Came across this product again today to help with a particular need. I’ve used Red Gate products before at another employer and loved them. However, I never realized the SQL LogRescue product. On top of that, it is a free tool.

Kudos to the Red Gate folks for making this tool available to the SQL Server community. I know, your saying that there are other ways of dealing with transaction logs, and you’re right. But what I like about the SQL Log Rescue is the quick intuitive way to essentially create an audit trail, review the transactions in question and undo various transactions individually.

Now the downside….it is only for SQL Server 2000. I know. that’s two SQL versions ago. So hear is my question.

What are you using to handling transaction log, transaction restore and such? Let me know, add a comment.

Just like any other application, OLAP solutions designed and built in Visual Studio 2008 for SQL Server Analysis Service 2008 must be managed from source code to server. In this session, learn different methods for managing your OLAP solutions. Well cover TFS and VSS storage, manual deployment methods using the Visual Studio GUI, as well as command line build automation methods specific to SQL Server Analysis Services project deployments.

Building SSAS Solutions

• Visual Studio / BIDS
  • All BI Projects
  • Easy to use compared to older versions
  • Team Foundation Server / Team System

Building a Solution

• Source Control
  • Visual Source Safe
    • Long history
    • Easy to use
    • Best for small environments
    • Isolated Development
    • Administration
  • Team Foundation Server
    • Central Repository
    • Project Management
    • SQL Server Based Repository
    • Collaborative Development
  • Analysis Services Perspective
    • Team Development
    • Build Projects in BIDS
    • Manage Databases in SSMS
    • BEWARE!

Configuring Deployment

• Configure Project Environment
• Configure Source Control
• Create Project “Configurations”
• Define Targets
• Define Actions

Deployment Solutions

• Visual Studio / BIDS
  • Visual Deployment
  • Immediate Feedback
  • Multiple Configurations
  • Easy to Use
• Automating Builds
  • Msbuild.exe doesn’t work
  • Devenv.exe (Visual Studio Command Line)
  • *.asdatabasefiles = SSAS Deployment Wizard
  • Microsoft.AnalysisServices.Depoyment.exe
    • Must create an answer file using /a ONCE
    • Produces an xmlafile
  • Deploy xmlafile via ascmd.exe

Nothing too terribly new here, but a good reminder for some.

One of the problems that presents those new to Business Intelligence development is how to configure the development and production environments for maximum efficiency. This session will cover the installation and configuration of SSAS for both development and production environments. Attendees will leave this session with a good understanding of the options available and the specific needs in an SSAS environment.

Haha. Beer can aerodynamics analogy. Funny!

Better in 2008

• Innovative Cube Designer
• Best practice design alerts
• Enhanced dimension designs
• Enhanced aggregation designs

I like Ted. Introducing his dog via desktop picture. Nikki?

Installation

• Two important things to SSAS
  • Memory (RAM)
  • I/O
    • Separate channel for your OLAP data
• Add new features to existing SQL Server 2008 instance
• Add SSAS
• Service Accounts
  • Generally speaking: Use a domain service account (Check Carl Dubler’s guide)
• Add current user to the Administrators role
• Data Directories tab
  • Move folders into the appropriate locations based upon I/O channel decisions
• Proceed with the installation process.

Introduces new dog. Sasha! hehe

Configuration (In SSMS)

• Properties of Instance
  • Log\FlightRecorder
    • Captures trace info if stuff goes wrong
    • Enabled by default
    • FlightRecorderCurrrent.trc file
  • Memory
    • Personally Ted hates dynamic memory allocation.
    • If SQLServer and Analysis Services installed on the same box, you must set limits on SQL Server Memory
    • Books Online actually is very good on this topic
      • Planning & Architecture -> Physical Architecture topics are the ones
    • Set Low Memory Limit to 50% – Not a best practice but allows you to look at profiler, services, process and determine how to proceed.
    • While publishing cube, watch the msmdsrv.exe process to see memory utilization.

Books Online. Books Online. Books Online.

SSIS is a great tool for transferring data from one data source to another, and for implementing complex ETL processes. However, for simple, straightforward data transfer tasks, creating SSIS packages by hand can be time-consuming and repetitious. By attending this session, you’ll learn how to automate data transfers from a source to a target using SSIS. You’ll see the steps necessary to create both the control flow and the data flow portions of an SSIS package using the API and C# code. You’ll also learn about altering existing packages programmatically. Specific attention will be given to the generation of data flows with the proper source and destination components. We’ll take a look at some of the open source solutions available for this, plus some “roll your own” options.

