Book Review: Pentaho Data Integration Cookbook – Second Edition

Pentaho Data Integration Cookbook, Second Edition picks up where the first edition left off, by updating the recipes to the latest edition of PDI and diving into new topics such as working with Big Data and cloud sources, and more.

0674OS_ Pentaho Data Integration Cookbook (2nd edition).jpg

Book review by: David Fombella Pombal (twitter: @pentaho_fan)

Book Title: Pentaho Data Integration Cookbook – Second Edition

Authors: Alex Meadows, Adrián Sergio Pulvirenti, María Carina Roldán

Paperback: 462 pages

I would like to suggest this useful book since it shows us how to take advantage of all the aspects of Kettle through a set of practical recipes organized to find quick solutions to our everyday needs. Although this books covers advanced topics, all recipes are explained step by step in order to help all type of readers.

Target Audience
If you are a software developer, data scientist, or anyone else looking for a tool that will help extract, transform, and load data as well as provide the tools to perform analytics and data cleansing, then this book is for you.

Rating: 9 out of 10

Chapter 1, Working with Databases – 15 recipes

This chapter shows us how to work with relational databases with Kettle.The recipes show us how to create and share database connections, perform typical database functions (select, insert, update, and delete), as well as more advanced tricks such as building and executing queries at ETL runtime. Remember that in Kettle you can connect to MySQL,Oracle, SQL Server, PostgreSQL, db2 …. and nearly all the database engines available.

Chapter 1Inserting new records when PK has to be generated based on previous values transformation

Chapter 2, Reading and Writing Files – 15 recipes

This topic not only shows us how to read and write files (csv, txt, excel …), but also how to work with semi-structured files, and read data from Amazon Web Services S3 instances.

Chapter 2Loading data into an AWS S3 Instance transformation

Chapter 3, Working with Big Data and Cloud Sources – 8 recipes

This third chapter covers how to load and read data from some of the many different NoSQL data sources (MongoDB, HBase, Hadoop …) as well as from I would like to remark the importance of this issue of the book due to the importance of Big Data techniques nowadays.

Chapter 3 Loading data into HBaseLoading data into HBase transformation

Chapter 4, Manipulating XML Structures – 10 recipes

This topic shows us how to read, write, and validate XML  files. Simple and complex XML structures are shown as well as more specialized formats such as RSS feeds. Even an HTML page is generated using XML and XSL transformations. You should read carefully this chapter if you are used to work loading,reading, updating or validating XML files.

Chapter 4Generating an HTML page using XML and XSL sources transformation

Chapter 5, File Management – 9 recipes

This chapter demonstrates how to copy, move, transfer, and encrypt files and directories. Here you will learn how to get data from remote FTP servers, zip files and encrypt files using OpenPGP standard.

Chapter 5Encrypting and decrypting files transformation

Chapter 6, Looking for Data – 8 recipes

This issue shows you how to search for information through various methods via databases, web services, files, and more. This chapter also shows you how to validate data with Kettle’s built-in validation steps. Besides, in last recipe you will learn how to validate data at runtime.

Chapter 6Validating data at runtime transformation

Chapter 7, Understanding and Optimizing Data Flows – 12 recipes

This chapter details how Kettle moves data through jobs and transformations and how to optimize data flows (Processing jobs in parallel, splitting a stream into 2 or more, comparing streams ….).

Chapter 7Run transformations in parallel job

Chapter 8, Executing and Re-using Jobs and Transformations – 9 recipes

This chapter shows us how to launch jobs and transformations in various ways through static or dynamic arguments and parameterization. Object-oriented transformations through subtransformations are also explained.Chapter 8

Moving the reusable part of a transformation to a sub-transformation (Mapping)

Chapter 9, Integrating Kettle and the Pentaho Suite – 6 recipes

This chapter works with some of the other tools in the Pentaho suite (BI Server, Report Designer) to show how combining tools provides even more capabilities and functionality for reporting, dashboards, and more. In this part of the book you will create Pentaho reports from PDI,  execute PDI transformations from BI Server and populating a dashboard with PDI.

