Platform

Highly scalable

Works on massive data sets

Support for multiple data sources

Easy deployment/ seamless integration

Deep analytics

Canned & customized reports as well as valuable BI

Support for real time analytics




Building Big Data Analytics Platform






Commercial

Teradata/ Netezza

Greenplum/ Vertica/ Aster

Informatica

SAS/ Microstrategy/

Business Objects

Pentaho/ Jasper

Open source Hybrid





Commercial

Teradata/ Netezza


Informatica

SAS/ Microstrategy/

Business Objects

Pentaho/ Jasper

Open source

CloverETL/ Kettle/ Talend

Jaspersoft/ Pentaho

Reporting

Hadoop• Apache• Cassandra

Hybrid





Commercial

Teradata/ Netezza


Informatica

SAS/ Microstrategy/

Business Objects

Pentaho/ Jasper

Open source

CloverETL/ Kettle/ Talend

Jaspersoft/ Pentaho

Reporting

Hadoop• Apache• Cassandra

Hybrid

ETL - Open Source and Commercial

Analytics - Open Source or

Commercial

Commercial Hadoop Versions

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Our Key Learnings

Open source yields better results for larger volumes of data

Parallel processing or faster mechanisms can be used for import/export of data

Real time is a myth in big data – needs careful design

Hadoop is the most cost effective option for big data

Reuse of existing EDW investments possible




Impetus Big Data Analytics Platform- iLaDaP





iLadap- Technologies Used

Plug and Play Service Oriented Architecture

Workflow and ETL

Underlying PB Scale Store

BI and Analytics Query Engine

Real Time Analytics

Application Integration/ Development

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Reusing EDW Investments

• Infrastructure

• Code – logic and algorithm

• Traditional data warehouse

• RDBMS engine

• Reporting tools

• ETL tools

• Development and testing strategy




Case Study - 1

The Client Leaders in internet services and media in Europe

Key Challenge Very high volumes of data recorded each month Near real time reporting engine needed How much infrastructure needed?

Impetus Solution Proposed Cloud for POC Usage of Flume for collecting streaming data Usage of Hbase/Hive for analysis

Benefits Realised

• Highly scalable• Near real time analytics

admin

chnage as per the format I shared

Web Analytics

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Case Study - 2

The Client One of the key players in Telecom industry

Key Challenge CDR Data Conversion Customer churn analysis

Impetus Solution Workflow based CDR data conversion Canned reports for CDR data Used Intellicus to generate customer churn analysis reports

Benefits Realised

• Predefined canned reports for customer churn analysis• Better customer management

Case Study - 3

The Client Leading online product retailer

Key Challenge Recommendation engine Cross product customer analysis Provide ‘Big Picture’ across business units

Impetus Solution Proposed iLaDaP based solution Apache Mahout based recommendation engine Clickstream, Server log and OLTP cross analysis

Benefits Realised

• Better product recommendations• True centralized business overview across product and business lines

garora

need 2-3 points stating benefits realized

Summing up…

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Big Data Analytics needs a well-thought of strategy Any single vendor technology may not be sufficient to build a Big

Data Analytics Platform Hybrid solutions are effective due to their flexible cost model Selecting the right tools is the key to build a successful Big Data

Analytics Platform Easy extension of the existing EDW infrastructure possible




Impetus Technologies

We offer innovative product engineering

and technology R&D services





Questions

Please send in your questions

using the chat panel





Thank you

Mail us at [email protected] visit bigdata.impetus.com


mailto:[email protected]

http://bigdata.impetus.com/



big data analytics platform- beyond traditional enterprise dw

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