Session Specific blog entry: http://agilebi.com/cs/blogs/jwelch/archive/2009/03/27/sswug-business-intelligence-virtual-conference-and-sds.aspx

Common Scenarios for SSIS

• Doing a simple import to or export from a SQL Server table
• Moving data from server to server on a regular basis
• Performing complex ETL to transform data

Should You Automate?

• Does the process follow a consistent pattern?
• Do you need to perform the same operation against many tables?
• Do you have lots of packages that look exactly the same?
• If so, the answer is YES!

Automation Benefits

• DRY (Don’t Repeat Yourself) [why haven't I heard this TLA before?]
  • Save Time and Money
  • Reduces the chance of introducing bugs due to human error
• Can help enforce consistency in package design
• Speeds up repetitive, autopilot tasks

All these tools below are a revelation to me. This rocks!

SSIS API

• Benefits
  • Complete access to all the features of SSIS
  • You are in complete control
  • Control Flow manipulation is straightforward
• Drawbacks
  • Steep learning curve, but more examples and resources being published.
  • Data Flow manipulation is complex due to COM layer
  • Requires a lot of code to perform simple tasks

EzAPI

• Open source project from SSIS team
• Benefits
  • Vastly reduced amount of code to create packages
  • Automates much of the column mapping process
• Drawbacks
  • Hides some features of SSIS
  • Not under active development (Sounds like death to me)

Package Generation Sample

• Open source project from SSIS team
• Benefits
  • Command line interface
  • Good learning code
• Drawbacks
  • Only supports SQL Server, Excel, and flat files

MDDE Studio

• Open source project originally from Microsoft
• Benefits
  • Template based approach
  • Works with 2005
• Drawbacks
  • Not under active development
  • Only works with 2005

Project Vulcan

• Open source project from Microsoft
• Benefits
  • Uses XML files to drive package generation
  • Generates SSIS and SSAS objects
  • Active development team
• Drawbacks
  • Very complex, little to no documentation

Recommended Approach

• Simple Generation
  • Use EzAPI to minimize coding
  • Use the SSIS API for things EzAPI doesn’t expose
• Industrial Strength Generation
  • Evaluate MDDE and Vulcan to determine which one is closest to your requirements
  • Customize from there

Where To Start

• Control Flow is easiest to learn
• Data Flow is considerably more complex
  • Start with simple column mappings
• Review existing packages to get details
  • View XML for package
  • Load through the API and iterate through the package

Be Aware

• Many features in BIDS are implemented in the BIDS UI, not the SSIS object model
  • flat file metadata detection, automatic column mapping, etc.
• Script Tasks and Components are especially problematic
  • Pre-compiling scripts.
  • Don’t use if you don’t have to.

Considerations

• Should you automate?
  • Not a great choice if packages are rarely the same twice
  • Can you get the same results from templates?
• One time automation vs. recurring generation
  • Producing a “first pass” package can be a good accelerator
  • Recurring generation may limit flexibility, but can be more powerful

SSIS Automation Resources

• http://sqlsrvintegrationsrv.codeplex.com/
• http://sqlservermddestudio.codeplex.com/
• http://vulcan.codeplex.com/
• Blogs:
  • http://blogs.msdn.com/mattm/default.aspx
  • http://www.sqlis.com/category/Code-Development.aspx

Is your customer looking for drag and drop reports or capabilities inside of Excel. SQL Server Analysis Services (SSAS) is the answer for you then. You’ll be amazed how quickly you can develop sophisticated reports after watching the basics of this session.

SSAS is its own server. It is not part of the SQL Server service.

• IIS
• XMLA between server and client

MDX – SQL Language for Cubes
BIDS – Building the Cubes
SSMS – Managing SSAS

Analysis Services

What are cubes?

• What are measures? (See Erik Veerman Dimensional modeling session)
  • The value you are measuring, e.g. the count of users with blue eyes, from WA state, etc.
  • The question you are trying to ask
• What are measure groups?
  • Groupings of these measures within your cube
  • Typically define them by your business problem (e.g. Sales, HR, etc.)
• Cubes are a grouping of measure groups.

