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Home > Data > Data Processing > Pentaho Data Integration 4 Cookbook
Pentaho Data Integration 4 Cookbook
Pentaho Data Integration 4 Cookbook

Pentaho Data Integration 4 Cookbook: Over 70 recipes to solve ETL problems using Pentaho Kettle

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Pentaho Data Integration 4 Cookbook

Chapter 2. Reading and Writing Files

In this chapter, we will cover:

  • Reading a simple file

  • Reading several files at the same time

  • Reading unstructured files

  • Reading files having one field by row

  • Reading files having some fields occupying two or more rows

  • Writing a simple file

  • Writing an unstructured file

  • Providing the name of a file (for reading or writing) dynamically

  • Using the name of a file (or part of it) as a field

  • Reading an Excel file

  • Getting the value of specific cells in an Excel file

  • Writing an Excel file with several sheets

  • Writing an Excel file with a dynamic number of sheets

Introduction

Files are the most primitive, but also the most used format to store and interchange data. PDI has the ability to read data from all kind of files and different formats. It also allows you to write back to files in different formats as well.

Reading and writing simple files is a very straightforward task. There are several steps under the input and output categories of steps that allow you to do it. You pick the step, configure it quickly, and you are done. However, when the files you have to read or create are not simple—and that happens most of the time—the task of reading or writing can become a tedious exercise if you don't know the tricks. In this chapter, you will learn not only the basics for reading and writing files, but also all the how-tos for dealing with them.

Note

This chapter covers plain files (txt, csv, fixed width) and Excel files. For recipes for reading and writing XML files, refer to Chapter 3, Manipulating XML Structures.

Reading a simple file

In this recipe, you will learn the use of the Text file input step. In the example, you have to read a simple file with a list of authors' information like the following:

"lastname","firstname","country","birthyear"
"Larsson","Stieg","Swedish",1954
"King","Stephen","American",1947
"Hiaasen","Carl ","American",1953
"Handler","Chelsea ","American",1975
"Ingraham","Laura ","American",1964

Getting ready

In order to continue with the exercise, you must have a file named authors.txt similar to the one shown in the introduction section of this recipe.

How to do it...

Carry out the following steps:

  1. Create a new transformation.

  2. Drop a Text file input step to the canvas.

  3. Now, you have to type the name of the file (authors.txt) with its complete path. You do it in the File or directory textbox.

    Tip

    Alternatively, you can select the file by clicking on the Browse button and looking for the file. The textbox will be populated with the complete path of the file.

  4. Click on the Add button. The...

Reading several files at the same time

Sometimes you have several files to read, all with the same structure, but different data. In this recipe, you will see how to read those files in a single step. The example uses a list of files containing names of museums in Italy.

Getting ready

You must have a group of text files in a directory, all with the same format. In this recipe, the names of these files start with museums_italy_ for example, museums_italy_1, museums_italy_2, museums_italy_roma, museums_italy_genova, and so on.

Each file has a list of names of museums, one museum on each line.

How to do it...

Carry out the following steps:

  1. Create a new transformation.

  2. Drop a Text file input step onto the work area.

  3. Under the File or directory tab, type the directory where the files are.

  4. In the Regular Expression textbox, type: museums_italy_.*\.txt

  5. Then click on the Add button. The grid will be populated, as shown in the following screenshot:

    Note

    ${Internal.Transformation.Filename.Directory} is a variable...

Reading unstructured files

The simplest files for reading are those where all rows follow the same pattern: Each row has a fixed number of columns, and all columns have the same kind of data in every row. However, it is common to have files where the information does not have that format. In many occasions, the files have little or no structure. Suppose you have a file with roller coaster descriptions, and the file looks like the following:

JOURNEY TO ATLANTIS
SeaWorld Orlando

Journey to Atlantis is a unique thrill ride since it is ...
Roller Coaster Stats
Drop: 60 feet 
Trains: 8 passenger boats
Train Mfg: Mack 

KRAKEN
SeaWorld Orlando

Named after a legendary sea monster, Kraken is a ...
Kraken begins with a plunge from a height of 15-stories ...
Roller Coaster Stats
Height: 151 feet
Drop: 144 feet
Top Speed: 65 mph 
Length: 4,177 feet
Inversions: 7 
Trains: 3 - 32 passenger
Ride Time: 2 minutes, 2 seconds 

KUMBA
Busch Gardens Tampa
...

As you can see, the preceding file is far from being...