Chapter 9Creating a Pentaho report directly from PDI transformation

Chapter 10, Getting the Most Out of Kettle – 9 recipes

This part works with some of the commonly needed features (e-mail and logging) as well as building sample data sets, and using Kettle to read meta information on jobs and transformations via files or Kettle’s database repository.

Chapter 10Programming custom functionality using Java code transformation

Chapter 11, Utilizing Visualization Tools in Kettle – 4 recipes

This chapter explains how to work with plugins and focuses on DataCleaner, AgileBI, and Instaview, an Enterprise feature that allows for fast analysis of data sources.

Chapter 11PDI Marketplace (Here you can install all plugins available)

Chapter 12, Data Analytics – 3 recipes

This part shows us how to work with the various analytical tools built into Kettle, focusing on statistics gathering steps and building datasets for Weka (Pentaho Data Mining tool), you will also read data from a SAS datafile.

Chapter 13Reading data from a sas file transformation

Appendix A, Data Structures, shows the different data structures used throughout the book.

App ASteelwheels database model structure

Appendix B, References, provides a list of books and other resources that will help you
connect with the rest of the Pentaho community and learn more about Kettle and the other
tools that are part of the Pentaho suite.

Book link:


Book Review: Pentaho Data Integration Beginner’s Guide – Second Edition

Hello friends today I am going to review Pentaho Data Integration Beginner’s Guide – Second Edition:

5040OS.jpgFirst of all, I would like to congratulate Maria Carina a great contributor to the community pentaho I met in person in last  Pentaho Community Meeting #PCM13 in  Sintra.

Below you can check the link to purchase the book:

Book review by: David Fombella Pombal (twitter: @pentaho_fan)

Book Title: Pentaho Data Integration Beginner’s Guide – Second Edition

Authors: María Carina Roldán

Paperback: 502 pages

I would like to recommend this book because if you are a noob in Pentaho Data Integration you will gain a lot of knowledge of this cool tool, besides if you are advanced with PDI you can use it as reference guide book.

Target Audience
This book is an excellent starting point for database administrators, data warehouse developers, or anyone who is responsible for ETL and data warehouse projects and needs to load data into them.

Rating: 9 out of 10

Although this book is oriented to PDI 4.4.0 CE version, some new features of PDI 5.0.1 CE are listed in an Appendix of the book

Kettle version

Chapter List

Chapter 1 – Getting Started with Pentaho Data Integration
In this chapter  you learn what Pentaho Data Integration is and installing the software required to start using PDI graphical designer. As an additional task MySQL DBMS server is installed.

Chapter 1Hello world transformation

Chapter 2 – Getting started with Transformations
This chapters introduces us in the basic terminology of PDI and an introduction in handling runtime errors is performed. We will also learn the simplest ways of transforming data.Chapter 2Calculating project duration transformation

Chapter 3 – Manipulating Real-World Data
Here we will learn how to get data from different sorts of files (csv, txt, xml …)  using PDI. Besides we will send data from Kettle to plain files

Chapter 3Creation of a CSV file with random values transformation

Chapter 4 – Filtering, Searching, and Performing Other Useful Operations with Data
Explains how to sort and filter data, grouping data by different criteria and looking up for data outside the main stream of data. Some data cleasing tasks are also performed in this chapter.

Chapter 4Filtering data transformation

Chapter 5 – Controlling the Flow of Data
In this very important for ETL developers chapter we will learn how to control the flow of data. In particular we will cover the following topics: Copying and distributing rows, Splitting streams based on conditions and merging streams of data.

Chapter 5Copying rows transformation

Chapter 6 – Transforming Your Data by Coding
This chapter explains how to insert code in your transformations. Specially you will learn: Inserting and testing Javascript and Java code in your transformations and Distinguishing situations where coding is the best option, from those where there are better alternatives. PDI uses the Rhino javascript engine from Mozilla . For allowing Java programming inside PDI, the tool uses the Janino project libraries. Janino es a supper-small and fast embedded compiler that compiles Java code at runtime . In summary,always remember that code in the Javascript step is interpreted, whereas the code in User Java Class is compiled. This means that a transformation that uses the UDJC step will have much better performance.

