SlideShare a Scribd company logo
1 of 20
Download to read offline
DBA AS PROTECTOR OF THE DATA:
NOTES FROM THE FIELD
 Speaker: Denise McInerney
 Development DBA, Intuit


                San Francisco SQL Server User Group
                         October 13, 2010




                   Mark Ginnebaugh, User Group Leader,
                         mark@designmind.com
Agenda
     g
2

       Introduction
       Goals
       Topics
         My POV
         What is “bad” data?
        D i
          Design
         Garbage in
         Transactions
         Change Management & Controls
         Partnership
         Attitude
                         Copyright © 2010 Denise McInerney
Who am I?
   SQL Server DBA since 1998
   Web-based OLTP applications
   Focus on design and performance tuning
   Development DBA www.intuitmarket.com
   PASS volunteer since 2003
       Founded Women in Tech chapter
   Contact me
       denise.mcinerney@sqlpass.org
       denise_mcinerney@intuit.com
       Twitter: @denisemc06

                          Copyright © 2010 Denise McInerney   3
www.intuitcareers.com
4


       Lead DBA                                   Openings in Menlo Park &
                                                     p    g
       Sr. Database Engineer                       Mountain View, CA and require
                                                    SQL experience
       Senior Data Quality Analyst
       Senior QA Developer                        Follow Intuit Careers on
                                                    F ll I t it C
       Software Engineer in Quality                FaceBook, Twitter and LinkedIn
       Server Side Engineer                        for updates on job openings
       Software E i
        S f       Engineer
                                                   Apply directly online at
       Architect                                   www.intuitcareers.com
       Performance Engineer
       Test Automation Engineer                   Questions on this job or any
       Systems Engineer                            others? Post, Tweet or Message
                                                    us on our Social Media ‘Intuit
       Software Tester                             Careers
                                                    Careers’ sites

                             Copyright © 2010 Denise McInerney
PASS
5



       PASS Community Summit Nov 8 11
                                  8-11
         Best   SQL Server training value
       PASS Women in Tech
         8th   Annual Women in Tech Panel @ Summit
           “Recruiting
             Recruiting,
                      Retaining & Advancing Women in Technology:
            Why Does it Matter?”
         http://wit.sqlpass.org

         #passwit   on Twitter


                            Copyright © 2010 Denise McInerney
My Goals
     y
6


       Show you how to proactively ensure that the data in
        your transactional system is clean and correct
        GOING IN
       Explain why an integrated approach is needed
       Give Real-life examples
              Real life




                        Copyright © 2010 Denise McInerney
My Point of View
         y
7


       Data protection intrinsic to the job
       Backups, security—what about quality?
       DBA s
        DBA’s approach different, complementary
                         different
       Broad exposure to the stack
       Better at your job
        B              j b
         Morehighly valued, trusted
        S
         Successful
                f l



                          Copyright © 2010 Denise McInerney
Define “bad” data
8


       Inaccurate
       Missing
       Misleading
       Causes bugs
        B
         Bugs   have consequences
                h
       Breaks reports
       Impacts customers
        I


                         Copyright © 2010 Denise McInerney
Design
9


       Have one—even in an “agile” shop
                one even          agile
       It’s a system, not a collection of tables
       Don t
        Don’t be lazy
         Re-use a column
         Just add a column

         Just add a table

       Bad design invites bad data



                         Copyright © 2010 Denise McInerney
Design
         g
10




        Always assume the data will be used by others




                        Copyright © 2010 Denise McInerney
Bad Design Examples
             g      p
11




               Copyright © 2010 Denise McInerney
Garbage In
          g
12


        Failed INSERT…the silent killer
          Order header, no detail
          Order taken, not fulfilled

          Missing records = no reconciliation

        Concatenation
          First name + last name in first name field
          Zip code 5 + 4

          Phone + phone extension
            h        h
        Duplicate orders, different order numbers

                            Copyright © 2010 Denise McInerney
Garbage In
          g
13




      Default values substituted for real data
      NULL <> ‘’ or “”




                      Copyright © 2010 Denise McInerney
Garbage In
          g
14




              Copyright © 2010 Denise McInerney
Transactions
15




        Understand them
        Don’t assume others understand them
        BEGIN TRAN…COMMIT TRAN is not enough




                      Copyright © 2010 Denise McInerney
Transactions
16




                Copyright © 2010 Denise McInerney
Change Control
         g
17


        Lots of ways data gets IN
                   y       g
        Code—application & SQL
          Version  control
          Scripts for everything!