Reading files having one field by row

When you use one of the Kettle steps meant for reading files, Kettle expects the data organized in rows, where the columns are the fields. Suppose that instead of having a file with that structure, your file has one attribute per row as in the following example:

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This file contains book information. In the file, each book is described in three rows: one for the title, one for the published or expected publishing date, and one row for the price.

There is no direct way to tell Kettle how to interpret these rows, but a simple transformation can do the trick.

Getting ready

Create a file containing the preceding text or download the sample file from...

Reading files with some fields occupying two or more rows

When you use one of the Kettle steps devoted for reading files, Kettle expects one entity per row. For example, if you are reading a file with a list of customers, then Kettle expects one customer per row. Suppose that you have a file organized by rows, where the fields are in different columns, but some of the fields span several rows, as in the following example containing data about roller coasters:

Roller Coaster      Speed     Location                   Year
Kingda Ka           128 mph   Six Flags Great Adventure
                              Jackson, New Jersey        2005
Top Thrill Dragster 120 mph   Cedar Point
                              Sandusky, Ohio             2003
Dodonpa             106.8 mph Fuji-Q Highland
                              FujiYoshida-shi            2001
                              Japan
Steel Dragon 2000   95 mph    Nagashima Spa Land
                              Mie                        2000...

Writing a simple file

In this recipe, you will learn the use of the Text file output step for writing text files.

Let's assume that you have a database with outdoor products and you want to export a catalog of products to a text file.

Getting ready

For this recipe, you will need a database with outdoor products with the structure explained in Appendix, Data Structures.

How to do it...

Carry out the following steps:

  1. Create a new transformation.

  2. Drop a Table input step into the canvas. Enter the following SQL statement:

    SELECT innerj.desc_product, categories.category, innerj.price FROM products innerj
INNER JOIN categories
ON innerj.id_category = categories.id_category

  3. From the Output category, add a Text file output step.

  4. In the Filename textbox under the File tab, type or browse to the name of the destination file.

  5. In the Extension textbox, leave the default value txt.

  6. Check the Do not create file at start checkbox. This checkbox prevents the creation of the file when there is no data to write to it...

Writing an unstructured file

A standard file generated with Kettle is a file with several columns, which may vary according to how you configured the Fields tab of the Output step and one row for each row in your dataset, all with the same structure. If you want the file to have a header, the header is automatically created with the names of the fields. What if you want to generate a file somehow different from that?

Suppose that you have a file with a list of topics for a writing examination. When a student has to take the examination, you take that list of topics and generate a sheet like the following:

Student name: Mary Williams
-------------------------------------------------------------
Choose one of the following topics and write a paragraph about it
(write at least 300 words)

1. Should animals be used for medical research?
2. What do you think about the amount of violence on TV?
3. What does your country mean to you?
4. What would happen if there were no televisions?
5. What would...

Providing the name of a file (for reading or writing) dynamically

Sometimes, you don't have the complete name of the file that you intend to read or write in your transformation. That can be because the name of the file depends on a field or on external information. Suppose you receive a text file with information about new books to process. This file is sent to you on a daily basis and the date is part of its name (for example, newBooks_20100927.txt).

Getting ready

In order to follow this recipe, you must have a text file named newBooks_20100927.txt with sample book information such as the following:

"Title","Author","Price","Genre"
"The Da Vinci Code","Dan Brown","25.00","Fiction"
"Breaking Dawn","Stephenie Meyer","21.00","Children"
"Foundation","Isaac Asimov","38.50","Fiction"
"I, Robot","Isaac Asimov","39.99","Fiction"

How to do it...

Carry out the following steps:

  1. Create a new transformation.

  2. Drop a Get System Info step from the Input category into the canvas. Add a new field named today...

Using the name of a file (or part of it) as a field

There are some occasions where you need to include the name of a file as a column in your dataset for further processing. With Kettle, you can do it in a very simple way.

In this example, you have several text files about camping products. Each file belongs to a different category and you know the category from the filename. For example, tents.txt contains tent products. You want to obtain a single dataset with all the products from these files including a field indicating the category of every product.

Getting ready

In order to run this exercise, you need a directory (campingProducts) with text files named kitchen.txt, lights.txt, sleeping_bags.txt, tents.txt, and tools.txt. Each file contains descriptions of the products and their price separated with a |. For example:

Swedish Firesteel - Army Model|$19.97
Mountain House #10 Can Freeze-Dried Food|$53.50
Coleman 70-Quart Xtreme Cooler (Blue)|$59.99
Kelsyus Floating Cooler|$26.99
Lodge LCC3...

