Microsoft SQL Server 2008 Analysis Services Business Intelligence Skills for MCTS 70-448 and MCITP 70-452 Video Mentor

II. Implementing and Managing SQL Server 2008 Analysis Services

a. Lab 1: Install and Configure SSAS

i. Pre-Installation Checklist (min req's, editions, etc.)

ii. Install SSAS in non-clustered environment then a clustered environment

iii. Configuring Logging, Storage (partition, dimension) & Security

b. Lab 2: SSAS Solution Management

i. Deploying Solutions (Dep Wiz, XMLA Scripts, Sync DB Wiz SSIS, BIDS)

ii. SSAS Project Object Processing (explanation and options)

iii. Implementing Object Processing (SSMS, BIDS, XMLA Scripts)

c. Lab 3: Monitor and Maintain SSAS

i. Monitoring queries (SQL Server Profiler, etc.)

ii. Monitoring server performance (System Monitor counters, DBCC UBOW, etc.)

iii. Backing up and restoring the SSAS database (SSMS, sqlcmd)

iv. Regenerating SSAS database objects (XMLA Scripts, reprocessing)

d. Lab 4: Create Basic SSAS Database Objects

i. Creating a Cube (Cube Wiz, Designer, CLI)

ii. Creating a Data Source View (add tables, create relationships, add keys, denormalization)

iii. Creating Named Queries and Calculations

iv. Creating a Dimension (Cube Designer, Dimension-Usage Tab, defining dims & fact tables)

v. Creating and Maintaining a Measure Group or Partition (defining measures by properties, adding measures to measure groups, partition storage modes, unknown member handling)

vi. Defining Data Access (ROLAPvsMOLAPvsHOLAP, caching, aggregation design, refresh)

vii. Creating a Query, Calculation, KPI and Action Using MDX

e. Lab 5: Modifying a SSAS Cube

i. Modifying SSAS Dimensions (member properties, attributes, column bindings, ragged hierarchies)

ii. Modifying SSAS Cubes (analyzing calculated members, perspectives, translations, KPI's, actions and drillthrough)

f. Lab 6: Secure SSAS Database Objects

i. Implementing security on a cube or dimension (roles, granularity, leveraging Windows)

ii. Implementing cell-level security (granular and drillthrough)

iii. Using MDX

II. Implementing Data Mining

a. Lab 1: Implement Data Mining

i. Creating a Data Mining Solution (DMWiz, DMDesigner)

ii. Defining and Implementing a Data Mining Model (prediction, trend analysis, compliance)

iii. Implementing a Data Mining Definition Method (algorithm parameters)

b. Lab 2: Process and Query a Data Mine

i. Processing a Data Mining Object (structure=full/structure/default ; model=full/default)

ii. Querying a Data Mining Model Using DMX (SSMS, Prediction Query Builder, reporting results), ADOMD, SSIS or .NET application viewer controls

c. Lab 3: Manage a Data Mining Solution

i. Validating a Data Mining Model (accuracy charts)

ii. Implementing Data Mining Security (structure and model)