We talk about DocumentDB, Azure Table Storage, Azure Blob Storage, Azure SQL Database, Azure SQL Data Warehouse, and Azure Data Lake.

1. Survey of the
Microsoft Azure Data
Landscape
Ike Ellis, Partner, Crafting Bytes

2. 2
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4. Agenda
• Azure Blob Storage
• Azure Table Storage
• Azure DocumentDB
• Azure SQL Database
• Azure SQL in a VM
• Azure SQL Data Warehouse
• Azure Data Lake
• Lots of other things supported:
• Postgres, MySQL, MongoDB, Redis
4

5. Topic Agenda
• What is it?
• How is it used?
• What are the competitors?
• DEMO!
5

6. Azure Blob Storage
6

7. Azure Blob Storage
• Blobs are files (PDFs, JPGs, DOCs, etc)
• Highly durable, massively scalable
• More than 40 trillion stored objects
• 3.5+ Million requests/second
• Exposed via REST APIs
• Use them in .NET, C++, Java, Node.JS, Android…
• AzCopy, PowerShell
7

8. Blob Storage Fault Tolerance & Scalability
8

9. What kind of blobs can I have?
• Share files with clients
• off-load static content from web servers (invoices, contracts, resumes)
• Azure Websites – Platform as a Service – no files on a webserver
• SQL BAK Files
• VM Hard Drives
9

10. Competitors?
• On premise SANS and arrays
• Amazon S3 Blob Storage
10

11. Azure Blob
Storage
Demo

12. Azure Table Storage
• Much of it similar to Azure Blob Storage
• Same scalability & redundancy
• Affordable price
• Very, very fast
• NoSQL key value pair solution
• Quick data retrieval, little configuration
12

13. 13

14. 14

15. Competitors
• Amazon DynamoDB Table Storage
15

16. Azure Table
Storage
Demo

17. JSON Document
Standard for passing data between a server and a web
application
Replacement for XML
Hierarchical
Terse
Simple data types

18. Modeling in DocumentDB
{
"id": "1",
"firstName": "Thomas",
"lastName": "Andersen",
"addresses": [
{
"line1": "100 Some Street",
"line2": "Unit 1",
"city": "Seattle",
"state": "WA",
"zip": 98012
}
],
"contactDetails": [
{"email: "thomas@andersen.com"},
{"phone": "+1 555 555-5555", "extension": 5555}
]
}
Reading is one operation
Writing is one operation
No assembly de-assembly

19. Query Playground
http://www.documentdb.com/sql/demo

20. • MongoDB
• Amazon DynamoDB
Competitors
20

21. 22

22. Azure SQL Database
• Platform as a Service
• All data is backed up for you
• Point in time restore
• Can be geo-redundant
• Scalable both in performance and in data size
• Up to 1TB
• Not feature complete with SQL Server in a VM

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Database Replicas
Replica 1
Replica 2
Replica 3
DB

24. https://azure.microsoft.com/en-
us/documentation/articles/sql-database-transact-sql-
information/
Azure SQL Database Unsupported Features
25

25. 26
You can
also make
it scale
up!

26. Amazon RDS
Competitors
27

27. Azure SQL
Database
Demo

28. • You manage backups
• You create fault tolerant options
• You manage disk space
• You manage patching
• You don’t manage hardware failure
• You don’t manage purchasing hardware
• You don’t manage networking infrastructure
Azure SQL Server in a VM
29

29. • Use Premium Storage.
• Use a VM size of DS3 or higher for SQL Enterprise edition and DS2
or higher for SQL Standard edition.
• Use a minimum of 2 P30 disks (1 for log files; 1 for data files
and TempDB).
• Keep the storage accountand SQL Server VM in the same region.
• Disable Azure geo-redundant storage (geo-replication) on the
storage account.
• Avoid using operating system or temporary disks for database
storage or logging.
Performance Considerations
30

30. • Back up to Azure Blob Storage
• Use Always on Availability Groups and Windows Failover
Clustering Services (WFCS) for fault tolerance
• Can use mirroring or log shipping, too
• Can also mix in on-premise
Backups & Fault Tolerance
31

31. Amazon EC2 – VMs in the cloud
Competitors
32

32. • Elastic Massively Parallel Processing System
• Use T-SQL to query across relational and non-relational
data
• Up to petabyte volumes of data
• Scale compute separately from data
• When paused, you only pay for storage
• Deploys in seconds
Azure SQL Data Warehouse
33

33. • Supports 32 concurrent queries
• Used for fanning out queries over multiple machines for
processing/aggregation/analytics
• Performance becomes far more predictable than with
just straight SQL Server
• Not used in OLTP environments
Azure SQL Data Warehouse
34

34. • A unit of scale that determines how much hardware will
give great performance
• Done in increments of 100 (mostly)
• How many DTUs?
• Start Small
• Monitor
• Change as needed, it’s instant
What is a DTU (Data Warehouse Unit)?
35
ALTER DATABASE MySQLDW MODIFY (SERVICE_OBJECTIVE = 'DW1000') ;

35. Two choices:
• Distribute data based on hashing values from a single
column
• Good if clusters of tables will be joined and are related
• Distribute data evenly but randomly
• Fail-safe method
Partitioning Data
36

36. 37

37. Non-supported data types
38
•geometry, use a varbinary type
•geography, use a varbinary type
•hierarchyid, CLR type not native
•image, text, ntext when text based use varchar/nvarchar (smaller the better)
•nvarchar(max), use varchar(4000) or smaller for better performance
•numeric, use decimal
•sql_variant, split column into several strongly typed columns
•sysname, use nvarchar(128)
•table, convert to temporary tables
•timestamp, re-work code to use datetime2 and CURRENT_TIMESTAMP function.
•varchar(max), use varchar(8000) or smaller for better performance
•uniqueidentifier, use varbinary(8)
•user defined types, convert back to their native types where possible
•xml, use a varchar(8000) or smaller for better performance - split across columns if needed

38. • primary keys
• foreign keys
• check constraints
• unique constraints
• unique indexes
• computed columns
• sparse columns
• user-defined types
• indexed views
• identities
• sequences
• triggers
• synonyms
Unsupported Features
39

39. Amazon RedShift
Competitors
40

40. Azure SQL
Data
Warehouse
Demo

41. HDFS for the cloud
Can use tools like Spark, Storm, Flume, Sqoop, Kafka, etc.
No fixed limits on account size or file size
Azure Data Lake
42

42. • An enterprise wide repository of every type of data
collected in a single place
• Prior to any formal definition of requirements or schema.
Allows every type of data to be kept without
discrimination Organizations can then use Hadoop or
advanced analytics to find patterns of the data.
• Serve as a repository for lower cost data preparation
prior to moving curated data into a data warehouse.
What is a generic data lake?
43

43. • Azure Data Lake Store – Built on HDFS
• Azure Data Lake Analytics – Built on Yarn. Introduces U-
SQL
Products
44

44. A lot of Hadoop implementations, but nothing really quite
like it
Competitors
45

45. • MongoDB
• PostGres
• Redis
• MySQL
• Oracle
More data options….
46

46. 47
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47. Ike Ellis
Crafting Bytes
• Small San Diego Software Studio
• Modern web, mobile, Azure, SQL Server
• Looking for future teammates!
Book: Developing Azure Solutions
Podcast Guest: Talk Python to Me – Dec 2015
.NET Rocks – Sept 2015
• www.craftingbytes.com
• blog.ikeellis.com
• www.ikeellis.com
• SDTIG – www.sdtig.com
Ike Ellis, MVP
@ike_ellis
619.922.9801
ike@craftingbytes.com