• Pentaho Data Integration and Pentaho BI Suite  
• Using PDI in real-world scenarios
• Loading data warehouses or data marts
• Integrating data
• Data Cleansing
• Migrating information
• Exporting data
• Integrating PDI along with other Pentaho tools

Demo – Installing PDI

Demo – starting and customizing Spoon

• Setting preferences in the Options window
• Storing transformations and jobs in a repository  

Demo  – creating a hello world transformation  Directing Kettle engine with transformations

• Exploring the Spoon interface
• Designing a transformation
• Running and previewing the transformation

Demo – installing MySQL on Windows

Personalizing the error handling

• Reading several files at once

Demo – reading all your files at a time using a single text file input step  

• regular expressions
• Troubleshooting reading files
• Sending data to files

Demo  – sending the results of matches to a plain file

• Getting system information

Demo – reading and writing matches files with flexibility

• Running transformations from a terminal window

Demo  – running the matches transformation from a terminal window

Demo – getting data from an XML file with information about countries

• PDI transformation files
• Getting data from XML files
• Kettle variables

Filtering/Searching/Performing related Useful Operations  with Data

Sorting data

Demo – sorting information about matches with the Sort rows step

• Calculations on groups of rows

Demo – calculating football match statistics by grouping data Group by Step

• Numeric fields
• Filtering

Demo – counting frequent words by filtering

Demo – refining the counting task by filtering even more

• Filtering rows using the Filter rows step
• Looking up data

Demo – finding out which language people speak

• The Stream lookup step
• Data cleaning

Demo – fixing words before counting them

• Cleansing data with PDI
• Controlling the Flow of Data
• Splitting streams

Demo – browsing new features of PDI by copying a dataset

• Copying rows
• Distributing rows

Demo – assigning tasks by distributing

• Splitting the stream based on conditions

Demo – assigning tasks by filtering priorities with the Filter rows step

• PDI steps – splitting the stream-based on conditions

Demo – assigning tasks by filtering priorities with the Switch/Case step

• Merging streams

Demo – gathering progress and merging it all together

• PDI options for merging streams

Demo – giving priority to Bouchard by using the Append Stream

• Treating invalid data by splitting and merging streams

Demo – treating errors in the estimated time to avoid discarding rows

• Treating rows with invalid data
• Transforming Your Data by Coding
• Doing simple tasks with the JavaScript

Demo – counting frequent words by coding in JavaScript

• Using the JavaScript language in PDI
• Using transformation predefined constants
• Testing the script using the Test script button

• Reading and parsing unstructured files with JavaScript

Demo – changing a list of house descriptions with JavaScript

• Looping over the dataset rows
• Doing simple tasks with the Java Class

Demo – counting frequent words by coding in Java

• Using the Java language in PDI
• Sending rows to the next step
• Data types equivalence
• Transforming the dataset with Java

Demo – splitting the field to rows using Java

• Avoiding coding by using purpose-built steps

• Converting row data to column data by using the Row Demoralizer
• Aggregating data with a Row Demoralizer

Demo – aggregating football matches data with the Row  Demoralizer

• Using Row Demoralizer for aggregating data
• Normalizing data

Demo – enhancing the matches file by normalizing the dataset

• Modifying the dataset with a Row Normalizer
• Summarizing: PDI steps which operate on sets of rows
• Generate a customized time-dimension dataset using Kettle-variables

Demo – creating the time dimension dataset

Getting variables

Demo – parameterizing the start and end date of the time  dimension dataset

• Connecting with Relational Database Management Systems
• Querying a database

Demo – getting data about shipped orders

• Generating data from the database from Table input step
• Generating a new dataset using the SELECT statement  
• Making flexible queries using parameters

Demo – getting orders in a range of dates using parameters

• Adding parameters to your queries
• Making flexible queries by using Kettle variables

Demo – getting orders in a range of dates by using Kettle variables

• Using Kettle variables in your queries
• Sending data to a database

Demo – loading a table with a list of manufacturers

• Inserting or updating data by using other PDI

Demo – inserting new products or updating existing ones

Demo – testing the update of existing products

• Eliminating data from a database

Demo – deleting data about discontinued items

• Exploring the Jigsaw database model
• Doing simple lookups

Demo – using a Database lookup step to create a list of products to buy

Performing complex lookups

Demo – using a Database join step to create a list of suggested  products to buy

• Joining data to the stream-data by using a Database-join
• Introducing dimensional modeling
• Loading dimensions with data

Demo – loading a region dimension with a Combination  lookup/update step

• Describing data with dimensions
• Loading Type I SCD with a Combination lookup/update
• Storing history of changes

Demo – keeping a history of changes in products by using the  Dimension lookup/update

• Keeping an entire history of data with a Type II SCD
• Loading Type II SCDs with the Dimension lookup/update step

Using the Transformation job entry

Receiving arguments and parameters in a job

Demo – generating a hello world file by using arguments and  parameters

Using named parameters in jobs

Running jobs from a terminal window

Demo – executing the hello world job from a terminal window

Demo – calling the hello world transformation with fixed arguments  and parameters

Deciding: use of a command-line argument or a named parameter

Creating Advanced Transformations and Jobs

Re-using part of your transformations

Demo– calculating statistics with the use of a sub-transformation

Creating a job as a process flow

Demo – generating top average scores by copying and getting rows

Use the copy/get rows mechanism to transfer data between transformations  

Demo – generating custom files by executing a transformation for  every input row

Executing for each row

Enhancing your processes with the use of variables

Demo – generating custom messages by setting a variable with the  name of the examination file

Case Study : Developing and Implementing a Sample Datamart

Exploring the sales data mart

Deciding the level of granularity

Loading the dimensions

Demo – loading the dimensions for the sales datamart

Extending the sales datamart model

Loading a fact table with aggregated data

Demo – loading the sales fact table by looking up dimensions

Get the data from the source – SQL queries

Translating the business keys into surrogate keys

Obtaining the surrogate key for Type I SCD

Obtaining the surrogate key for Type II SCD

Obtaining the surrogate key for the Junk dimension

Obtaining the surrogate key for the Time dimension

Getting facts and dimensions together

Demo – loading the fact table using a range of dates obtained  from the command line

Demo – loading the SALES star schema model

Automating the administrative tasks

Demo – automating the loading of the sales data mart