Analysis Services Dimensions

What are dimensions and hierarchies? (See Erik Veerman Dimensional modeling session)

• What is a dimension?
  • It is what you want to categorize or pivot against
  • e.g. How many users are from WA state?
  • The dimension is geography, “WA” is the member
• •What is a hierarchy?
  • How do you want to organize the members of your dimension
  • e.g. Geography has numerous levels such as country, state, and city
  • Hierarchy is the organization of those levels such as City ->State ->Country

Concrete examples like these are helpful for my learning and understanding. Thanks!

User Interfaces

• Query language is MDX
• BIDS for developers
• Excel for most users is adequate or Reporting Services
• SharePoint for web users
• 3rdparty applications like Proclarity or Panorama
• Controls you can purchase and build into your own apps

Create User defined hierarchies.

• Remember that in 2008 the hierarchy can be thought of as reversed.
• Looks graphically like date -> month -> qtr -> year
  • Means: to get to a date you must go through year, qtr, month, date

Deal with problematic keys.

Most end users will use Excel. 2007 provides additional features.

This talk describes the new MERGE statement and Change Data Capture (CDC) feature introduced in SQL Server 2008, and how to take advantage of them in SQL Server Integration Services. Well cover the traditional ways of doing incremental data loads, and see how these new key features make the process a whole lot easier. If you’re interested in learning best practices for this new SQL Server 2008 functionality, you won’t want to miss this session!

CDC and Merge are new in SQL Server 2008

Also 2008 has improved SCD processing

Options to Capture Change

• Audit columns – created via triggers
• Log Scraping
• Full DB Diff compare

(New Options) Change Data Capture in SQL Server

• Changes captured from the log asynchronously
  • Really just integrated log scraping
• Tells you what changed at the source
• Enabled per table or DB
• CDC API’s provide access to change data

CDC Components

• CDC Tables
• Capture Job
• Cleanup Job
• API

Change Tracking

• Captures that a row was changed, but not the changed data
• Synchronous capture mechanism
• Less overhead for applications that do not require the historical information

Turn CDC on:

• sp_cdc_enable_db
• sp_cdc_enable_table
• LSN = Log Sequence Number
• Sp_cdc_generate_wrapper_function
  • Creates a custom function DDL for reading CDC data

Loading SSIS Package

• Source from the CDC Wrapper function
• Split on CDC operation
  • Insert
  • Update
  • Delete

Lookup Changes in SQL 2008

• Explicit cache modes
  • Full
  • Partial
  • None
• Cache connection manager

External Reference Data

• Joins can work but can be difficult to manage
• Instead, use the SSIS Lookup transform.

The MERGE Statement

• Single statement can deal with Inserts, Updates and Deletes all at once
• Use Cases
  • Conditionally insert or update rows in a target table
  • Synchronize two tables
• Built to perform

Using MERGE in SSIS (Execute SQL Tasks in SSIS)

• Stage
  • Store data in a temp table
• Optimize
  • Create indexes on join cols
  • Provides addtl perf benefits
• Execute
  • Run the MERGE SQL statement

When doing the insert, you must drop the FK constraints in the destination table.

Taking Advantage of MERGE

• Consume CDC data
• Update Dimensions
• Alternative to the Slowly Changing Dimension Wizard

Additional Resources

• SSIS Team Blog
  • http://blogs.msdn.com/mattm
• Comparing Change Data Capture and Change Tracking
  • http://msdn.microsoft.com/en-us/library/cc280519.aspx
• Optimizing MERGE Statement Performance
  • http://msdn.microsoft.com/en-us/library/cc879317.aspx
• MERGE Destination
  • http://www.codeplex.com/SQLSrvIntegrationSrv/Release/ProjectReleases.aspx?ReleaseId=19048

In this session, you’ll learn how to load a typical data warehouse in SSIS efficiently. You’ll start by seeing some of the strengths and weaknesses of the Slowly Changing Dimension (SCD) Wizard in SSIS and how you can get around some of the weaknesses including your own home-brewed solution. You’ll then see how to load a fact table using SSIS and how to make the common components scale.

Dimensional Modeling (See Dimensional Modeling 101 Notes)

• Data Separated into fact and dimension tables
• Dimension tables answer the pivot or where clause
  • Make as wide and descriptive as possible
  • Surrogate keys operate as unique ID for each row
  • Keep surrogate keys as small as possible
• Fact tables answer the what questions or select statement
  • Intersect all dimension tables
  • Surrogate keys from each dimension in this table
  • Measures are the “what” like Price, Quantity, Duration

The challenge is how you move the data from the OLTP (Relational) DB into the Data Warehouse.