          Change management
                g         g
        Data updates
          How   do your lookup tables get populated?
                    y         p        g p p
        People
          Who  can update data directly?
          Everyone takes short cuts
It Takes a Village
                     g
18
Summary--Pieces
     Summary--Pieces of the Puzzle
           y
19




        DBA’s job to guard data quality
        Starts with design
        Many types of “bad”
        Transactions are crucial
        Know how data gets in your database
          Implement   controls & processes
        Cultivate relationships
        Pessimism & vigilance
                            Copyright © 2010 Denise McInerney
To learn more or inquire about speaking opportunities, please contact:
 o ea     o e o qu e about spea g oppo tu t es, p ease co tact:

                Mark Ginnebaugh, User Group Leader
                      mark@designmind.com

More Related Content

Similar to Microsoft SQL Server DBA as Protector of the Data - Oct 2010

DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...
DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...
DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...DATAVERSITY
 
Data-Ed: Unlocking business value through data modeling and data architecture...
Data-Ed: Unlocking business value through data modeling and data architecture...Data-Ed: Unlocking business value through data modeling and data architecture...
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
 
Make Better Decisions With Your Data 20080916
Make Better Decisions With Your Data 20080916Make Better Decisions With Your Data 20080916
Make Better Decisions With Your Data 20080916Dan English
 
Empowering the Business with Agile Analytics
Empowering the Business with Agile AnalyticsEmpowering the Business with Agile Analytics
Empowering the Business with Agile AnalyticsInside Analysis
 
Staying Productive with Social Streams
Staying Productive with Social StreamsStaying Productive with Social Streams
Staying Productive with Social StreamsLuis Benitez
 
From Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllFrom Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllDataWorks Summit
 
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, Md
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, MdCleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, Md
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, MdDeb Thomas
 
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MD
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MDCleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MD
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MDClearedJobs.Net
 
Information Architecture
Information ArchitectureInformation Architecture
Information ArchitectureInnoTech
 
SharePoint Information Architecture
SharePoint Information ArchitectureSharePoint Information Architecture
SharePoint Information ArchitectureCredera
 
Application Logging for fun and profit. Houston TechFest 2012
Application Logging for fun and profit.  Houston TechFest 2012Application Logging for fun and profit.  Houston TechFest 2012
Application Logging for fun and profit. Houston TechFest 2012Jane Prusakova
 
Uxd corporate presentation
Uxd corporate presentationUxd corporate presentation
Uxd corporate presentationMandar Mayekar
 
ATAAS2016 - Big data analytics – data visualization himanshu and santosh
ATAAS2016 - Big data analytics – data visualization   himanshu and santoshATAAS2016 - Big data analytics – data visualization   himanshu and santosh
ATAAS2016 - Big data analytics – data visualization himanshu and santoshAgile Testing Alliance
 
Ug apm - ca executive insight customer presentation v2.2 english
Ug  apm - ca executive insight customer presentation v2.2 englishUg  apm - ca executive insight customer presentation v2.2 english
Ug apm - ca executive insight customer presentation v2.2 englishCA Technologies Italia
 
Delivering Insights: Building the DataScience Web Application
Delivering Insights: Building the DataScience Web ApplicationDelivering Insights: Building the DataScience Web Application
Delivering Insights: Building the DataScience Web ApplicationDataScience
 

Similar to Microsoft SQL Server DBA as Protector of the Data - Oct 2010 (20)

My Resume.
My Resume.My Resume.
My Resume.
 
DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...
DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...
DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Archi...
 
