1. Azure SQL Data Warehouse for SQL Server DBAS
August 2017
Warner Chaves
SQL MCM/ Data Platform MVP

2. Bio DBA and Consultant for 11 years
Previously L3 DBA at HP in Costa Rica, now Principal Consultant at Pythian in Ottawa, Ontario.
Microsoft Data Platform MVP.
Blog: Sqlturbo.com
Company: Pythian.com

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4. Agenda
Objective: cover Azure SQL Data Warehouse in a way that is easy to understand and adopt for SQL Server DBAs
We will go over:
Why Data Warehousing in the cloud?
Service Cost and Model
Fundamental differences with SQL Server
Loading and querying data

5. Pre-requisites
Sql Server experience.
Basic Data Warehousing concepts.

6. Cloud vs Traditional Data Warehousing
Significant upfront investment
Capacity is forecasted and fixed
Client needs to manage the solution
Static or semi-static software
Client needs to complete the ecosystem
Predictable recurring bill
Dynamic capacity
Solution managed by the provider
Software in continuous improvement
Tightly integrated with the rest of cloud services

7. So what is Azure SQL DW? Microsoft Azure Service
Successor to the on-premises appliance known as APS/PDW
Targeted at running multi-TB Data Warehousing workloads
It’s a PaaS service – DWaaS (AWS RedShift – Google BigQuery)
It’s an MPP (Massively Parallel Processing) system
Compute is distributed
Storage is distributed

8. SMP vs MPP
Symmetric MultiProcessing
Massively Parallel Processing

9. Azure SQL DW Connection Client Control Node Compute Nodes
Data Movement Service
Connection
Client
Control Node
Compute Nodes
Distributions

10. Service Model Compute and Storage are scaled and billed separately
Compute is measured in Data Warehousing Units
The DWU control the capacity of the Compute Nodes
Storage is billed in 1TB increments
The service allows you to PAUSE compute and stop getting charged for it

11. Backup and Recovery The service keeps backups for 7 days
A snapshot is made every 4 to 8 hours
In case of DR, you can do a geo-restore to a ‘paired datacenter’ with the daily backup
If you need to retain a copy for more than 7 days, right now the option is to do a restore and then pause compute so you’re only paying for storage (we’re hoping for improvements in this regard…)

12. How is the engine different from SQL Server?

13. Distribution Method
Most important concept for good performance in Azure SQL DW.
It determines the way ASDM will distribute the records in different buckets.
There are three methods:
HASH distribution
Round-Robin distribution
Replicated

14. Hash Distribution Same values end up in the same bucket.
If the distribution column is used in joins or for a Group By then no data movement is necessary.
If a particular value is dominant in the table then a distribution can be overloaded compared to the other ones and lower system performance.

15. Overloaded distribution

16. Round Robin Distribution
ASDW simply does a Round-Robin over the records and puts each record in a different bucket.
The values in the record don’t matter when assigning a bucket.
Data movement is required for most operations.
If a table doesn’t have a good HASH column and is too big to be a replicated table then this can be the best option.
If a value is skewed, the distribution will still be uniform.

17. Replicated Distribution
The table is copied to each compute node.
Recommended for tables smaller than 2GB.
For smaller tables that are usually part of join predicates.
For simple predicates like equality or inequality.
The storage is table size X amount of compute nodes so don’t abuse it.

18. T-SQL Differences
ASDW encourages the use of the CTAS (Create Table AS) construct
Fully Parallel
Logging is minimized
Joins on UPDATE – DELETE not supported (there are workarounds)
MERGE not supported (for now at least)
Statistics creation and updates is not automatic (for now…)
Some of the complex data types are not present (geography, geometry, hierarchy, xml)
Full list here:

19. DEMO: Portal and Metadata

20. Data Warehouse Design
Good service to consider if your DW is at 1TB+ and growing.
Default table type is Clustered Columnstore.
Ideal columnstore segment is 1 million records (same as SQL Server).
ASDW uses 60 Distributions.
Fact Tables: Columnstores (optionally with Partitioning) with HASH distribution (if possible)
Dimension Tables: B-Tree o Columnstore (if it’s a large dimension) HASH, Replicated or Round-Robin.

21. The thing about Partitioning Daily
365 partitions
60 distributions
21900 partitions
1 million is the ideal segment
records

22. Partitioning usually at the weekly or monthly level if necessary

23. The thing about Partitioning Monthly
12 partitions
60 distributions
720 partitions
1 million is the ideal segment
records

24. Data Loading Two ways of loading data: Control Node Methods
Through the Control Node
PolyBase
Control Node Methods
SSIS
BCP
Loads from Blob Storage or Azure Data Lake
Parallel multi-threaded load that does not go through the Control Node
For large data loads the Control Node can become a bottleneck

25. DEMO: Loading data with PolyBase

26. Querying Data
Azure SQL DW has some differences in terms of query execution.
There are concurrency limits depending on the DWUs.
There are transaction size limits per Distribution also based off DWUs.
Each user gets assigned a resource class to determine how much compute they get.
Some DMVs keep historical information.
The use of Query Labels is recommended for troubleshooting and monitoring.

27. Query execution is queued if necessary
Concurrency Limits
DWUs
100
200
300
400
500
600
1000+
Concurrent Queries 4 8 12 16 20 24 32
Query execution is queued if necessary

28. Memory assigned is per distribution
Resource Classes
CLASS
SMALL
MEDIUM
LARGE
X-LARGE
Default X
Memory
100MB
Up to 3200MB
Up to 6400MB
Up to 12800MB
Memory assigned is per distribution

29. OPTION (LABEL = 'QuantitySum');
Query Label
SELECT sum(Quantity)
FROM FactTransactionHistory
OPTION (LABEL = 'QuantitySum');

30. DEMO: Querying Data

31. Questions?

32. Thanks!!