1. F**** around with Big Data and Predictive Analytics
Featuring Kettle, Weka & Hadoop.
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2. Pentahuh?
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3. What’s Pentaho exactly?
CENTRAL ADMINISTRATION, AUDITING & MONITORING
DELIVER
When & Where
Users Need It
STREAMLINE
Information Delivery
VISUALIZE
& Report Information
In Any Style
ACCESS
All Enterprise
Data Sources
ISV & Packaged
Applications
SaaS / Cloud
Applications
EMBEDDED
Web
Mobile
Print
E-Mail
STANDALONE
‣ Advanced &
Predictive Analytics
DATA MINING
‣ Interactive
‣ Operational
‣ Enterprise
REPORTING
‣ Ad hoc Exploration
‣ Multi-Dimensional
ANALYSIS
‣ Interactive Metrics
‣ Rich Visualizations
DASHBOARDS
ERP / CRM /
Enterprise Apps
(e.g. SAP, Oracle)
Hadoop &
NoSQL Data
Unstructured &
semi-structured
(XML, Excel, Files, etc.)
Relational
Data Sources
Cloud
(e.g. Salesforce,
Amazon, Dell)
‣ Data Integration
‣ Graphical
ETL Designer
INTEGRATE,
CLEANSE,
& ENRICH DATA
‣ In Memory
Caching
‣ High
Performance
ANALYTICS
ACCELERATOR
‣ Direct Access
‣ Hadoop
Clustering/
Scheduling
‣ Instant OLAP
Cubes
‣ Enterprise
Scalability

4. We do open source analytics.
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5. Why does Pentaho claim to have
anything to do with Big Data??
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6. Project Kettle
powerful Extraction, Transformation and Loading (ETL) capabilities
using an innovative, metadata-driven approach
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7. Bring the code to the data
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JDBC

8. Bring the code to the data
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JDBCKettle

9. KettleKettle
Bring the code to the data
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Kettle

10. Project Weka
a comprehensive set of tools for machine learning and data mining
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11. 11© 2012, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555

12. 12© 2012, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555

13. Bring Weka to the data
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Kettle
Kettle
JDBCKettle
Kettle

14. Bring Weka to the data
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15. JDBC Services for Kettle
runtime optimization and SQL pushdown
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16. A smart(er) JDBC Layer
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Kettle
Kettle
Kettle
Kettle JDBC
SELECT
CUSTOMER_ID,
SUM(UNIT_SALES)
FROM
SALES_FACT
WHERE
AGE_GROUP_ID > 3
GROUP BY
CUSTOMER_ID;

17. SELECT
CUSTOMER_ID
FROM
SALES_FACT;
SELECT
CUSTOMER_ID,
SUM(UNIT_SALES)
FROM
SALES_FACT
WHERE
AGE_GROUP_ID > 3
GROUP BY
CUSTOMER_ID;
A smart(er) JDBC Layer
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Kettle
Kettle
Kettle
Kettle Kettle JDBC
Kettle
Kettle

18. The gains
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• Job design and
administration becomes
trivial.
• Runs the rich Kettle plugin
environment directly on the
nodes.
• Performs much better than
Hive.
• The JDBC layer is pretty
neat.

19. The caveats
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• True parallel machine
learning algorithms are rare
and hard to design.
• Not an actual
production-ready design.
• Clients might have
caches, which must be
notified by the BD store for
updates.

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20
Demo!

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21
Thank you!
Join the conversation. You can find us on:
blog.pentaho.com
@Pentaho
Facebook.com/Pentaho
Pentaho Business Analytics

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22
Want to learn more?
Learning Linear Models in Hadoop with Weka
http://markahall.blogspot.ca/2013/03/learning-linear-models-in-hadoop-with.html
Introduction to MapReduce with Pentaho Data Integration
http://www.youtube.com/watch?v=KZe1UugxXcs
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