Data-Ed: Unlocking business value through data modeling and data architecture...
Data-Ed: Unlocking business value through data modeling and data architecture...Data-Ed: Unlocking business value through data modeling and data architecture...
Data-Ed: Unlocking business value through data modeling and data architecture...
 
Make Better Decisions With Your Data 20080916
Make Better Decisions With Your Data 20080916Make Better Decisions With Your Data 20080916
Make Better Decisions With Your Data 20080916
 
Empowering the Business with Agile Analytics
Empowering the Business with Agile AnalyticsEmpowering the Business with Agile Analytics
Empowering the Business with Agile Analytics
 
Staying Productive with Social Streams
Staying Productive with Social StreamsStaying Productive with Social Streams
Staying Productive with Social Streams
 
From Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllFrom Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for All
 
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, Md
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, MdCleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, Md
Cleared Job Fair Job Seeker Handbook March 3, 2011, Bwi, Md
 
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MD
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MDCleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MD
Cleared Job Fair Job Seeker Handbook March 3, 2011, BWI, MD
 
Information Architecture
Information ArchitectureInformation Architecture
Information Architecture
 
SharePoint Information Architecture
SharePoint Information ArchitectureSharePoint Information Architecture
SharePoint Information Architecture
 
bio data
bio databio data
bio data
 
Application Logging for fun and profit. Houston TechFest 2012
Application Logging for fun and profit.  Houston TechFest 2012Application Logging for fun and profit.  Houston TechFest 2012
Application Logging for fun and profit. Houston TechFest 2012
 
Uxd corporate presentation
Uxd corporate presentationUxd corporate presentation
Uxd corporate presentation
 
ATAAS2016 - Big data analytics – data visualization himanshu and santosh
ATAAS2016 - Big data analytics – data visualization   himanshu and santoshATAAS2016 - Big data analytics – data visualization   himanshu and santosh
ATAAS2016 - Big data analytics – data visualization himanshu and santosh
 
Ug apm - ca executive insight customer presentation v2.2 english
Ug  apm - ca executive insight customer presentation v2.2 englishUg  apm - ca executive insight customer presentation v2.2 english
Ug apm - ca executive insight customer presentation v2.2 english
 
dave2
dave2dave2
dave2
 
Delivering Insights: Building the DataScience Web Application
Delivering Insights: Building the DataScience Web ApplicationDelivering Insights: Building the DataScience Web Application
Delivering Insights: Building the DataScience Web Application
 
Iwill_CV[1][1][1][1]
Iwill_CV[1][1][1][1]Iwill_CV[1][1][1][1]
Iwill_CV[1][1][1][1]
 
JeremiahHolder-CV
JeremiahHolder-CVJeremiahHolder-CV
JeremiahHolder-CV
 

More from Mark Ginnebaugh

Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction Mark Ginnebaugh
 
Platfora - An Analytics Sandbox In A World Of Big Data
Platfora - An Analytics Sandbox In A World Of Big DataPlatfora - An Analytics Sandbox In A World Of Big Data
Platfora - An Analytics Sandbox In A World Of Big DataMark Ginnebaugh
 
Microsoft SQL Server Relational Databases and Primary Keys
Microsoft SQL Server Relational Databases and Primary KeysMicrosoft SQL Server Relational Databases and Primary Keys
Microsoft SQL Server Relational Databases and Primary KeysMark Ginnebaugh
 
DesignMind Microsoft Business Intelligence SQL Server
DesignMind Microsoft Business Intelligence SQL ServerDesignMind Microsoft Business Intelligence SQL Server
DesignMind Microsoft Business Intelligence SQL ServerMark Ginnebaugh
 
San Francisco Bay Area SQL Server July 2013 meetings
San Francisco Bay Area SQL Server July 2013 meetingsSan Francisco Bay Area SQL Server July 2013 meetings
San Francisco Bay Area SQL Server July 2013 meetingsMark Ginnebaugh
 
Silicon Valley SQL Server User Group June 2013
Silicon Valley SQL Server User Group June 2013Silicon Valley SQL Server User Group June 2013
Silicon Valley SQL Server User Group June 2013Mark Ginnebaugh
 