Discusses SCD (Slowly Changing Data) Dimension types (Already here)

Don’t make everything Type two or your DB will bloat significantly. Also your reports would be more difficult to write.

Probably want to fix NULL values to be something. Use a Derived Column transform. Makes Nulls to be something like 0, Unknown, etc…

SCD Wizard Strengths

• SSIS transform that creates many other transforms conditionally
• Reduces design time of SCD load by 80%-90% to minutes per dimension
• Can be customized easily
• Compares differences between source and destination to find changes and new records
• Outputs:
• Type 0,1,2 update
• Inferred members
• New rows
• Duplicate rows

Historical Attribute Options – How do you want to set the expiration of historical records.

Problem with SCD Wizard is that any time you go back and change the configuration, all the output logic below it gets re-written. You lose what you created.

SCD Wizard Weaknesses

• Scalability -Generally up to about 50,000 records into the transform but varies based on number of updates
• Maintainability -After you customize, rerunning the wizard recreates all the transforms
• Uses OLE DB Command transforms for updates is row-level. Creates scalability issue here if lots of updates.

Making Your Own SCD Wizard

• Can use a Merge Join or Lookup Transform
  • If no match found, it is an insert (Ignore Errors)
• Lookup Transform will scale better than Merge Join but lacks parameterization
• Add a Conditional Split transform after Lookup to direct to insert, duplicate or update path

Additional Scalability

• Watch your Lookup Transformation for scalability issues (don’t cache too much!)
  • Potentially cache only the last 1 years worth of data with Partial Caching
  • Only cache columns needed
• Additional scalability can be reached by landing updates into a staging table
  • Then set-based update with an Execute SQL task.
• Checksum Transform can be used to detect changes across many columns
  • Or HASHBYTES T-SQL statement

Inferred Members

• Created during the fact load
  • A new Dim record is created using the value of “unknown” or NULL as a placeholder
  • The record is flagged as an inferred member

Slowly Changing Dimension Wizard

• SSIS transform that creates many other transforms conditionally
• Handles:
• Type 0 (fixed attribute)
• Type 1 (changing attribute)
• Type 2 (historical attribute)
• Inferred members
• Typically can address 80% of the business scenarios

SCD Wizard Strengths

• SSIS transform that creates many other transforms conditionally
• Reduces design time of SCD load by 80%-90% to minutes per dimension
• Can be customized easily
• Compares differences between source and destination to find changes and new records
• Outputs:
• Type 0,1,2 update
• Inferred members
• New rows
• Duplicate rows

SCD Wizard Weaknesses

• Scalability -Generally up to about 50,000 records into the transform but varies based on number of updates
• Maintainability -After you customize, rerunning the wizard recreates all the transforms
• Uses OLE DB Command transforms for updates is row-level. Creates scalability issue here if lots of updates.

Lookup Transform – Lookup source against target dimension table. Select ALL available lookup fields and alias at TARGET_ so you can match against them. Link by primary key. Ignore the failure of matches.

Then use a conditional split – If target PK is NULL it is a new record, else Update.

“Pretty much a pain in the butt to write.” – Brian Knight in reference to the else UPDATE piece from the conditional split.

Ugh…no transcript and this info is not in the slides. ARGH! Can’t remember it all.

Brian uses the Checksum source vs. Checksum destination to deal with the UPDATE referred to above, but not guaranteed to be unique.

Hashbytes on the other hand is more unique if you wish. But it does not work on numeric fields. Need to cast them.

Fact Table Loads

• Series of Lookup Transforms
  • In Type 2 Dimensions add WHERE EndDate IS NOT NULL
• Measures created Derived Column Transforms
• Aggregate transform to roll up the grain
• Lookup failure would create an inferred member or set to unknown

Are you a SQL Server DBA, Developer, Report Writer?
Do you align with a desire to spread God’s Word?
Do you love working in an evolving/energetic environment?

Yes? Well here’s  a neat opportunity.  The Seed Company is a Christian non-profit focused on Bible Translation. Based in Arlington, Texas in the heart of the D/FW metroplex they are seeking a multi-talented individual to fill a big need.

“The Seed Company is a faith-based, 501(c)3 organization committed to the vision of creatively networking God’s people around the world to translate the Bible into every language for His glory”

Here’s the info on the position.

Brief Description: Preserve and extend TSC staff capabilities through database development, administration, and reporting.