Reading an Excel file

Kettle provides the Excel input step, in order to read data from Excel files. In this recipe, you will use this step to read an Excel file regarding museums in Italy. The file has a sheet with one column for the name of the museum and other for the city where it is located. The data starts in the C3 cell (as shown in the screenshot in the next section).

Getting ready

For this example, you need an Excel file named museumsItaly.xls with a museums sheet, as shown in the following screenshot:

You can download a sample file from the book's site.

How to do it...

Carry out the following steps:

  1. Create a new transformation.

  2. Drop an Excel input step from the Input category.

  3. Under the Files tab, browse to the museumsItaly.xls file and click on the Add button. This will cause the name of the file to be moved to the grid below.

  4. Under the Sheet tab, fill in the first row as follows: type museums in the Sheet name column, 2 in the Start row, and 2 in the Start column.

    Note

    The rows and columns...

Getting the value of specific cells in an Excel file

One of the good things about Excel files is that they give you freedom to write anywhere in the sheets, which sometimes is good if you want to prioritize the look and feel. However, that could cause you troubles when it's time to automatically process the data in those files. Suppose that you have an Excel file with values for a couple of variables you'd like to set, as shown in the following screenshot:

In this example, you want to set values for three variables: Year, ProductLine, and Origin. The problem is that you don't know where in the sheet that table is. It can be anywhere, near the upper left corner of the sheet. As you cannot ask Kettle to scan somewhere near the upper next corner, you will learn in this recipe how to get that data with a simple transformation.

Getting ready

Create an Excel file with the preceding table. Feel free to write the values anywhere within the first rows and columns, so long as the labels and values are...

Writing an Excel file with several sheets

Writing an Excel file with Kettle has a lot in common with writing a text file. Except for a couple of settings specific for Excel files, configuring an Excel Output step is quite similar to configuring a Text file output step. One of the differences is that when you write an Excel file, you add a sheet to the file. What if you want to write more than one sheet in the same file?

Suppose you have a datasource containing books and their authors and you want to create an Excel file with two sheets. In the first sheet, you want the authors and in the second, the books' titles. This recipe teaches you how to do this.

Getting ready

In order to run this recipe, you will need a database with books and authors with the structure described in Appendix, Data Structures.

How to do it...

Carry out the following steps, in order to create the sheet with the authors' details:

  1. Create a new transformation.

  2. Drop a Table Input step into the canvas, in order to read the author...

Writing an Excel file with a dynamic number of sheets

When you generate an Excel file, you usually generate it with a single sheet. You can however generate a file with more sheets. With PDI, you can generate an Excel file with several sheets even if you don't know in advance how many sheets you will generate, or the name of those sheets.

In this recipe, you will create such an Excel file. Your file will have book title information separated in different sheets depending of the genre of the books.

Getting ready

You will need a database containing books and authors with the structure described in Appendix, Data Structures.

How to do it...

Carry out the following steps:

  1. Create a new job.

  2. From the File Management category, drop a Delete file job entry into the work area.

  3. In the File name textbox, type the path and name of the Excel file you will create, in order to remove the file if it exists.

  4. Then you have to add two Transformation entries: one for selecting the book's categories (Transf_Categories...

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Key benefits

  • Manipulate your data by exploring, transforming, validating, integrating, and more
  • Work with all kinds of data sources such as databases, plain files, and XML structures among others
  • Use Kettle in integration with other components of the Pentaho Business Intelligence Suite
  • Each recipe is a carefully organized sequence of instructions packed with screenshots, tables, and tips to complete the task as efficiently as possible

Description

Pentaho Data Integration (PDI, also called Kettle), one of the data integration tools leaders, is broadly used for all kind of data manipulation such as migrating data between applications or databases, exporting data from databases to flat files, data cleansing, and much more. Do you need quick solutions to the problems you face while using Kettle? Pentaho Data Integration 4 Cookbook explains Kettle features in detail through clear and practical recipes that you can quickly apply to your solutions. The recipes cover a broad range of topics including processing files, working with databases, understanding XML structures, integrating with Pentaho BI Suite, and more. Pentaho Data Integration 4 Cookbook shows you how to take advantage of all the aspects of Kettle through a set of practical recipes organized to find quick solutions to your needs. The initial chapters explain the details about working with databases, files, and XML structures. Then you will see different ways for searching data, executing and reusing jobs and transformations, and manipulating streams. Further, you will learn all the available options for integrating Kettle with other Pentaho tools. Pentaho Data Integration 4 Cookbook has plenty of recipes with easy step-by-step instructions to accomplish specific tasks. There are examples and code that are ready for adaptation to individual needs.

Who is this book for?