Microsoft SQL Server Continuous Integration
Microsoft SQL Server Continuous IntegrationMicrosoft SQL Server Continuous Integration
Microsoft SQL Server Continuous IntegrationMark Ginnebaugh
 
Hortonworks Big Data & Hadoop
Hortonworks Big Data & HadoopHortonworks Big Data & Hadoop
Hortonworks Big Data & HadoopMark Ginnebaugh
 
Microsoft SQL Server Physical Join Operators
Microsoft SQL Server Physical Join OperatorsMicrosoft SQL Server Physical Join Operators
Microsoft SQL Server Physical Join OperatorsMark Ginnebaugh
 
Microsoft PowerPivot & Power View in Excel 2013
Microsoft PowerPivot & Power View in Excel 2013Microsoft PowerPivot & Power View in Excel 2013
Microsoft PowerPivot & Power View in Excel 2013Mark Ginnebaugh
 
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball Approach
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMicrosoft Data Warehouse Business Intelligence Lifecycle - The Kimball Approach
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMark Ginnebaugh
 
Fusion-io Memory Flash for Microsoft SQL Server 2012
Fusion-io Memory Flash for Microsoft SQL Server 2012Fusion-io Memory Flash for Microsoft SQL Server 2012
Fusion-io Memory Flash for Microsoft SQL Server 2012Mark Ginnebaugh
 
Microsoft Data Mining 2012
Microsoft Data Mining 2012Microsoft Data Mining 2012
Microsoft Data Mining 2012Mark Ginnebaugh
 
Microsoft SQL Server PASS News August 2012
Microsoft SQL Server PASS News August 2012Microsoft SQL Server PASS News August 2012
Microsoft SQL Server PASS News August 2012Mark Ginnebaugh
 
Microsoft Mobile Business Intelligence
Microsoft Mobile Business Intelligence Microsoft Mobile Business Intelligence
Microsoft Mobile Business Intelligence Mark Ginnebaugh
 
Microsoft SQL Server 2012 Cloud Ready
Microsoft SQL Server 2012 Cloud ReadyMicrosoft SQL Server 2012 Cloud Ready
Microsoft SQL Server 2012 Cloud ReadyMark Ginnebaugh
 
Microsoft SQL Server 2012 Master Data Services
Microsoft SQL Server 2012 Master Data ServicesMicrosoft SQL Server 2012 Master Data Services
Microsoft SQL Server 2012 Master Data ServicesMark Ginnebaugh
 
Microsoft SQL Server Testing Frameworks
Microsoft SQL Server Testing FrameworksMicrosoft SQL Server Testing Frameworks
Microsoft SQL Server Testing FrameworksMark Ginnebaugh
 
Microsoft SQL Server - How to Collaboratively Manage Excel Data
Microsoft SQL Server - How to Collaboratively Manage Excel DataMicrosoft SQL Server - How to Collaboratively Manage Excel Data
Microsoft SQL Server - How to Collaboratively Manage Excel DataMark Ginnebaugh
 
Microsoft SQL Server Flash Storage
Microsoft SQL Server Flash StorageMicrosoft SQL Server Flash Storage
Microsoft SQL Server Flash StorageMark Ginnebaugh
 

More from Mark Ginnebaugh (20)

Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
 
Platfora - An Analytics Sandbox In A World Of Big Data
Platfora - An Analytics Sandbox In A World Of Big DataPlatfora - An Analytics Sandbox In A World Of Big Data
Platfora - An Analytics Sandbox In A World Of Big Data
 
Microsoft SQL Server Relational Databases and Primary Keys
Microsoft SQL Server Relational Databases and Primary KeysMicrosoft SQL Server Relational Databases and Primary Keys
Microsoft SQL Server Relational Databases and Primary Keys
 
DesignMind Microsoft Business Intelligence SQL Server
DesignMind Microsoft Business Intelligence SQL ServerDesignMind Microsoft Business Intelligence SQL Server
DesignMind Microsoft Business Intelligence SQL Server
 