Responsibilities:

General:

1. Has servant heart, and a desire to make others more effective in their work.
2. Good interpersonal skills.
3. Strong written and verbal communication skills.
4. Ability to understand user needs and to design database solutions appropriate to those needs.
5. Familiarity with computer networking and security issues.
6. Familiarity with server administration and backup/restore techniques.
7. Ability to document routine tasks.
8. Working knowledge of Microsoft Access, Windows Server and Microsoft Office products.
9. Working knowledge of web technologies.

Specific:

1. Ability to develop and maintain data dictionaries and data models/ schemas.
2. Ability to design and maintain reports and reporting environments.
3. Ability to design and implement user interfaces for data entry and reporting.
4. Ability to design and maintain integration (ETL, SSIS) with other database systems.
5. Knowledge of tools for database design, performance monitoring and user interface development.
6. Thorough understanding of SQL Server and reporting tools.

The position is full-time and will be out of the Arlington, Texas office. There are a number of neat initiatives going on including moving to an SSRS reporting environment (SSRS 2008), the potential to start into Data Warehousing, data integration and automation with a number of internal and external information systems.

Interested parties should contact:

Dave Mutchler
dave_mutchler@tsco.org
(817)-557-2121

Corporate Headquarters

The Seed Company
3030 Matlock Rd. Suite 104
Arlington TX 76015
USA
view map
Phone (817) 557-2121
Toll Free (877)593-7333

Contact Info: Email resumes with cover letter to: dave_mutchler@tsco.org

Business Type: Bible Translation

Comments: The Seed Company is an affiliate of Wycliffe Bible Translators and seeks to make employment decisions in accordance with applicable federal and state employment discrimination laws and regulations. As a religious organization, The Seed Company is entitled to make employment decisions on the basis of religious beliefs and practices of the applicant or employee. All positions require a personal commitment to Christ, evidenced by personal testimony and identification with a local church.

In SQL 2008, Maintenance Plans create a workflow of tasks to ensure your database is optimized and backed up on a regular schedule. The Maintenance Plan will create an SSIS package that will be scheduled and run with SQL Agent. Attend this session and learn how Maintenance Plans will ease your administrative overhead.

Common Tasks

• Database backups
• Database Integrity checks
• Index Maintenance
• Automated Cleanup

Creating New Plans

• Maintenance Plan Wizard
• Maintenance Plan Design
• Must be member of sysadmin fixed server role

Important Tasks to Deal With

• Backup Database
• Reorganize Index (fragmentation level < 30%, Reorg)
• Rebuild Index (fragmentation level > 30%, Rebuild)
• Update Statistics
• Check Database Integrity (DBCC CHECKDB)
  • Also validates indexes in SQL Server 2008
• History Cleanup
  • Cleans information inside information Sys DB’s
• Maintenance Cleanup (MSDB)
  • Cleans up after maintenance plans themselves
  • Or Backup files, logs, etc…

“Failure to plan for a disaster is inexcusable.” – Thomas LaRock

Schedules will depend upon your organizational needs.

Recommended Practices

• Create separate plans for system versus user databases
  • Yes, System DB’s need to be backed up and able to be restored!
• Tasks should be run at non-peak hours (Duh!) 
  • Be sure to check for other running tasks, like reporting or ETL loads.
• Do not automate the Shrink Database task
  • Really? Do use it period according to Thomas!
• Perform database backups prior to other maintenance tasks
  • Back them up before you start touching anything even in a maintenance plan.
  • Always have a “comfortable” restore point.

MultiServer Management

• Configure a SQL Server Agent to be a “Master”
• Specify Target
• Define new Multi-Server jobs or
• Convert existing Local Job to multi-server.
  • Right click
  • See Target tab. Use it.

FYI, the job only shows on the Master. It does not show in the target server job list.

“Since it’s a demo, we need everything to work.” – Thomas LaRock – Great quote. Too bad it didn’t work.

Additional Resources

• http://sqlserverpedia.com/wiki/Main_Page
• http://msdn.microsoft.com/en-us/library/ms187658.aspx

In a Business Intelligence solution, the fact tables hold the core data that you are analyzing – facts (also called measures). Therefore, fact tables are a critical component to get right the first time. Poor fact table design will lead to poor performance and difficult calculations. This session dives into fact table and considers the basic column types, measure aggregation types, fact table types, and volume considerations.