Chapter 6Transformation with java code

Chapter 7 – Transforming the Rowset
This chapter will be dedicated to learn how to convert rows to columns (denormalizing) and converting columns to rows (normalizing) . Furthermore, you will be introduced to a very important topic in data warehousing called time dimensions.

Chapter 7Denormalizing rows transformation

Chapter 8 – Working with databases
This is the firs of two chapters fully dedicated to working with databases. We will learn how to connect to a database, preview and get data from a database and insert/update/delete data from a database.

Chapter 8List of some of the many types of databases available to connect to in PDI

Chapter 9 – Performing Advanced Operations with Databases
This chapter explains different advanced operations with databases: Doing simple and complex lookups in a database. Besides an introduction in dimensional modeling and loading dimensions is included.

Chapter 9Database lookup in a transformation

Chapter 10 – Creating Basic Task Flows
So far, we have been working with data (running transformations). A PDI transformation does not run in isolation and usually is embedded in a bigger process. These processes like generating a daily report and transfer the report to a shared repository or updating a data ware house and  sending a notification by email  can be implemented by PDI jobs. In this chapter we will be introduced to jobs, executing tasks upon conditions and working with arguments and named paramenters.

Chapter 10Creating a folder transformation

Chapter 11 – Creating Advanced Transformations and Jobs
This chapter is about learning techniques for creating complex transformations and jobs (create subtransformations, implement process flows, nest jobs, iterate the execution of jobs and transformations …)

Chapter 11Execute transformation included in a job for every input row

Chapter 12 – Developing and Implementing a Simple Datamart
This chapter will cover the following: Introduction to a sales datamart based on a provided database, loading the dimensions and fact table of the sales datamart and automating what has been done.

Appendix A- Working With Repositories
PDI allows us storing our transformations and jobs under 2 different configurations: file-based and database repository. Along this book we have used file-based option, however the database repository is convenient in some situations.

Appendix B- Pan and Kitchen – LaunchingTransformations and Jobs from the Command Line

Despite having used Spoon as the tool for running jobs and transformation you may also run them from a terminal window. Pan is a cmd-line program which lets you launche the transformations designed in Spoon, both the .ktr files and from a repository. The counterpart to Pan is Kitchen, which allows you to run jobs from .kjb files and from a repository.

Appendix C-  Quick Reference – Steps and Job Entries

This appendix summarizes the purpose of  the steps and jobs entries  used in the labs throughout the book.

Appendix D-  Spoon Shortcuts

This very useful appendix includes tables summarizing  the main Spoon shortcuts.

Appendix E-  Introducing PDI 5 features

New PDI 5 features (PDI 5 is currently available now)

Book link:

Remove Duplicate rows using Kettle PDI

Quick tip showing how to use UniqueRows kettle step to remove rows from CSV text file duplicates.

1)sorting the rows using Sort Rows step based on the key field.

2)Use the UniqueRows to remove the duplicates.

Sample Input Data:



Click on input File and fill the gaps as showed in the screen capture.

We are reading Comma separated file and also without header .Please check the highlighted options and select them according to your input.

If you want to trim the incoming string fields make sure you don’t specify length of the string field and if we specify the length the trim function will not work.

Next We need to configure Sort Rows transformation.

You can define temp directory if sort stage requires scratch space and also depending on the system memory you can specify number of rows use the memory.If the number exceeds or memory is not available then it will use the specified scratch space.

Based on your requirement you can capture duplicate rows into an error file by checking  Redirect Duplicate row option.And also warning messaged appeared unique rows stage requires sorted input otherwise you don’t get desired results.

We are using output file step to write the text file output.

After Executing the tranformation here is the output.


As you can see only the Unique Rows are written in the Output file

Community Data Validation ( Thanks to WebDetails & Pedro Alves for this Job)

Why ?