If you are a software developer or anyone involved or interested in developing ETL solutions, or in general, doing any kind of data manipulation, this book is for you. It does not cover PDI basics, SQL basics, or database concepts. You are expected to have a basic understanding of the PDI tool, SQL language, and databases.

What you will learn

  • Configure Kettle to connect to databases, explore them, and perform CRUD operations
  • Read, write, and parse simple and unstructured files
  • Solve common Excel needs such as reading from a particular cell or generating several sheets at a time
  • Read, validate, and generate simple and complex XML structures
  • Manipulate files by copying, deleting, compressing, or transferring to remote servers
  • Look up information from different sources such as databases, web services, or spreadsheets among others
  • Work with data flows performing operations such as joining, merging, or filtering rows
  • Customize the Kettle logs to your needs
  • Embed Java code in your transformations to gain performance and flexibility
  • Execute and reuse transformations and jobs in different ways
  • Integrate Kettle with Pentaho Reporting, Pentaho Dashboards, Community Data Access, and Pentaho BI Platform
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Publication date : Jun 23, 2011
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781849515245
Category :
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Concepts :
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Tools :
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Length: 352 pages
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Language : English
ISBN-13 : 9781849515245
Category :
Data
Concepts :
Data Processing
Tools :
Pentaho

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Table of Contents

9 Chapters
Working with Databases Chevron down icon Chevron up icon
Working with Databases
Introduction
Connecting to a database
Getting data from a database
Getting data from a database by providing parameters
Getting data from a database by running a query built at runtime
Inserting or updating rows in a table
Inserting new rows where a simple primary key has to be generated
Inserting new rows where the primary key has to be generated based on stored values
Deleting data from a table
Creating or altering a database table from PDI (design time)
Creating or altering a database table from PDI (runtime)
Inserting, deleting, or updating a table depending on a field
Changing the database connection at runtime
Loading a parent-child table
Reading and Writing Files Chevron down icon Chevron up icon
Reading and Writing Files
Introduction
Reading a simple file
Reading several files at the same time
Reading unstructured files
Reading files having one field by row
Reading files with some fields occupying two or more rows
Writing a simple file
Writing an unstructured file
Providing the name of a file (for reading or writing) dynamically
Using the name of a file (or part of it) as a field
Reading an Excel file
Getting the value of specific cells in an Excel file
Writing an Excel file with several sheets
Writing an Excel file with a dynamic number of sheets
Manipulating XML Structures Chevron down icon Chevron up icon
Manipulating XML Structures
Introduction
Reading simple XML files
Specifying fields by using XPath notation
Validating well-formed XML files
Validating an XML file against DTD definitions
Validating an XML file against an XSD schema
Generating a simple XML document
Generating complex XML structures
Generating an HTML page using XML and XSL transformations
File Management Chevron down icon Chevron up icon
File Management
Introduction
Copying or moving one or more files
Deleting one or more files
Getting files from a remote server
Putting files on a remote server
Copying or moving a custom list of files
Deleting a custom list of files
Comparing files and folders
Working with ZIP files
Looking for Data Chevron down icon Chevron up icon
Looking for Data
Introduction
Looking for values in a database table
Looking for values in a database (with complex conditions or multiple tables involved)
Looking for values in a database with extreme flexibility
Looking for values in a variety of sources
Looking for values by proximity
Looking for values consuming a web service
Looking for values over an intranet or Internet
Understanding Data Flows Chevron down icon Chevron up icon
Understanding Data Flows
Introduction
Splitting a stream into two or more streams based on a condition
Merging rows of two streams with the same or different structures
Comparing two streams and generating differences
Generating all possible pairs formed from two datasets
Joining two or more streams based on given conditions
Interspersing new rows between existent rows
Executing steps even when your stream is empty
Processing rows differently based on the row number
Executing and Reusing Jobs and Transformations Chevron down icon Chevron up icon
Executing and Reusing Jobs and Transformations
Introduction
Executing a job or a transformation by setting static arguments and parameters
Executing a job or a transformation from a job by setting arguments and parameters dynamically
Executing a job or a transformation whose name is determined at runtime
Executing part of a job once for every row in a dataset
Executing part of a job several times until a condition is true
Creating a process flow
Moving part of a transformation to a subtransformation
Integrating Kettle and the Pentaho Suite Chevron down icon Chevron up icon
Integrating Kettle and the Pentaho Suite
Introduction
Creating a Pentaho report with data coming from PDI
Configuring the Pentaho BI Server for running PDI jobs and transformations
Executing a PDI transformation as part of a Pentaho process
Executing a PDI job from the Pentaho User Console
Generating files from the PUC with PDI and the CDA plugin
Populating a CDF dashboard with data coming from a PDI transformation
Getting the Most Out of Kettle Chevron down icon Chevron up icon
Getting the Most Out of Kettle
Introduction
Sending e-mails with attached files
Generating a custom log file
Programming custom functionality
Generating sample data for testing purposes
Working with Json files
Getting information about transformations and jobs (file-based)
Getting information about transformations and jobs (repository-based)