Facts

• The fact itself
  • The “measure” that is being tracked. The thing
  • Quantity, count, amount, percent
  • Most always numerical, continuous values
    • e.g., price of a product, quantity sold, budget value, count of customers
• Facts (or measures) can be classified by…
  • Numerical data type
  • Aggregation type
  • Additive nature
  • Granularity – level of detail stored
• Fact tables
  • Capture measures/facts
  • Association with dimensions (using surrogate key as foreign key in fact table)
    • No dimensions attributes!
  • Some tracking information included
• Different types of fact tables
  • Transactional – Additive facts tracking events over time (Star Schema)
    • Most common type of fact table
      • Track the occurrence of events, each detailed event is captured into a row in the fact table
      • Measures are typically additive across all dimensions
      • Common transactional fact table types
        • Sales, Visits, Web-page hits, Account transactions
  • Snapshot or inventory – Pictures in time of levels or balances
    • Periodic
    • Accumulating
    • Known as inventory level fact tables
      • Time dimension used to identify grain
      • Non additive measures across time, but typically additive across all other dimensions
      • Common transactional fact table types
        • Inventory levels, Event booking levels, Chart of account balance levels
  • Factless – Dimensionality relationships
    • No measured facts!
      • Are useful to describe events and coverage
      • Information that something has or has not happened
        • Often used to represent many-to-many relationships
        • Contain only dimension keys
        • Common factless fact tables:
      • Class attendance, Event tracking, Coverage tables, Promotion or campaign facts
• Fact Table Granularity
  • Never mix the grain of the table!
  • The level of detail of data contained in the fact table
  • The description of a single instance (a record) of the fact table
  • Typically includes a time level and a distinct combinations of other dimensions
    • e.g. Daily item totals by product, by store, Weekly snapshot of store inventory by product

Maybe include ETL load date/time in the fact table.

Measures – Additive Nature

• Additive: Facts that can be summed up/aggregated across all of the dimensions in the fact table
  • e.g. discrete numerical measures of activity, i.e. quantity sold, dollars sold
• Semi-Additive: Facts that can be summed up for some of the dimensions in the fact table, but not the others
  • e.g. numerical measures of intensity, i.e. account balance, inventory level, distinct counts
• Non-Additive: Facts that cannot be summed up for any of the dimensions present in the fact table.
  • e.g. room temp

Aggregations

• Aggregation (Aggs): A summarization of base-level fact table records
  • Common aggregation scenarios
    • Category product by store by day
    • District store by product by day
    • Monthly sales by product by store
    • Category product by store district by day
    • Category product by store district by month
• Aggregations need to account for the additive nature of the measures
  • Aggregations can be created on-the-fly or by the process of pre-aggregation
  • Common aggregations
    • Sum
    • Count, Distinct Count
    • Max, Min
    • Average
    • Semi-additive: Last Child, Last Non-empty Child

http://www.microsoft.com/fasttrack
SQL Server Fast Track Data Warehouse accelerates your data warehouse roadmap with new SQL Server 2008 Enterprise scalable reference architectures for HP, Dell and Bull. Reduce costs, save time and reduce risk with reliable, pre-tested hardware and best practices for warehousing. Read more.

Design with Additive in Mind

• Think dimensionally!
• Complex requirements don’t need to be designed with complex queries
• Many times new fact tables can be designed that can answer specific questions, such as date attributes and ranges

Getting Started

• Step 1: Identify high-value business process to model (orders, invoices, shipments, inventory)
  • Confirm data source availability
  • Understand value vs. complexity
• Step 2: Identify reporting grain of the business process
  • The grains is the level of detail at which the data should be represented for analytics
  • This may not be the same grain as the source!
  • For snapshot facts, determine what time level will be captured for each snapshot (daily, weekly, monthly)
• Step 3: Identify dimensionality that will apply to each fact table
  • Time, product, customer, store, etc.
  • Some dimensions are not grain-identifying
  • Validate the source can associate to the fact table
• Step 4: Identify measured facts that will populate fact table
  • Validate the base measures are identifiable from the source
  • Some measures may be derived
  • Measure examples: product count, quantity sold, dollars sold, inventory quantity
• Identify business questions:
  • How much total business did my newly remodeled stores do compared with the chain average?
  • How did leather goods items costing less than $5 do with my most frequent shoppers?
  • What was the revenue comparison of non-holiday weekend days to holiday weekend days?
• Analyze questions to assist design!

I really like these “Getting There” bullets. Erik does a good job summarize and giving you something practical to walk away with. Nice stuff.