Data Warehouse with Pentaho Data Integration

Course ID : PHI-BI-002 Platform : Windows http://www.phi-Integration.com

Data Warehouse with Kettle (Pentaho Data Integration)

Data Warehouse with Kettle (Pentaho Data Integration) I.

Introduction Data analysis as part of business intelligence solutions is a growingly demanded needs. Unfortunately in most transactional systems, rarely data is organized in an effective and efficient aggregates. In those cases, producing an analytical report require an exhaustive and time consuming efforts. Thus, to successfully build a comprehensive BI solution we need to transform our transactional data into analytical friendly format, i.e. Multi Dimensional Modelling. The transformation is usually stored in a data warehouse. Of course, in data warehouse process usually involved a tool called ETL (Extract, Transform and Load). Pentaho Data Integration (Kettle) is one of the ETL tool that is open source and very popular. This course presents an overview of data warehouse, multi dimensional, ETL concept, the extensive use of Pentaho Data Integration to build a powerful data warehouse solution. The practical approach of this course involved many cases we face before, so that participants will get a highly valuable skills from attending the class.

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Data Warehouse with Kettle (Pentaho Data Integration)

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Who Should Attend ? This course is designed for those new to Data Warehouse and ETL or need to understand the basics of Pentaho Data Integration.

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Objectives At the completion of this course, attendee should be able to : Understand the concepts and topics of Data Warehouse, Dimensional Modeling, OLAP and ETL Use Pentaho Data Integration to build simple jobs / transformations Consume data from several data sources Building and Populating Fact and Dimensional tables Troubleshooting techniques Schedule job / transformation

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Course Duration 5 days / 30 hours

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Course Prerequisites Basic understanding one of several popular DBMS (Oracle, SQL Server, MySQL, etc) and of Structured Query Language (SQL) No understanding of other Pentaho tools is needed

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Course Requirements PC or Laptop with minimum of 2GHz CPU, 1 GB of RAM, DVD Drive and 2 GB of available hard disk space. Softwares : ◦ ◦ ◦ ◦ ◦ ◦

Microsoft Windows XP Pro Java Runtime Environment (JRE) Apache Tomcat MySQL 5.0 Database Server Pentaho Data Integration Pentaho Data Analysis (Mondrian)

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Data Warehouse with Kettle (Pentaho Data Integration)

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Course Outline 1. Introduction to Data Warehouse ◦ Data Warehouse. ◦ Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). ◦ Data Warehouse and OLAP. ◦ Delivering Solution with ETL (Extract, Transform, Load) Tool. 2. Installation and Configuration ◦ Java Runtime Environment / Java Development Kit. ◦ Pentaho Data Integration. ◦ XAMPP package (Apache HTTP Server and MySQL). ◦ SQLYog – a GUI based mysql client. ◦ Data and Script samples. 3. Short Introduction to MySQL ◦ MySQL Storage Engines. ◦ Administering MySQL via PHPMyAdmin. ◦ PHI-Minimart sample database installation. 4. Pentaho Data Integration (Kettle) ◦ Introducing Kettle as Pentaho’s ETL Suite ◦ Architecture ◦ Components ▪ Spoon : Graphical UI Designer for job / transformation steps ▪ Pan : Command line batch script for transformation execution ▪ Kitchen : Command line batch script for transformation execution ▪ Carte : Cluster server ◦ Job / Transformation ▪ Step and Hop. ▪ Row and Meta Data. ▪ Relation between job and transformation. 5. Getting Started with Spoon ◦ File system and RDBMS based Repository ◦ Spoon Development Environment ◦ Database Connections ◦ Job and Transformation ▪ Creating job ▪ Creating transformation ▪ Calling transformation from job

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Data Warehouse with Kettle (Pentaho Data Integration)

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Configuring Log

6. Multi Dimensional Modelling ◦ Normalized versus Multi Dimensional Model ◦ Fact and Dimension Tables ◦ Star Schema and Snowflake Schema ◦ Tasks : ▪ Create a Kettle transformation to map PHI-Minimart transactional database sample to dimensional modeling database. ▪ Create logs for each steps. 7. Change Data Capture (CDC) ◦ What is CDC ? ◦ Why CDC is so hard that heavily relied on data source ? ◦ SQL Server 2008’s CDC feature demonstration. ◦ Tasks : ▪ Create a Kettle transformation to map PHI-Minimart transactional database sample to dimensional modeling database ▪ Create logs for each steps 8. Slowly Changing Dimension (SCD) ◦ Slowly Changing Dimension to solve master data historical problems. ◦ SCD Types ◦ Use of Kettle’s step to solve several SCD types with several schema : ▪ Insert / Update ▪ Punch Through 9. Orphan / Late Arrival ◦ What is Late Arrival Dimension? ◦ Typical Situations where Late Arrival occurs. ◦ Best practice of Late Arrival’s handling. 10. OLAP View of Multidimensional Data (Mondrian / JPivot) ◦ Mondrian Installation ◦ Creating scheme based on our fact and dimension tables ◦ View and navigate our Cube using Web Browser 11. Data Staging ◦ What is Data Staging? ◦ Background : Physical I/O versus In-Memory Processing ◦ Task : PHI-Integration.com

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Data Warehouse with Kettle (Pentaho Data Integration)

▪ Create a transformation to join from 3 data sources : text file, Excel spreadsheet, and RDBMS ▪ Create a currency staging table to solve sequential dependence problem. 12. Advance Controls ◦ Environment Variables. ◦ Shared Objects. ◦ Error Handling. ◦ Email job results. ◦ Task : ▪ Create a dynamic tables dump using variable and looping control. ▪ Refining existing transformations to use email alert. 13. Automation ◦ Using Windows Task Scheduler to schedule ETL running job and transformation.

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