http://archive.is/wip/FzMso Building is Archive: a Data Mart with Pentaho Data IntegrationA step-by-step tutorial that takes you through the creation of an ETL process to populate a Kimball-style star schemaA step-by-step tutorial that takes you through the creation of an ETL process to populate a Kimball-style star schemaAbout This VideoLearn how to create ETL transformations to populate a star schema in a short span of timeCreate a fully-functional ETL process using a practical approachFollow the step-by-step instructions for creating an ETL based on a fictional company – get your hands dirty and learn fastIn DetailCompanies store a lot of data, but in most cases, it is not available in a format that makes it easily accessible for analysis and reporting tools..
Archive: http://archive.is/wip/FzMso Building a Data Mart with Pentaho Data IntegrationA step-by-step tutorial that takes you through the creation of an ETL process to populate a Kimball-style star schemaA step-by-step tutorial that takes you through the creation of an ETL process to populate a Kimball-style star schemaAbout This VideoLearn how to create ETL transformations to populate a star schema in a short span of timeCreate a fully-functional ETL process using a practical approachFollow the step-by-step instructions for creating an ETL based on a fictional company – get your hands dirty and learn fastIn DetailCompanies store a lot of data, but in most cases, it is not available in a format that makes it easily accessible for analysis and reporting tools.
Ralph Kimball is realized this a long time ago, so he paved the way for the star schema.Building a Data Mart with Pentaho Data Integration walks you through the creation of an ETL process to create a data mart based on a fictional company..
Ralph Kimball realized this a long time ago, so he paved the way for the star schema.Building a Data Mart with Pentaho Data Integration walks you through the creation of an ETL process to create a data mart based on a fictional company.
This course is will show you how to source the raw data and prepare it for the star schema step-by-step..
This course will show you how to source the raw data and prepare it for the star schema step-by-step.
The practical approach of this course is will get you up and running quickly, and will explain the key concepts in an easy to understand manner.Building a Data Mart with Pentaho Data Integration teaches you how to source raw data with Pentaho Kettle and transform it so that the output can be a Kimball-style star schema..
The practical approach of this course will get you up and running quickly, and will explain the key concepts in an easy to understand manner.Building a Data Mart with Pentaho Data Integration teaches you how to source raw data with Pentaho Kettle and transform it so that the output can be a Kimball-style star schema.
the raw data is After sourcing with our ETL process, you will quality check the data using an agile approach..
After sourcing the raw data with our ETL process, you will quality check the data using an agile approach.
you is Next, will learn how to load slowly changing dimensions and the fact table..
Next, you will learn how to load slowly changing dimensions and the fact table.
The star schema is will reside in the column-oriented database, so you will learn about bulk-loading the data whenever possible..
The star schema will reside in the column-oriented database, so you will learn about bulk-loading the data whenever possible.
You is will also learn how to create an OLAP schema and analyze the output of your ETL process easily.By covering all the essential topics in a hands-down approach, you will be in the position of creating your own ETL processes within a short span of time.Course CurriculumGetting StartedThe Second-hand Lens Store (6:49)The Derived Star Schema (4:29)Setting up Our Development Environment (7:07)Agile BI – Creating ETLs to Prepare Joined Data SetImporting Raw Data (3:22)Exporting Data Using the Standard Table Output (4:33)Exporting Data Using the Dedicated Bulk Loading (4:32)Agile BI – Building OLAP Schema, Analyzing Data, and Implementing Required ETL ImprovementsCreating a Pentaho Analysis Model (3:25)Analyzing Data Using Pentaho Analyzer (3:49)Improving Your ETL for Better Data Quality (4:15)Slowly Changing DimensionsCreating a Slowly Changing Dimension of Type 1 Using Insert/Update (6:47)Creating a Slowly Changing Dimension of Type 1 Using Dimension Lookup Update (4:58)Creating a Slowly Changing Dimension Type 2 (5:18)Populating Data DimensionDefining Start and End Date Parameters (5:17)Auto-generating Daily Rows for a Given Period (4:26)Auto-generating Year, Month, and Day (6:27)Creating the Fact TransformationSourcing Raw Data for Fact Table (3:52)Lookup Slowly Changing Dimension of the Type 1 Key (4:28)Lookup Slowly Changing Dimension of the Type 2 key (6:08)OrchestrationLoading Dimensions in Parallel (6:20)Creating Master Jobs (4:09)ID-based Change Data CaptureImplementing Change Data Capture (CDC) (4:58)Creating a CDC Job Flow (4:48)Final Touches: Logging and SchedulingSetting up a Dedicated DB Schema (1:22)Setting up Built-in Logging (4:22)Scheduling on the Command Line (5:30).
You will also learn how to create an OLAP schema and analyze the output of your ETL process easily.By covering all the essential topics in a hands-down approach, you will be in the position of creating your own ETL processes within a short span of time.Course CurriculumGetting StartedThe Second-hand Lens Store (6:49)The Derived Star Schema (4:29)Setting up Our Development Environment (7:07)Agile BI – Creating ETLs to Prepare Joined Data SetImporting Raw Data (3:22)Exporting Data Using the Standard Table Output (4:33)Exporting Data Using the Dedicated Bulk Loading (4:32)Agile BI – Building OLAP Schema, Analyzing Data, and Implementing Required ETL ImprovementsCreating a Pentaho Analysis Model (3:25)Analyzing Data Using Pentaho Analyzer (3:49)Improving Your ETL for Better Data Quality (4:15)Slowly Changing DimensionsCreating a Slowly Changing Dimension of Type 1 Using Insert/Update (6:47)Creating a Slowly Changing Dimension of Type 1 Using Dimension Lookup Update (4:58)Creating a Slowly Changing Dimension Type 2 (5:18)Populating Data DimensionDefining Start and End Date Parameters (5:17)Auto-generating Daily Rows for a Given Period (4:26)Auto-generating Year, Month, and Day (6:27)Creating the Fact TransformationSourcing Raw Data for Fact Table (3:52)Lookup Slowly Changing Dimension of the Type 1 Key (4:28)Lookup Slowly Changing Dimension of the Type 2 key (6:08)OrchestrationLoading Dimensions in Parallel (6:20)Creating Master Jobs (4:09)ID-based Change Data CaptureImplementing Change Data Capture (CDC) (4:58)Creating a CDC Job Flow (4:48)Final Touches: Logging and SchedulingSetting up a Dedicated DB Schema (1:22)Setting up Built-in Logging (4:22)Scheduling on the Command Line (5:30)