San Francisco Bay Area SQL Server July 2013 meetings
San Francisco Bay Area SQL Server July 2013 meetingsSan Francisco Bay Area SQL Server July 2013 meetings
San Francisco Bay Area SQL Server July 2013 meetings
 
Silicon Valley SQL Server User Group June 2013
Silicon Valley SQL Server User Group June 2013Silicon Valley SQL Server User Group June 2013
Silicon Valley SQL Server User Group June 2013
 
Microsoft SQL Server Continuous Integration
Microsoft SQL Server Continuous IntegrationMicrosoft SQL Server Continuous Integration
Microsoft SQL Server Continuous Integration
 
Hortonworks Big Data & Hadoop
Hortonworks Big Data & HadoopHortonworks Big Data & Hadoop
Hortonworks Big Data & Hadoop
 
Microsoft SQL Server Physical Join Operators
Microsoft SQL Server Physical Join OperatorsMicrosoft SQL Server Physical Join Operators
Microsoft SQL Server Physical Join Operators
 
Microsoft PowerPivot & Power View in Excel 2013
Microsoft PowerPivot & Power View in Excel 2013Microsoft PowerPivot & Power View in Excel 2013
Microsoft PowerPivot & Power View in Excel 2013
 
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball Approach
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMicrosoft Data Warehouse Business Intelligence Lifecycle - The Kimball Approach
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball Approach
 
Fusion-io Memory Flash for Microsoft SQL Server 2012
Fusion-io Memory Flash for Microsoft SQL Server 2012Fusion-io Memory Flash for Microsoft SQL Server 2012
Fusion-io Memory Flash for Microsoft SQL Server 2012
 
Microsoft Data Mining 2012
Microsoft Data Mining 2012Microsoft Data Mining 2012
Microsoft Data Mining 2012
 
Microsoft SQL Server PASS News August 2012
Microsoft SQL Server PASS News August 2012Microsoft SQL Server PASS News August 2012
Microsoft SQL Server PASS News August 2012
 
Microsoft Mobile Business Intelligence
Microsoft Mobile Business Intelligence Microsoft Mobile Business Intelligence
Microsoft Mobile Business Intelligence
 
Microsoft SQL Server 2012 Cloud Ready
Microsoft SQL Server 2012 Cloud ReadyMicrosoft SQL Server 2012 Cloud Ready
Microsoft SQL Server 2012 Cloud Ready
 
Microsoft SQL Server 2012 Master Data Services
Microsoft SQL Server 2012 Master Data ServicesMicrosoft SQL Server 2012 Master Data Services
Microsoft SQL Server 2012 Master Data Services
 
Microsoft SQL Server Testing Frameworks
Microsoft SQL Server Testing FrameworksMicrosoft SQL Server Testing Frameworks
Microsoft SQL Server Testing Frameworks
 
Microsoft SQL Server - How to Collaboratively Manage Excel Data
Microsoft SQL Server - How to Collaboratively Manage Excel DataMicrosoft SQL Server - How to Collaboratively Manage Excel Data
Microsoft SQL Server - How to Collaboratively Manage Excel Data
 
Microsoft SQL Server Flash Storage
Microsoft SQL Server Flash StorageMicrosoft SQL Server Flash Storage
Microsoft SQL Server Flash Storage
 

Recently uploaded

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...apidays
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfOverkill Security
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Zilliz
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 

Recently uploaded (20)

