The document discusses big data analytics using Microsoft's Azure cloud services, emphasizing the role of various tools and components such as data factories, machine learning, and Azure Data Lake. It outlines the benefits of cloud-based analytics including scalability, cost-effectiveness, and rapid architecture evolution. Additionally, it details technologies like U-SQL for data preparation and Azure HDInsight for processing large data sets with Hadoop and Spark.

1. Big Data Analytics in the
Cloud
Microsoft Azure
Cortana Intelligence Suite
Mark Kromer
Microsoft Azure Cloud Data Architect
@kromerbigdata
@mssqldude

2. What is Big Data Analytics?
Tech Target: “… the process of examining large data sets to uncover hidden patterns, unknown correlations, market
trends, customer preferences and other useful business information.”
Techopedia: “… the strategy of analyzing large volumes of data, or big data. This big data is gathered from a wide
variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The
aim in analyzing all this data is to uncover patterns and connections that might otherwise be invisible, and that
might provide valuable insights about the users who created it. Through this insight, businesses may be able to
gain an edge over their rivals and make superior business decisions.”
2
 Requires lots of data wrangling and Data Engineers
 Requires Data Scientists to uncover patterns from
complex raw data
 Requires Business Analysts to provide business value
from multiple data sources
 Requires additional tools and infrastructure not
provided by traditional database and BI technologies
Why Cloud for Big Data Analytics?
• Quick and easy to stand-up new, large, big data architectures
• Elastic scale
• Metered pricing
• Quickly evolve architectures to rapidly changing landscapes

3. Azure Data Platform-at-a-glance

4. Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive
Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine
Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data

5. What it is:
When to use it:
Microsoft’s Cloud Platform including IaaS, PaaS and SaaS
• Storage and Data
• Networking
• Security
• Services
• Virtual Machines
• On-demand Resources and Services

6. What it is:
When to use it:
A pipeline system to move data in, perform activities on
data, move data around, and move data out
• Create solutions using multiple tools as a single process
• Orchestrate processes - Scheduling
• Monitor and manage pipelines
• Call and re-train Azure ML models

7. ADF Components

8. ADF Logical Flow

9. Example - Churn
Call Log Files
Customer Table
Call Log Files
Customer Table
Customer
Churn Table
Azure Data
Factory:
Data Sources
Customers
Likely to
Churn
Customer
Call Details
Transform & Analyze PublishIngest

10. Simple ADF:
• Business Goal: Transform and Analyze Web Logs each month
• Design Process: Transform Raw Weblogs, using a Hive Query,
storing the results in Blob Storage
Web Logs
Loaded to
Blob
Files ready
for analysis
and use in
AzureML
HDInsight HIVE query
to transform Log
entries

11. PowerShell ADF Example
1. Add-AzureAccount and enter the user name and password
2. Get-AzureSubscription to view all the subscriptions for this
account.
3. Select-AzureSubscription to select the subscription that you
want to work with.
4. Switch-AzureMode AzureResourceManager
5. New-AzureResourceGroup -Name
ADFTutorialResourceGroup -Location "West US"
6. New-AzureDataFactory -ResourceGroupName
ADFTutorialResourceGroup –Name DataFactory(your
alias)Pipeline –Location "West US"

12. Using Visual Studio
• Use in mature dev environments
• Use when integrated into larger development
process

13. What it is:
When to use it:
A Scaling Data Warehouse Service in the Cloud
• When you need a large-data BI solution in the cloud
• MPP SQL Server in the Cloud
• Elastic scale data warehousing
• When you need pause-able scale-out compute

14. Elastic scale & performance
Real-time elasticity
Resize in <1 minute On-demand compute
Expand or reduce
as needed
Pause Data Warehouse to Save
on Compute Costs. I.e. Pause
during non-business hours

15. Storage can be as big or
small as required
Users can execute niche workloads
without re-scanning data
Elastic scale & performance
Scale

18. SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
SELECT COUNT_BIG(*)
FROM dbo.[FactInternetSales];
Compute
Control

19. What it is:
When to use it:
Data storage (Web-HDFS) and Distributed Data Processing (HIVE,
Spark, HBase, Storm, U-SQL) Engines
• Low-cost, high-throughput data store
• Non-relational data
• Larger storage limits than Blobs

20. Ingest all data
regardless of
requirements
Store all data
in native format
without schema
definition
Do analysis
Using analytic
engines like Hadoop
and ADLA
Interactive queries
Batch queries
Machine Learning
Data warehouse
Real-time analytics
Devices

21. WebHDFS
YARN
U-SQL
ADL Analytics ADL HDInsight
Store
HiveAnalytics
Storage

22. No limits to SCALE
Store ANY DATA in its native format
HADOOP FILE SYSTEM (HDFS) for the
cloud
Optimized for analytic workload
PERFORMANCE
ENTERPRISE GRADE authentication,
access control, audit, encryption at rest
Azure Data Lake
Store
A hyper scale repository for big
data analytics workloads
Introducing ADLS

