PluralSight Advanced Integration Services
This course explains how to apply Integration Services features to build packages that support the extract, transform and load operations of a data warehouse. It covers design patterns for staging data and for loading data into fact and dimension tables. In addtion, this course describes how to enhance ETL packages with data cleansing techniques and offers insight into the buffer architecture of the data flow engine to hep package developers get the best performance from packages. This course was written for SQL Server 2012 Integration Services, but most principles apply to SQL Server 2005 and later.
 Data Warehousing Packages, Part I | 00:57:46 |
| Introduction | 01:17 |
| Introduction to Data Warehousing | 04:32 |
| Dimensional Modeling | 05:41 |
| Data Profiling | 02:05 |
| Demo: Data Profiling | 08:22 |
| ETL Design Patterns | 02:58 |
| Extract Package | 03:57 |
| Demo: Extracting to Staging Tables | 12:04 |
| Load Patterns | 01:50 |
| Fact Extract for Ongoing Load | 03:16 |
| Demo:Extracting Records since Last Load | 07:58 |
| Summary | 03:46 |
 Data Warehousing Packages, Part II | 00:49:34 |
| Introduction | 00:51 |
| Slowly Changing Dimensions | 02:52 |
| Dimension Load Patterns: Type 0, Type 1, and Type 2 | 04:42 |
| Dimension Load Package | 00:54 |
| Demo: Transforming and Loading Data for Type 0 | 07:25 |
| Demo: Loading Data with The Slowly Changing Dimension Wizard | 13:34 |
| Fact Table Design Patterns | 01:39 |
| Demo: Performing ETL for A Fact Table | 06:33 |
| Demo: Selecting Data for Ongoing Fact Table Load | 01:41 |
| Analysis Services Objects | 00:52 |
| Demo: Using The Analysis Services Task | 04:22 |
| Demo: Orchestrating The ETL Process | 02:38 |
| Summary | 01:31 |
 Data Cleansing | 01:13:17 |
| Introduction | 00:46 |
| Data Flow and Data Quality | 04:20 |
| Column Problems: Missing Data Default | 00:48 |
| Demo: Derived Column | 04:27 |
| Columnn Problems: Derive Missing Data | 01:00 |
| Demo: Lookup Transformation | 05:57 |
| Column Problems:Translation | 01:53 |
| Demo: Lookup Transformation and Cache Transformation | 06:29 |
| Column Problems: Data Type | 01:32 |
| Demo: Data Conversion Transformation | 05:08 |
| Column Problems: Truncation | 02:04 |
| Demo: Flagging Unfixable Data Flow Errors | 03:04 |
| Record Problems: Missing Dimension Data | 03:04 |
| Demo: Inferred Members | 11:24 |
| Record Problems: Lookup Failures | 01:41 |
| Demo: Fuzzy Lookup Transformation | 06:46 |
| Record Problems: Duplicate Data | 01:23 |
| Record Problems: Fuzzy Grouping and Data | 03:56 |
| Business Rule Problems: Out of Range Values | 01:02 |
| Data Quality Services | 02:32 |
| Summary | 04:01 |
 Package Performance | 00:38:45 |
| Introduction | 01:27 |
| Pipeline Buffer Architecture | 04:20 |
| Non-Blocking Streaming Transformations | 01:38 |
| Demo: Executing Non-Blocking Streaming Transformations | 01:36 |
| Non-Blocking Row-Based Transformations | 01:17 |
| Demo: Executing Non-Blocking Row-Based Transformations | 02:02 |
| Partially Blocking Transformations | 01:05 |
| Demo: Executing Partially BlockingTransformations | 01:20 |
| Blocking Transformations | 00:52 |
| Demo: Executing Blocking Transformations | 00:49 |
| Synchronous versus Asynchronous | 01:09 |
| Execution Trees | 02:52 |
| Demo: Reviewing The Pipeline Execution Tree Log | 04:01 |
| Performance Optimization: Buffers | 03:11 |
| Demo: Performance Optimization: Buffer Sizing | 01:57 |
| Parallelism, Transformations, Sources, and Destinations | 03:01 |
| Performance Monitoring | 03:08 |
| Summary | 03:00 |