slides pentaho-hadoop-weka

F**** around with Big Data and Predictive Analytics

Featuring Kettle, Weka & Hadoop.

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

Pentahuh?

2© 2012, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-7555

What’s Pentaho exactly?

CENTRAL ADMINISTRATION, AUDITING & MONITORING

DELIVER When & WhereUsers Need It

STREAMLINE Information Delivery

VISUALIZE& Report Information In Any Style

ACCESSAll 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

We do open source analytics.


Why does Pentaho claim to have anything to do with Big Data??


Project Kettle powerful Extraction, Transformation and Loading (ETL) capabilities

using an innovative, metadata-driven approach


Bring the code to the data


JDBC



JDBCKettle

KettleKettle



Kettle

Project Weka a comprehensive set of tools for machine learning and data mining


Bring Weka to the data


Kettle

Kettle

JDBCKettle

Kettle

Bring Weka to the data


JDBC Services for Kettleruntime optimization and SQL pushdown


A smart(er) JDBC Layer


Kettle

Kettle

Kettle

Kettle JDBC

SELECT CUSTOMER_ID, SUM(UNIT_SALES)

FROM SALES_FACT

WHERE AGE_GROUP_ID > 3

GROUP BY CUSTOMER_ID;

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


Kettle

Kettle

Kettle

Kettle Kettle JDBC

Kettle

Kettle

The gains


• 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.

The caveats


• 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.


Demo!


Thank you!

Join the conversation. You can find us on:

blog.pentaho.com

@Pentaho

Facebook.com/Pentaho

Pentaho Business Analytics


Want to learn more?

Learning Linear Models in Hadoop with Wekahttp://markahall.blogspot.ca/2013/03/learning-linear-models-in-hadoop-with.html

Introduction to MapReduce with Pentaho Data Integrationhttp://www.youtube.com/watch?v=KZe1UugxXcs`

slides pentaho-hadoop-weka

Technology