23. • No fixed limits on:
• Amount of data stored
• How long data can be stored
• Number of files
• Size of the individual files
• Ingestion/egress throughput
Seamlessly scales
from a few KBs
to several PBs
No limits to scale

24. No limits to storage
24
• Each file in ADL Store is sliced into blocks
• Blocks are distributed across multiple data
nodes in the backend storage system
• With sufficient number of backend storage
data nodes, files of any size can be stored
• Backend storage runs in the Azure cloud
which has virtually unlimited resources
• Metadata is stored about each file
No limit to metadata either.
Azure Data Lake Store file
…Block 1 Block 2 Block 2
Backend Storage
Data node Data node Data node Data node Data nodeData node
Block Block Block Block Block Block

25. Massive throughput
25
• Through read parallelism ADL Store provides
massive throughput
• Each read operation on a ADL Store file
results in multiple read operations executed
in parallel against the backend storage data
nodes
Read operation
Azure Data Lake Store file
…Block 1 Block 2 Block 2
Backend storage
Data node Data node Data node Data node Data nodeData node
Block Block Block Block Block Block

26. Enterprise grade security
Enterprise-grade security permits even
sensitive data to be stored securely
Regulatory compliance can be enforced
Integrates with Azure Active Directory for
authentication
Data is encrypted at rest and in flight
POSIX-style permissions on files and
directories
Audit logs for all operations
26

27. Enterprise grade availability and reliability
27
• Azure maintains 3 replicas of each data
object per region across three fault and
upgrade domains
• Each create or append operation on a replica
is replicated to other two
• Writes are committed to application only
after all replicas are successfully updated
• Read operations can go against
any replica
• Provides ‘read-after-write’ consistency
Data is never lost or unavailable
even under failures
Replica 1
Replica 2 Replica 3
Fault/upgrade
domains
Write Commit

28. Enterprise-
grade
Limitless scaleProductivity
from day one
Easy and
powerful data
preparation
All data
0100101001000101010100101001000
10101010010100100010101010010100
10001010101001010010001010101001
0100100010101010010100100010101
0100101001000101010100101001000
10101010010100100010101010010100
10001010101001010010001010101001
0100100010101010010100100010101
0100101001000101010100101001000
10101010010100100010101010010100

29. Developing big data apps
Visual Studio
U-SQL, Hive, & Pig
NET

30. Work across all cloud data
Azure Data Lake
Analytics
Azure SQL DW Azure SQL DB
Azure
Storage Blobs
Azure
Data Lake Store
SQL DB in an
Azure VM

31. Simplified management and administration

32. What is
U-SQL?
A hyper-scalable, highly extensible
language for preparing, transforming
and analyzing all data
Allows users to focus on the what—
not the how—of business problems
Built on familiar languages (SQL and
C#) and supported by a fully integrated
development environment
Built for data developers & scientists
32

33. REFERENCE MyDB.MyAssembly;
CREATE TABLE T( cid int, first_order DateTime
, last_order DateTime, order_count int
, order_amount float );
@o = EXTRACT oid int, cid int, odate DateTime, amount float
FROM "/input/orders.txt“
USING Extractors.Csv();
@c = EXTRACT cid int, name string, city string
FROM "/input/customers.txt“
USING Extractors.Csv();
@j = SELECT c.cid, MIN(o.odate) AS firstorder
, MAX(o.date) AS lastorder, COUNT(o.oid) AS ordercnt
, SUM(c.amount) AS totalamount
FROM @c AS c LEFT OUTER JOIN @o AS o ON c.cid == o.cid
WHERE c.city.StartsWith("New")
&& MyNamespace.MyFunction(o.odate) > 10
GROUP BY c.cid;
OUTPUT @j TO "/output/result.txt"
USING new MyData.Write();
INSERT INTO T SELECT * FROM @j;

34. Automatic
optimized out-of-
the-box
user-driven
visibility
Heat map
010010
100100
010101

35. EXTRACT Expression
@s = EXTRACT a string, b int
FROM "filepath/file.csv"
USING Extractors.Csv(encoding: Encoding.Unicode);
• Built-in Extractors: Csv, Tsv, Text with lots of options
• Custom Extractors: e.g., JSON, XML, etc.
OUTPUT Expression
OUTPUT @s
TO "filepath/file.csv"
USING Outputters.Csv();
• Built-in Outputters: Csv, Tsv, Text
• Custom Outputters: e.g., JSON, XML, etc. (see http://usql.io)
Filepath URIs
• Relative URI to default ADL Storage account: "filepath/file.csv"
• Absolute URIs:
• ADLS: "adl://account.azuredatalakestore.net/filepath/file.csv"
• WASB: "wasb://container@account/filepath/file.csv"

