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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
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
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
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
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
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
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
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
46. 47
Session Evaluations
ways to access
Go to passSummit.com Download the GuideBook App
and search: PASS Summit 2015
Follow the QR code link displayed
on session signage throughout the
conference venue and in the
program guide
Submit by 5pm
Friday November 6th to
WIN prizes
Your feedback is
important and valuable.
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
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