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TALLER
Pentaho Data Integration:
Extrayendo, Integrando,
Normalizando y Preparando
mis datos
Proyectos Programa Big Data y Business Intelligence
Alex Rayón
alex.rayon@deusto.es
Noviembre, 2015
Before starting….
Who has
used a
relational
database? Source: http://www.agiledata.org/essays/databaseTesting.html
2
Before starting…. (II)
Who has written
scripts or Java
code to move
data from one
source and load
it to another?
Source: http://www.theguardian.com/teacher-network/2012/jan/10/how-to-teach-code
3
Before starting…. (III)
What did you use?
1.Scripts
2.Custom Java Code
3.ETL
4
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
5
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
6
Pentaho at a glance
Business Intelligence
7
Pentaho at a glance (II)
8
Pentaho at a glance (III)
Business Intelligence & Analytics
Open Core
GPL v2
Apache 2.0
Enterprise and OEM licenses
Java-based
Web front-ends
9
Pentaho at a glance (IV)
The Pentaho Stack
Data Integration / ETL
Big Data / NoSQL
Data Modeling
Reporting
OLAP / Analysis
Data Visualization
Source: http://helicaltech.com/blogs/hire-pentaho-consultants-hire-pentaho-developers/
10
Pentaho at a glance (V)
Modules
Pentaho Data Integration
Kettle
Pentaho Analysis
Mondrian
Pentaho Reporting
Pentaho Dashboards
11
Pentaho at a glance (VI)
Figures
+ 10.000 deployments
+ 185 countries
+ 1.200 customers
Since 2012, in Gartner
Magic Quadrant for BI
Platforms
1 download / 30
12
Pentaho at a glance (VII)
Open Source Leader
13
Pentaho at a glance (VIII)
Single Platform
14
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
15
Academic field
16
Academic field (II)
17
Academic field (III)
18
Academic field (IV)
19
Academic field (V)
20
Academic field (VI)
21
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
22
ETL
Definition and characteristics
An ETL tool is a tool that
Extracts data from various data sources (usually legacy
data)
Transforms data
from → being optimized for transaction
to → being optimized for reporting and analysis
synchronizes the data coming from different databases
data cleanses to remove errors
Loads data into a data warehouse
23
ETL
Why do I need it?
ETL tools save time and money when
developing a data warehouse by removing
the need for hand-coding
It is very difficult for database administrators
to connect between different brands of
databases without using an external tool
In the event that databases are altered or new
databases need to be integrated, a lot of hand-
coded work needs to be completely redone24
ETL
Business Intelligence
ETL is the heart
and soul of
business
intelligence (BI)
ETL processes
bring together
and combine data
from multiple
source systems
into a data
warehouse
Source: http://datawarehouseujap.blogspot.com.es/2010/08/data-warehouse.html
25
ETL
Business Intelligence (II)
According to most
practitioners, ETL
design and
development work
consumes 60 to 80
percent of an entire BI
project
Source: http://www.dwuser.com/news/tag/optimization/
Source: The Data Warehousing Institute. www.dw-institute.com
26
ETL
Processing framework
Source: The Data Warehousing Institute. www.dw-institute.com
27
ETL
Tools
Source: http://www.slideshare.net/jade_22/kettleetltool-090522005630phpapp01
28
ETL
Open Source tools
CloverETL
KETL
Kettle
Talend
29
ETL
CloverETL
Create a basic archive of functions
for mapping and transformations,
allowing companies to move large
amounts of data as quickly and
efficiently as possible
Uses building blocks called
components to create a
transformation graph, which is a
visual depiction of the intended
30
ETL
CloverETL (II)
The graphic presentation simplifies even
complex data transformations, allowing for
drag-and-drop functionality
Limited to approximately 40 different
components to simplify graph creation
Yet you may configure each component to meet
specific needs
It also features extensive debugging capabilities
to ensure all transformation graphs work31
ETL
KETL
Contains a scalable, platform-independent
engine capable of supporting multiple
computers and 64-bit servers
The program also offers performance
monitoring, extensive data source support,
XML compatibility and a scheduling engine for
time-based and event-driven job execution
32
ETL
Kettle
The Pentaho company produced Kettle as an OS
alternative to commercial ETL software
No relation to Kinetic Networks' KETL
Kettle features a drop-and-drag, graphical environment
with progress feedback for all data transactions,
including automatic documentation of executed jobs
XML Input Stream to handle huge XML files without
suffering a loss in performance or a spike in memory
usage
Users can also upgrade the free Kettle version for
33
ETL
Talend
Provides a graphical environment for data integration,
migration and synchronization
Drag and drop graphic components to create the java code
required to execute the desired task, saving time and
effort
Pre-built connectors to enable compatibility with a wide
range of business systems and databases
Users gain real-time access to corporate data, allowing for
the monitoring and debugging of transactions to ensure
smooth data integration
34
ETL
Comparison
The set of criteria that were used for the ETL
tools comparison were divided into seven
categories:
TCO
Risk
Ease of use
Support
Deployment
Speed 35
ETL
Comparison (II)
36
ETL
Comparison (III)
Total Cost of Ownership
The overall cost for a certain
product.