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Full star icon 4.4 (95)
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Building LLM Powered Applications
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Full star icon 4.2 (22)
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Python Machine Learning By Example
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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(8 Ratings)
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4 star 25%
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Benaglia Nicola Jul 15, 2011
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Pentaho Data Integration (PDI) has reached its 4th version with a lot of new interesting features and capabilities.This versatile tool is a must for all people working with data integration.Transformations and jobs are the target in PDI to realize a task including data reading, writing, manipulations and integrations, doing mathematical or logicaloperations, all this is tipical of a ETL tool (where ETL stands for Extract, Transform and Load).Do you need to move data from an excel file to a database, from a database to a text file?Do you need to extract data from a LDAP server, FTP, mail, log file, compressed file, web service or web site?All this must be done regularly, automatically?Would it be cool to be notified by email if the process failed?Sure you can do it in a lot of ways, but an ETL tool gives you the necessary help.In addition an open source ETL, like Pentaho Data Integration, has behind a strong and skilled community to help you.This book provides a lot of step-by-step examples (called "recipes") with a lot of practical, useful and very smart hints and strategies for developing transformations and jobs.New steps (a step a is basic task, for example reading from a file, sorting , grouping, calculating, ...) are very well described and explainedChapters of this book cover deeply all you need to know to understand the software and be ready to write your own transformations and be quickly productive.I found very useful the space dedicated to:- read and write file: unstructured and structured text files, excel and openoffice spreadsheets- XML files and validation with DTD and XSD Schemas- use fuzzy match step- reuse and flexibility of trasformations (name parameters, variable, mapping)- sending email with log log about the status of the execution- file management: retrieve file from server like FTP, copying, moving, deleting, comparing- integration of Kettle with Pentaho Suite (Pentaho Reporting Engine)The way all these subjects are explained is progressive and gradual. The use of targeted examples makes the reading very pleasant and easy.I suggest this book to you.
Amazon Verified review Amazon
donJaneiro Aug 10, 2011
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an excellent read (the product itself is not that bad either...). It would seem that you could use this book to supplement the one written by Matt Casters and co. If you are a newbie to ETL I suppose that this would be the preferred approach.The book is clearly categorized and the recipes are interrellated. Another refreshing aspect of the book is the fact that the authors are opinionated when necessary. This means that you can learn from their ideas on best practices regarding data-flows. If you have a background in eg. integration services and you know which data-flow pattern you want to implement, but perhaps don't know how to do it in PDI, then this book can be seen as a sort of conversion guide (and then some). The book is also peppered with links to great sites concerning the Pentaho toolset (especially PDI).
Amazon Verified review Amazon
Data Aggregator Jul 07, 2011
Full star icon Full star icon Full star icon Full star icon Full star icon 5
PDI4_Cookbook is worth owning even if you have the 'other' two Kettle books on hand given the depth of that open source product. Easy to follow, it could be used as a first book on PDI once you get through the basic install/sample documents from the Pentaho.com site. It is well organized, up to date with PDI4 features and the recipes are for the most part truly useful in the ETL domain. The authors spend a sizable amount of space on the more obscure but useful Kettle facilities (e.g., sub-transformations or generating sample data, etc.) so this book will pay off in web searches for solutions. Recommended for all PDI developers!
Amazon Verified review Amazon
Nelson Sousa Aug 02, 2011
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book a very good guide for new users, not only to Pentaho Data Integration but to ETL in general. All recipes are based on real world examples that one finds quite often and the explanations on how things work are a valuable resource for those that are giving the first steps on data integration and like to learn by example.But also experienced users can benefit from the recipes, given their wide range and applicability. I've worked with Maria Roldan for the past year and despite being an experienced Pentaho Data Integration user, I keep a copy of the book in my desk and browse through it every now and again because I'm facing a problem I remember seeing solved quickly and elegantly there.
Amazon Verified review Amazon
Bill From Ann Arbor Nov 29, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Incredibly informative and useful. This book probably saved me at least a 100 hours of ramp up time.
Amazon Verified review Amazon

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