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 

Microsoft SQL Server DBA as Protector of the Data - Oct 2010

  • 1. DBA AS PROTECTOR OF THE DATA: NOTES FROM THE FIELD Speaker: Denise McInerney Development DBA, Intuit San Francisco SQL Server User Group October 13, 2010 Mark Ginnebaugh, User Group Leader, mark@designmind.com
  • 2. Agenda g 2  Introduction  Goals  Topics  My POV  What is “bad” data? D i Design  Garbage in  Transactions  Change Management & Controls  Partnership  Attitude Copyright © 2010 Denise McInerney
  • 3. Who am I?  SQL Server DBA since 1998  Web-based OLTP applications  Focus on design and performance tuning  Development DBA www.intuitmarket.com  PASS volunteer since 2003  Founded Women in Tech chapter  Contact me  denise.mcinerney@sqlpass.org  denise_mcinerney@intuit.com  Twitter: @denisemc06 Copyright © 2010 Denise McInerney 3
  • 4. www.intuitcareers.com 4  Lead DBA  Openings in Menlo Park & p g  Sr. Database Engineer Mountain View, CA and require SQL experience  Senior Data Quality Analyst  Senior QA Developer  Follow Intuit Careers on F ll I t it C  Software Engineer in Quality FaceBook, Twitter and LinkedIn  Server Side Engineer for updates on job openings  Software E i S f Engineer  Apply directly online at  Architect www.intuitcareers.com  Performance Engineer  Test Automation Engineer  Questions on this job or any  Systems Engineer others? Post, Tweet or Message us on our Social Media ‘Intuit  Software Tester Careers Careers’ sites Copyright © 2010 Denise McInerney
  • 5. PASS 5  PASS Community Summit Nov 8 11 8-11  Best SQL Server training value  PASS Women in Tech  8th Annual Women in Tech Panel @ Summit  “Recruiting Recruiting, Retaining & Advancing Women in Technology: Why Does it Matter?”  http://wit.sqlpass.org  #passwit on Twitter Copyright © 2010 Denise McInerney
  • 6. My Goals y 6  Show you how to proactively ensure that the data in your transactional system is clean and correct GOING IN  Explain why an integrated approach is needed  Give Real-life examples Real life Copyright © 2010 Denise McInerney
  • 7. My Point of View y 7  Data protection intrinsic to the job  Backups, security—what about quality?  DBA s DBA’s approach different, complementary different  Broad exposure to the stack  Better at your job B j b  Morehighly valued, trusted S Successful f l Copyright © 2010 Denise McInerney
  • 8. Define “bad” data 8  Inaccurate  Missing  Misleading  Causes bugs B Bugs have consequences h  Breaks reports  Impacts customers I Copyright © 2010 Denise McInerney
  • 9. Design 9  Have one—even in an “agile” shop one even agile  It’s a system, not a collection of tables  Don t Don’t be lazy  Re-use a column  Just add a column  Just add a table  Bad design invites bad data Copyright © 2010 Denise McInerney
  • 10. Design g 10  Always assume the data will be used by others Copyright © 2010 Denise McInerney
  • 11. Bad Design Examples g p 11 Copyright © 2010 Denise McInerney
  • 12. Garbage In g 12  Failed INSERT…the silent killer  Order header, no detail  Order taken, not fulfilled  Missing records = no reconciliation  Concatenation  First name + last name in first name field  Zip code 5 + 4  Phone + phone extension h h  Duplicate orders, different order numbers Copyright © 2010 Denise McInerney
  • 13. Garbage In g 13  Default values substituted for real data  NULL <> ‘’ or “” Copyright © 2010 Denise McInerney
  • 14. Garbage In g 14 Copyright © 2010 Denise McInerney
  • 15. Transactions 15  Understand them  Don’t assume others understand them  BEGIN TRAN…COMMIT TRAN is not enough Copyright © 2010 Denise McInerney
  • 16. Transactions 16 Copyright © 2010 Denise McInerney
  • 17. Change Control g 17  Lots of ways data gets IN y g  Code—application & SQL  Version control  Scripts for everything!  Change management g g  Data updates  How do your lookup tables get populated? y p g p p  People  Who can update data directly?  Everyone takes short cuts
  • 18. It Takes a Village g 18
  • 19. Summary--Pieces Summary--Pieces of the Puzzle y 19  DBA’s job to guard data quality  Starts with design  Many types of “bad”  Transactions are crucial  Know how data gets in your database  Implement controls & processes  Cultivate relationships  Pessimism & vigilance Copyright © 2010 Denise McInerney
  • 20. To learn more or inquire about speaking opportunities, please contact: o ea o e o qu e about spea g oppo tu t es, p ease co tact: Mark Ginnebaugh, User Group Leader mark@designmind.com