Presented byBrad Gall

Using BI Techniques for Database Statistics

• BI Consultant with Pragmatic Works• Before Consulting I did Corporate IT for

many years▫ Systems Engineer, DBA, Business Intelligence

• I’ve been working with SQL Server and SharePoint for over 10 years

• Email: bradgallsql@outlook.com• New Blog: bradgallsql.com• Twitter: @bradgallsql

A little about me

•Today we’ll look at a real world example of using Microsoft Business Intelligence to report on SQL DBA Data

•This is something I did when I was working as a DBA and wanted to ▫A: get some reports on my databases▫B: learn some of the cool new tools from Microsoft in the process

•Familiar Data to most of us•Good end to end example of Self Service BI

What are we talking about today

•Part 1: Dimensional Modeling Techniques and Terminology▫Dimensional Modeling 101▫How does this apply the our DBA Data

•Part 2 (Demo): DBA statistic queries▫Dmv queries▫Queries to Load Dimensional Model “DBA Data Mart”

•Part 3 (Demo): “DBA Data Mart” reporting▫Load Data into PowerPivot▫Report with PivotTables

Agenda

•Kimball Data Warehousing

•2 Types of Tables▫Dimensions (Attributes) … what do I want to “slice by”▫Facts (Metrics) …what do I want to measure

Dimensional Modeling 101

• Dimension tables contain attributes that your users will use to slice and analyze metrics

• In a STAR schema, the Dimension tables are flattened (denormalized)

• Related attributes are grouped together into tables (some art some science to this)

• Keep attributes together in a table that you want to put into a Hierarchy

Dimensions

• Fact tables contain your “numbers”• These are the rows we’ll aggregate• Transaction Fact Tables▫ Should be modeled at the lowest

granularity that we need to analyze▫ Should also be modeled so that the

measures are SUM-able across all “grains” (Dimension tables)

▫ Should have a “narrow” physical structure (only keys and metrics)

Facts

• Dimensions▫ DimIndex

DatabaseName TableName IndexName

▫ DimStoredProcedures▫ DimDate▫ DimTime

• Facts▫ FactIndexGrowth▫ FactIndexUsage

Seeks and Scans▫ FactCachedQueryStats

Executions LogicalReads

▫ FactWaitStats WaitCounts SignalWaitTime

What are our Facts and Measures

Star Schema

• Query our DMVs (“stage”)• “Transform” our data in stored

procedure• Insert data into “DBA Data Mart”

• Load Data into PowerPivot (SSAS)

• Analyze Data with Excel Pivot Table (Power View report if time)

• Stage Data from Source• Transform Data (Cleanse and

“Reshape”)• Load Into Relational Data Warehouse

tables (Dimensional Model)• Load Data into SSAS database from

Relational Dimensional Model• Analytic Report Tools Built on OLAP

database

Typical Data Life Cycle Demo “Data Life Cycle”

• sys.dm_db_index_usage_stats• sys.dm_db_partition_stats (Index Sizes)• sys.dm_os_wait_stats

• Stored Procedure Stats▫ sys.dm_exec_query_stats▫ sys.dm_exec_sql_text▫ sys.dm_exec_cached_plans

• Good blog on these views Stratesql.com

DMVs used

• Built in to Excel 2013 (Add-in)• Self-Service Version of Analysis

Services Tabular (similar to SQL Express)

• Uses xVelocity Engine to compress and store data in Memory

(Speed!)• Excel Pivot Tables and Pivot Charts

read PowerPivot Model (drag and drop interface)

PowerPivot and Pivot Tables

DEMOS

•Scripts and files available on my blogbradgallsql.com

Conclusion