We also contribute to the development of enhancements and new features added components like Pentaho and STDashboard STPivot.
Look for a very practical approach in order to install during the week across the platform to perform all exercises with your own data, provide documentation, presentations and ability to access code of the developments we have made.
Furthermore, it is intended for the same you can install all components in their latest versions, starting from a more advanced than typical Pentaho demo, and the ability to access database schemas that ye may set or you may have already and present the solution through a Business Intelligence Portal.
Introduction Open Source Business Intelligence Platforms
a. Architecture and features of Pentaho, SpagoBI, BIRT, Mondrian, Kettle, Talend, etc ...
b. Development tools.
b. Functional Overview (jobs, process, control flow)
c. parameterization
• Environment Variables
• Configuration of connections to databases. Shared Connections.
• Parameterization of loads and load types
• Overview
• Types of Steps (Mail, File Managament, etc…)
• Steps description
• Examples of most common and useful Steps
• Overview
• Types of Steps (Input, Output, Tranform, etc…)
• Steps description
• Examples of most common and useful Steps
b. Star schema
c. Multidimensional/OLAP
d. OLAP. Mondrian. Mondrian Architecture
e. JPivot and MDX.
f. Mondrian Schemas. Schema Workbench for designing Mondrian Schemas.
g. Optimization Tips. Pentaho Aggregation Designer.
h. Alternatives to JPivot: STpivot y Saiku
i. Practical Examples
DAY 3
• WAQR, how it works. Web Adhoc Query Reporting
• Pentaho Metadata Editor
• Building a Metadata Model
• Waqr templates
Dashboards
a. Introduction.
• General concepts.
• Best practices.
• Practical design. Storyboard.
c. CDF (Community Dashboard Framework)
• Introduction.
• Examples.
• Introduction.
• Examples.
• Introduction.
• Examples. OLAP Access.
a. What is Data Mining. Introduction and examples
b. Data Mining tool: Weka
• Data Formats
• Data exploration
• Classification algorithms
• Visualization
• KnowledgeFlow
c. Data Mining algorithm with weka:
• Clustering algorithm
• Decision Trees
• linear regression
d. Simple examples on different algorithms and results analysis