This can mean initial ordering,
licensing servicing, support,
training, consulting, and any
other additional payments that
need to be made before the
product is in full use
Commercial Open Source products
are typically free to use, but the
support, training and consulting
are what companies need to pay37
ETL
Comparison (IV)
Risk
There are always risks with projects, especially big projects.
The risks for projects failing are:
Going over budget
Going over schedule
Not completing the requirements or expectations of the customers
Open Source products have much lower risk then
Commercial ones since they do not restrict the use of their
products by pricey licenses
38
ETL
Comparison (V)
Ease of use
All of the ETL tools, apart from Inaport, have GUI to simplify
the development process
Having a good GUI also reduces the time to train and use
the tools
Pentaho Kettle has an easy to use GUI out of all the tools
Training can also be found online or within the community
39
ETL
Comparison (VI)
Support
Nowadays, all software products have support and all of the
ETL tool providers offer support
Pentaho Kettle – Offers support from US, UK and has a
partner consultant in Hong Kong
Deployment
Pentaho Kettle is a stand-alone java engine that can run on
any machine that can run java. Needs an external
scheduler to run automatically.
It can be deployed on many different machines and used as40
ETL
Comparison (VII)
Speed
The speed of ETL tools depends largely on the data that
needs to be transferred over the network and the
processing power involved in transforming the data.
Pentaho Kettle is faster than Talend, but the Java-connector
slows it down somewhat. Also requires manual tweaking
like Talend. Can be clustered by placed on many machines
to reduce network traffic
41
ETL
Comparison (VIII)
Data Quality
Data Quality is fast becoming the most important feature in
any data integration tool.
Pentaho – has DQ features in its GUI, allows for customized
SQL statements, by using JavaScript and Regular
Expressions. It also has some additional modules after
subscribing.
Monitoring
Pentaho Kettle – has practical monitoring tools and logging
42
ETL
Comparison (IX)
Connectivity
In most cases, ETL tools transfer data from legacy systems
Their connectivity is very important to the usefulness of the
ETL tools.
Kettle can connect to a very wide variety of databases, flat
files, xml files, excel files and web services.
43
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
44
Kettle
Introduction
Project Kettle
Powerful Extraction, Transformation and
Loading (ETL) capabilities using an
innovative, metadata-driven approach
45
Kettle
Introduction (II)
What is Kettle?
Batch data integration
and processing tool
written in Java
Exists to retrieve,
process and load data
PDI is a synonymous
term
Source: http://www.dreamstime.com/stock-photo-very-old-kettle-isolated-image16622230
46
Kettle
Introduction (III)
It uses an innovative meta-driven approach
It has a very easy-to-use GUI
Strong community of 13,500 registered
users
It uses a stand-alone Java engine that
process the tasks for moving data between
many different databases and files
47
Kettle
Introduction (IV)
48
Kettle
Data Integration Platform
Source: http://download.101com.com/tdwi/research_report/2003ETLReport.pdf
49
Kettle
Architecture
Source: Pentaho Corporation
50
Kettle
Most common uses
Datawarehouse and datamart loads
Data Integration
Data cleansing
Data migration
Data export
etc.
51
Kettle
Data Integration
Changing input to desired output
Jobs
Synchronous workflow of job entries
(tasks)
Transformations
Stepwise parallel & asynchronous
processing of a recordstream52
Kettle
Data Integration challenges
Data is everywhere
Data is inconsistent
Records are different in each system
Performance issues
Running queries to summarize data for
stipulated long period takes operating
system for task
Brings the OS on max load53
Kettle
Transformations
String and Date Manipulation
Data Validation / Business Rules
Lookup / Join
Calculation, Statistics
Cryptography
Decisions, Flow control
54
Kettle
What is good for?
Mirroring data from master to slave
Syncing two data sources
Processing data retrieved from multiple
sources and pushed to multiple
destinations
Loading data to RDBMS
Datamart / Datawarehouse
55
Kettle
Alternatives
56
Code
Custom java
Spring batch
Scripts
perl, python, shell,
etc
Possibly + db
loader tool and
Commercial ETL
tools
Datastage
Informatica
Oracle Warehouse
Builder
SQL Server
Integration services
Kettle
Extraction
57
Kettle
Extraction (II)
Source: http://download.101com.com/tdwi/research_report/2003ETLReport.pdf
58
Kettle
Extraction (III)
RDBMS (SQL Server, DB2, Oracle, MySQL, PostgreSQL,
Sybase IQ, etc.)
NoSQL Data: HBase, Cassandra, MongoDB
OLAP (Mondrian, Palo, XML/A)
Web (REST, SOAP, XML, JSON)
Files (CSV, Fixed, Excel, etc.)
ERP (SAP, Salesforce, OpenERP)
Hadoop Data: HDFS, Hive
59
Kettle
Transportation
60
Kettle
Transformation
61
Kettle
Loading
62
Kettle
Environment
63
Kettle
Comparison of Data Integration tools
64
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
65
Big Data
Business Intelligente
Source: http://es.wikipedia.org/wiki/Weka_(aprendizaje_autom%C3%A1tico)
A brief (BI) history….
