Book Image

Pentaho Analytics for MongoDB Cookbook

By : Joel André Latino, Harris Ward
Book Image

Pentaho Analytics for MongoDB Cookbook

By: Joel André Latino, Harris Ward

Overview of this book

MongoDB is an open source, schemaless NoSQL database system. Pentaho as a famous open source Analysis tool provides high performance, high availability, and easy scalability for large sets of data. The variant features in Pentaho for MongoDB are designed to empower organizations to be more agile and scalable and also enables applications to have better flexibility, faster performance, and lower costs. Whether you are brand new to online learning or a seasoned expert, this book will provide you with the skills you need to create turnkey analytic solutions that deliver insight and drive value for your organization. The book will begin by taking you through Pentaho Data Integration and how it works with MongoDB. You will then be taken through the Kettle Thin JDBC Driver for enabling a Java application to interact with a database. This will be followed by exploration of a MongoDB collection using Pentaho Instant view and creating reports with MongoDB as a datasource using Pentaho Report Designer. The book will then teach you how to explore and visualize your data in Pentaho BI Server using Pentaho Analyzer. You will then learn how to create advanced dashboards with your data. The book concludes by highlighting contributions of the Pentaho Community.

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Pentaho Analytics for MongoDB Cookbook
Joel André Latino | Harris Ward
Dec, 2015 | 218 pages
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Table of Contents (15 chapters)
Pentaho Analytics for MongoDB Cookbook
Pentaho Analytics for MongoDB Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
PDI and MongoDB
PDI and MongoDB
Introduction
Learning basic operations with Pentaho Data Integration
Migrating data from the RDBMS to MongoDB
Loading data from MongoDB to MySQL
Migrating data from files to MongoDB
Exporting MongoDB data using the aggregation framework
MongoDB Map/Reduce using the User Defined Java Class step and MongoDB Java Driver
Working with jobs and filtering MongoDB data using parameters and variables
2
The Thin Kettle JDBC Driver
The Thin Kettle JDBC Driver
Introduction
Using a transformation as a data service
Running the Carte server in a single instance
Running the Pentaho Data Integration server in a single instance
Define a connection using a SQL Client (SQuirreL SQL)
3
Pentaho Instaview
Pentaho Instaview
Introduction
Creating an analysis view
Modifying Instaview transformations
Modifying the Instaview model
Exploring, saving, deleting, and opening analysis reports
4
A MongoDB OLAP Schema
A MongoDB OLAP Schema
Introduction
Creating a date dimension
Creating an Orders cube
Creating the customer and product dimensions
Saving and publishing a Mondrian schema
Creating a Mondrian 4 physical schema
Creating a Mondrian 4 cube
Publishing a Mondrian 4 schema
5
Pentaho Reporting
Pentaho Reporting
Introduction
Copying the MongoDB JDBC library
Connecting to MongoDB using Reporting Wizard
Connecting to MongoDB via PDI
Adding a chart to a report
Adding parameters to a report
Adding a formula to a report
Grouping data in reports
Creating subreports
Creating a report with MongoDB via Java
Publishing a report to the Pentaho server
Running a report in the Pentaho server
6
The Pentaho BI Server
The Pentaho BI Server
Introduction
Importing Foodmart MongoDB sample data
Creating a new analysis view using Pentaho Analyzer
Creating a dashboard using Pentaho Dashboard Designer
7
Pentaho Dashboards
Pentaho Dashboards
Introduction
Copying the MongoDB JDBC library
Importing a sample repository
Using a transformation data source
Using a BeanShell data source
Using Pentaho Analyzer for MongoDB data source
Using a Thin Kettle data source
Defining dashboard layouts
Creating a Dashboard Table component
Creating a Dashboard line chart component
8
Pentaho Community Contributions
Pentaho Community Contributions
Introduction
The PDI MongoDB Delete Step
The PDI MongoDB GridFS Output Step
The PDI MongoDB Map/Reduce Output step
The PDI MongoDB Lookup step
Index
Index

Creating the customer and product dimensions

In this recipe, we will guide you through creating degenerate customer and product dimensions. A degenerate dimension consists of a dimension that doesn't have its own physical table, and the data lives in the fact table. In other words, the customer and product json are sub-documents within the Order documents of the Orders MongoDB collection.

Getting ready

Before you start this recipe, you need to make sure you have the MongoDB databases created in previous chapters. Also, the Data Integration server should be running, and you will need the schema that you created in the previous recipe.

How to do it…

Proceed with the following steps:

  1. Let's add the Customer dimension to the Orders cube:

    1. Right-click on the Orders cube and select Add Dimension.

    2. Expand the new dimension object, and in the default hierarchy created, remove the text for the name field. For the primaryKey field, select the customerNumber option.

    3. Right-click on the hierarchy and select Add...

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