We need a way to do data validation

Use cases

Below are some of the use cases we want to tackle. Emphasized are the ones we think the current spec satisfies

  • Global
    • Is the server running?
    • Is the server running properly?
  • Connectivity
    • Do we have all the access we should? (network / database)
  • Query specific
    • Do we have up to date data?
    • Can we trust the data?
    • How long did the queries take to run?
    • Do we have wrong data? (duplicated users in community)
    • Do we have a big number of ‘unknowns’? (tk=1 in DW)
    • Do we have peaks or valleys in the data? (due to double process or no process)
    • Is the data stalled? (eg: number of twitter followers not updating)
    • Did the data format change
    • We need a way to handle known effects (eg: Christmas dip)
    • We need to correlate independent datasources (eg: comparing AUS with Blocklist evolution)
    • We need to connect the long running queries from CDA to CDV
    • Be able to validate big chunks of reprocessing
    • Do we have clearly wrong lines in resultset? (eg: a line there)
  • Dashboards
    • Are the dashboards rendering properly?
      • Do we have all the components?
      • Any layout change?
      • Any js errors?
    • Are the dashboards performing properly?
    • Can CDF talk with CDV to report client-side errors?
    • Alternatively, can CDA talk with CDV to report query errors?
      • Who caused the error?
  • ETL
    • Did the etl run?
    • Are we processing the expected amount of data?
    • Is the etl taking the expected time to run?
    • Did the etl finish before X am?
    • Test etl against tracer bullets?

Work flow

We expect from this system:

  • A central dashboard that allows us to quickly glimpse the overall status of our system.
    • Did all tests pass?
    • Which one failed?
    • Why?
    • When was the last time the tests ran
  • We need simple ways to define the tests (based on existing CDAs)
  • We need to which queries failed and which queries took long time to run
  • We need push notification system by email
  • We need to make sure it can talk to nagios
  • We need an outside test to check if server is up

Logging types

Every test will result in the following levels:

  • Critical
  • Error
  • Warn
  • Ok

Each specific test will be responsible for converting the output of that test (validation function for cda, tbd for kettle) into that status. The object format is:

 level: "Critical",
 type: "Missing data",
 description: "Whatever string the user defined" 

On each test definition, we need to be able to optionally set a timing threshold for the queries, and that will automatically generate a log with Type ‘Duration’

Test types

There are 4 possible types of tests:

  • CDA based query validation
  • ETL monitoring
  • Datawarehouse validation (a specific set of the cda based query validation)
  • Dashboard validation (we may opt to leave this one out for now as we’ll try to infer the errors from CDA’s 405)

CDA based query


We want to select one or more cda / dataAccessId from our system, define the input parameters and select the type of validation we need.
The shape of the function will be: f( [ query, [params] ], validationFunction )
The generic test will be the implementation of the validation function:

validationFunction = function ( [ {metadata: [] , resultset: [[]]} ] ) :  value

That will be freely mapped to the log outputs

ETL monitoring query

The workflow defined here has to match with the previous section. We’ll build specific CDA queries that will read the kettle log files. From that point on, specific validations will have to be built for this logs
We’ll need, in pentaho, to define which connection refers to the kettle logging tables. Either by defining a special jndi or specifying in the settings.
We’ll need to test for:

  • Time
  • Start /end time
  • Amount of data processed

Datawarehouse schema validation

There are some specific tests we can do on the sanity of a datawarehouse.

  • Coherent amount of data on a daily / hourly basis
  • Test the same as before with specific breakdowns
  • Test for the amount of ‘unknowns’ on dimensions

Invocation and Scheduling

There are 2 ways to call the validations:

  • By url request
  • Scheduled calls

Url will be based on the id / query name (tbd). The schedule calls will be cron based, with the following presets:

  • Every hour
  • Every day
  • Every week
  • Every month
  • Custom cron

User interface

This are the features in the main user interface (this is the ultimate goal, the implementation may be broken into stages):

  • See existing validations
    • Allow to fire a specific validation
    • Get the url of a specific validation / all validations
  • Create / Edit validation
    • Define query name
    • Define queries and parameters
    • Define validation function
    • Chose log alerts (when to throw error / severe / warn / ok)
    • Choose duration thresholds
    • Define error message
    • Define cron
  • Validation status dashboard
  • CDA Query error dashboard (Should this belong to CDA instead?)
    • Query and parameters
    • Error
    • Incidents
  • Duration dashboard to identify slow points in the system
    • Query and parameters
    • Duration
    • Incidents

