Transactional Replication exists in SQL Server since 1995; It is a feature that used correctly brings customization and scalability to your data. Considering your solutions from the data-flow perspective, SQL Server Transactional Replication allows you to move data (articles) across servers transparently to your ERP/LOB applications. In this session we will introduce a real customer scenario moving data from OLTP to DW server almost transparently. You will see how and where to make the changes/transformations to your data to addecuate to your business rules. To close the cicle you will see how to consume the data from SSAS, and how to customize it to near-real time sincronization.

1. Scale-out Scenarios with
Transactional Replication
Eladio Rincón
SolidQ, OLTP Director for Spain&Portugal
SQL Server MVP
eladio@solidq.com

2. The Sponsors

3. The Volunteers
 They spend their FREE time to give you this
event. (2 months per person)
 Because they are crazy.
 Because they want YOU
to learn from the BEST IN THE WORLD.
 If you see a guy with “STAFF” on their back –
buy them a beer, they deserve it.

4. Paulo Matos:

5. Paulo Borges:

6. João Fialho:

7. Bruno Basto:

8. Upcoming SQL Server events:
XXXIII Encontro da Comunidade SQLPort
Data Evento: 23 Abril 2013 - 18:30
Local do Evento: Auditório Microsoft, Parque das Nações, Lisboa
18:30 - Abertura e recepção.
19:10 - "Analyzing Twitter Data" - Niko Neugebauer (SQL Server MVP, Community Evangelist –
PASS)
20:15 - Coffee break
20:30 - "First Approach to SQL Server Analysis Services" - João Fialho (Consultor BI Independente)
21:30 - Sorteio de prémios
XXXIV Encontro da Comunidade SQLPort
Data Evento: 7 Maio 2013 - 19:00
Local do Evento: Porto
18:30 - Abertura e recepção.
19:00 - «Apresentação para Developers» - para definir
20:15 - Coffee break
20:30 - «Apresentação para definir» - para definir
21:30 - Sorteio de prémios

9. Eladio Rincón
 OLTP Director @ SolidQ Spain & Portugal
 SQL Server MVP since 2003
 Manages with other MVPs PASS Spanish
Chapter
 What I do?
 Designing HA and DR solutions
 Troubleshooting and Optimization
 Complex Upgrade and Migration projects
 Datawarehousing on PDW and Fast Track DW

10. Agenda
 The business Case to Improve
 Transactional Replication Concepts
 Demo: Seting up Transactional Replication
 Demo: Applying Business Logic (Transform)
 Demo: Consuming Data (Query)

11. Business Case to Improve
 Processing Type
 Online (OLTP)
 Analytical (BI / DW)
 Batches (mix OLTP y BI)
 Resources needed
 IOPS – IO Subsystem
 Volume – IO Subsystem
 Processing – CPU
 Concurrency – Apps

12. Proposed Architecture
Roles
Diversification

13. Proposed Architecture
Cons Pros
 Objects Location  Scalability
 Data in Several Servers  Scale-out
 Sync-ing Objects  Async Objects
 Data Coordination Processing
 Business Rules and  Non Real Time
Processing Rules Processing
 Might need to process in  Resources Fine-
several servers Allocation

14. Proposed Architecture:
Technology
 Transactional Replication
 Allocate the Data in Different Servers/Sites to:
 Async Processing
 Ad-hoc Reporting
 Data Aggregation
 SQL Server Analysis Services
 Data Aggretation Strenghts
 Client Tools for Querying (Excel  Self-Service BI)
 In Multi-Dimensional  Proactive Caching

15. Transactional Replication – Concepts

16. Transactional Replication – Concepts

17. Transactional Replication: Demo Scenario
Publisher Subscriptor
SNUCKI9SQL2012 SNUCKI9SQL2012DEV
Source DB Destination DB
AdventureWorksLT2012 MyDW
Source Table Destination Table
SalesOrderDetail SalesOrderDetail
SalesOrderHeader SalesOrderHeader

18. Setup
Transactional
Replication

19. Applying Business Logic to the
Distributed Data
Replication

20. Applying
BL to
Distributed Data

21. Consuming Data
 Multi-Dimensional or Tabular Models
 Pre-calculated data
 Less resources usage (CPU, IO)
 Periodical refresh: what business says
 SQL Server Agent jobs
 Proactive Caching (notifications)
 Data Consumption
 Excel or Reporting Services
 Flexible vs less-flexible

22. Consuming
Data

23. Following these Techniques
Servers 1 Servers 2
Procs 32 Procs 32 (Agg)
Memory 128GB Memory 64GB (Agg)
CPU Consumpt. +80% avg CPU Consumpt. 25% +30%
SAN High SAN Low
Batches/sec 1400 Batches/sec 2600
Activity Activity
OLTP 40% OLTP 30%
BI-Low 25% BI-Low 30%
BI-Medium 30% BI-Medium 35%

24. Final Thoughts
 Combine existing Technologies
 Partitioning, Replication, AlwaysOn, Log Shipping
 Improving the Infra (hardware) really helps (ROI)
 Memory at very atractive prices
 CPU and IO nice price
 Escalability vs Architecture
 Design your solution (Software) with Escalability in mind
 Adjust the Technology to your Solution needs (Software)

25. Eladio Rincón
eladio@solidq.com
www.elrincondelDBA.com
www.SolidQ.com