66
Big Data
WEKA
Project Weka
A comprehensive set of tools for Machine
Learning and Data Mining
Source: http://es.wikipedia.org/wiki/Weka_(aprendizaje_autom%C3%A1tico)
67
Big Data
Among Pentaho’s products
Mondrian
OLAP server written in Java
Kettle
ETL tool
Weka
Machine learning and Data Mining tool
68
Big Data
WEKA platform
WEKA (Waikato Environment for Knowledge Analysis)
Funded by the New Zealand’s Government (for more
than 10 years)
Develop an open-source state-of-the-art workbench
of data mining tools
Explore fielded applications
Develop new fundamental methods
Became part of Pentaho platform in 2006 (PDM -
Pentaho Data Mining)
69
Big Data
Data Mining with WEKA
(One-of-the-many) Definition: Extraction of implicit,
previously unknown, and potentially useful
information from data
Goal: improve marketing, sales, and customer support
operations, risk assessment etc.
Who is likely to remain a loyal customer?
What products should be marketed to which
prospects?
What determines whether a person will respond to
a certain offer? 70
Big Data
Data Mining with WEKA (II)
Central idea: historical data contains
information that will be useful in the
future (patterns → generalizations)
Data Mining employs a set of
algorithms that automatically detect
patterns and regularities in data
71
Big Data
Data Mining with WEKA (III)
A bank’s case as an example
Problem: Prediction (Probability Score) of a Corporate
Customer Delinquency (or default) in the next year
Customer historical data used include:
Customer footings behavior (assets & liabilities)
Customer delinquencies (rates and time data)
Business Sector behavioral data
72
Big Data
Data Mining with WEKA (IV)
Variable selection using the Information Value (IV) criterion
Automatic Binning of continuous data variables was used
(Chi-merge). Manual corrections were made to address
particularities in the data distribution of some variables
(using again IV)
73
Big Data
Data Mining with WEKA (V)
74
Big Data
Data Mining with WEKA (VI)
75
Big Data
Data Mining with WEKA (VII)
Limitations
Traditional algorithms need to have all data
in (main) memory
big datasets are an issue
Solution
Incremental schemes
Stream algorithms
MOA (Massive Online Analysis)
76
Big Data
Be careful with Data Mining
77
Table of Contents
Pentaho at a glance
In the academic field
ETL
Kettle
Big Data
Predictive Analytics
78
Predictive analytics
Unified solution for Big Data Analytics
79
Predictive analytics
Unified solution for Big Data Analytics (II)
Curren release: Pentaho Business Analytics Suite 4.8
Instant and interactive
data discovery for iPad
● Full analytical power
on the go – unique to
Pentaho
● Mobile-optimized user
interface
80
Predictive analytics
Unified solution for Big Data Analytics (III)
Curren release: Pentaho Business Analytics Suite 4.8
Instant and interactive data
discovery and development for
big data
● Broadens big data access to
data analysts
● Removes the need for
separate big data
visualization tools
● Further improves
productivity for big data
developers
81
Predictive analytics
Unified solution for Big Data Analytics (IV)
Pentaho Instaview
● Instaview is simple
○ Created for data analysts
○ Dramatically simplifies ways to
access Hadoop and NoSQL data
stores
● Instaview is instant & interactive
○ Time accelerator – 3 quick steps
from data to analytics
○ Interact with big data sources –
group, sort, aggregate & visualize
● Instaview is big data analytics
○ Marketing analysis for weblog data in
Hadoop
○ Application log analysis for data in
MongoDB
82
Predictive analytics
Comparison
Source: http://cdn.oreillystatic.com/en/assets/1/event/100/Using%20R%20and%20Hadoop%20for%20Statistical%20Computation%20at%20Scale%20Presentation.htm#/2
83
References
http://cdn.oreillystatic.com/en/assets/1/event/100/Big%20Data%20Architectural%20Patterns%20Presentation.pdf
http://blog.pentaho.com/tag/strata/
http://www.slideshare.net/mattcasters/pentaho-data-integration-introduction?from_search=2
http://www.slideshare.net/infoaxon/open-source-bi-7640848
http://download.101com.com/tdwi/research_report/2003ETLReport.pdf
http://www.slideshare.net/jade_22/kettleetltool-090522005630phpapp01
http://www.pentaho.com/Blend-of-the-
Week?mkt_tok=3RkMMJWWfF9wsRonuKvNce%2FhmjTEU5z17%2BQoXaO2hokz2EFye%2BLIHETpodcMTcdgPbjYDBceEJhqyQJxPr3
DJNAN1dt%2BRhDhCA%3D%3D#Analytics
84
Copyright (c) 2015 University of Deusto
This work (but the quoted images, whose rights are reserved to their owners*) is licensed under the Creative
Commons “Attribution-ShareAlike” License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/3.0/
Alex Rayón
Noviembre 2015
TALLER
Pentaho Data Integration:
Extrayendo, Integrando,
Normalizando y Preparando
mis datos
Proyectos Programa Big Data y Business Intelligence
Alex Rayón
alex.rayon@deusto.es
Noviembre, 2015

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