Technical approach

All the specific information will be stored in solution/cdv/queries/). The files will have the format _queryName.cdv and will internally be a JSON file with the following structure:

  type: "query",
  name: "validationName",
  group: "MyGrouping" 
  validation: [ 
    { cdaFile: "/solution/cda/test.cda", dataAccessId: "1" , parameters: [...] },
    { cdaFile: "/solution/cda/test2.cda", dataAccessId: "2" , parameters: [...] }
  validationType: "custom",
  validationFunction: "function(resultArray,arguments){ return 123 }",
  alerts: {
     /* This functions will be executed from bottom up. As the functions return true, the next one
        will be executed and the last matching level will be thrown.
        The exception to this rule is the optional okAlert(v) function. If this one returns true, no other calls will be made 
     criticalAlert: "function(v){ return v > 10 }",
     errorAlert: undefined,
     warnAlert:  "function(v){ return v > 5 }",
     okAlert: "function(v){return v<3;}",
     alertType: "MissingData",
     alertMessage: "function(level,v){return 'My error message: ' + v)"  /* this can either be a function or a string */
  executionTimeValidation: {
      expected: 5000,
      warnPercentage: 0.30,
      errorPercentage: 0.70,
      errorOnLow: true

  cron: "0 2 * * ? *" 

Preset validations

We won’t need to manually define all kinds of validations. CDV will support a preset that can also be extended by adding the definitions to solution/cdv/validationFunctions/ . The template for one such Javascript file looks like this:

  name: "Existence",
  validationArguments: [
    {name: "testAll", type:"boolean", default: true}

  validationFunction: function(rs, conf) {
    var exists = !!conf.testAll;

    return{return r.length > 0}).reduce(function(prev, curr){
      return conf.testAll ? (curr && prev) : (curr || prev);

  alertArguments: {
    {name: "failOnExistence" type: "boolean", default: true},
    {name: "failureLevel", type: "alarmLevel", default: "ERROR"},
    {name: "failureMessage", type: "String", default: "Failed Existence Test: ${result}"}

  alertMapper: function(result, conf) {
    var success = conf.failOnExistence && result,
        level = success ? "OK", conf.failureLevel,
        message = success ? conf.successMessage : conf.failureMessage; 
    return Alarm(level, message, result);

The wd.cdb.validation API is defined in the Validation Module.
There are 5 objects there that we need to analyze:

  • validationFunction(rs, conf) – This is the validation function that will be executed after the query runs
  • validationArguments – Definition of the arguments that will be used within the validation function
  • alertArguments – Definition of the arguments that will be sent to the alertMapper
  • alertMapper(result, conf) – Mapping between the validation result and the alerts

Preset validations or custom validations

When we define a query, we can chose which validation function to use and pass the parameters that specific validation requires.
Alternatively, we can use a custom validation function. That validation function has the following format, where all we need is to return the alarm level (this is a spec, may change after implementation)

function(rs, conf) {

    var exists ={return r.length > 0}).reduce(function(prev, curr){
      return conf.testAll ? (curr && prev) : (curr || prev);

    return exists ? Alarm.ERROR : Alarm.OK;

CDA integration

We need a tight integration between CDA and CDV to report:

  • Errors in CDA queries
  • Long running CDA queries
  • Queries with obvious errors in the structure (eg: missing lines)

It will obviously need to take into account the fact that CDV may not be installed and can’t have performance impacts in CDA

External interfaces

We can have several external interfaces supported:

  • Email
  • Http
  • Nagios integration
  • Server up check

The last one is a very specific check. All the other integrations will fail if suddenly the server hangs, and we must be notified of that. On http and nagios integration, we’ll be able to get reports on the individual tests and also on the test groups. This will not rerun the tests but get the report on the last status of a test.
On the http case, we can pass a flat to force a test to be rerun.
For nagios, we can have an export of test rules


We’ll be able to define the group rules, mainly for connectivity reasons. So the settings (that later can be converted to an UI), will look like this:

  • Groups
    • Email
      • Threshold
      • Destinations
    • SMS
      • Threshold
      • Destinations