Pentaho Data Integration 4 Cookbook

Pentaho Data Integration 4 Cookbook

Overview of this book

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.

Pentaho Data Integration 4 Cookbook

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

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

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

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

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

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

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

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

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

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)

Data Structures

Book's data structure

Museum's data structure

Outdoor data structure

Steel Wheels structure

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

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...

Pentaho Data Integration 4 Cookbook

Pentaho Data Integration 4 Cookbook

Overview of this book

Related Content you might be interested in

Current Title:

Pentaho Data Integration 4 Cookbook

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

Getting ready