36. • Create assemblies
• Reference assemblies
• Enumerate assemblies
• Drop assemblies
• VisualStudio makes registration easy!
• CREATE ASSEMBLY db.assembly FROM @path;
• CREATE ASSEMBLY db.assembly FROM byte[];
• Can also include additional resource files
• REFERENCE ASSEMBLY db.assembly;
• Referencing .Net Framework Assemblies
• Always accessible system namespaces:
• U-SQL specific (e.g., for SQL.MAP)
• All provided by system.dll system.core.dll
system.data.dll, System.Runtime.Serialization.dll,
mscorelib.dll (e.g., System.Text,
System.Text.RegularExpressions, System.Linq)
• Add all other .Net Framework Assemblies with:
REFERENCE SYSTEM ASSEMBLY [System.XML];
• Enumerating Assemblies
• Powershell command
• U-SQL Studio Server Explorer
• DROP ASSEMBLY db.assembly;

37. 'USING' csharp_namespace
| Alias '=' csharp_namespace_or_class.
Examples:
DECLARE @ input string = "somejsonfile.json";
REFERENCE ASSEMBLY [Newtonsoft.Json];
REFERENCE ASSEMBLY [Microsoft.Analytics.Samples.Formats];
USING Microsoft.Analytics.Samples.Formats.Json;
@data0 =
EXTRACT IPAddresses string
FROM @input
USING new JsonExtractor("Devices[*]");
USING json =
[Microsoft.Analytics.Samples.Formats.Json.JsonExtractor];
@data1 =
EXTRACT IPAddresses string
FROM @input
USING new json("Devices[*]");

38. Simple pattern language on filename and path
@pattern string =
"/input/{date:yyyy}/{date:MM}/{date:dd}/{*}.{suffix}";
• Binds two columns date and suffix
• Wildcards the filename
• Limits on number of files
(Current limit 800 and 3000 being increased in next refresh)
Virtual columns
EXTRACT
name string
, suffix string // virtual column
, date DateTime // virtual column
FROM @pattern
USING Extractors.Csv();
• Refer to virtual columns in query predicates to get partition
elimination
(otherwise you will get a warning)

39. • Naming
• Discovery
• Sharing
• Securing
U-SQL Catalog Naming
• Default Database and Schema context: master.dbo
• Quote identifiers with []: [my table]
• Stores data in ADL Storage /catalog folder
Discovery
• Visual Studio Server Explorer
• Azure Data Lake Analytics Portal
• SDKs and Azure Powershell commands
Sharing
• Within an Azure Data Lake Analytics account
Securing
• Secured with AAD principals at catalog and Database level

40. Views
CREATE VIEW V AS EXTRACT…
CREATE VIEW V AS SELECT …
• Cannot contain user-defined objects (e.g. UDF or UDOs)!
• Will be inlined
Table-Valued Functions (TVFs)
CREATE FUNCTION F (@arg string = "default")
RETURNS @res [TABLE ( … )]
AS BEGIN … @res = … END;
• Provides parameterization
• One or more results
• Can contain multiple statements
• Can contain user-code (needs assembly reference)
• Will always be inlined
• Infers schema or checks against specified return schema

41. CREATE PROCEDURE P (@arg string = "default“) AS
BEGIN
…;
OUTPUT @res TO …;
INSERT INTO T …;
END;
• Provides parameterization
• No result but writes into file or table
• Can contain multiple statements
• Can contain user-code (needs assembly reference)
• Will always be inlined
• Can contain DDL (but no CREATE, DROP FUNCTION/PROCEDURE)

42. CREATE TABLE T (col1 int
, col2 string
, col3 SQL.MAP<string,string>
, INDEX idx CLUSTERED (col2 ASC)
PARTITION BY (col1)
DISTRIBUTED BY HASH (driver_id)
);
• Structured Data, built-in Data types only (no UDTs)
• Clustered Index (needs to be specified): row-oriented
• Fine-grained distribution (needs to be specified):
• HASH, DIRECT HASH, RANGE, ROUND ROBIN
• Addressable Partitions (optional)
CREATE TABLE T (INDEX idx CLUSTERED …) AS SELECT …;
CREATE TABLE T (INDEX idx CLUSTERED …) AS EXTRACT…;
CREATE TABLE T (INDEX idx CLUSTERED …) AS myTVF(DEFAULT);
• Infer the schema from the query
• Still requires index and distribution (does not support partitioning)

43. Benefits of Table clustering and distribution
• Faster lookup of data provided by distribution and clustering when right
distribution/cluster is chosen
• Data distribution provides better localized scale out
• Used for filters, joins and grouping
Benefits of Table partitioning
• Provides data life cycle management (“expire” old partitions)
• Partial re-computation of data at partition level
• Query predicates can provide partition elimination
Do not use when…
• No filters, joins and grouping
• No reuse of the data for future queries

44. ALTER TABLE T ADD COLUMN eventName string;
ALTER TABLE T DROP COLUMN col3;
ALTER TABLE T ADD COLUMN result string, clientId string,
payload int?;
ALTER TABLE T DROP COLUMN clientId, result;
• Meta-data only operation
• Existing rows will get
• Non-nullable types: C# data type default value (e.g., int will be 0)
• Nullable types: null

45. Windowing Expression
Window_Function_Call 'OVER' '('
[ Over_Partition_By_Clause ]
[ Order_By_Clause ]
[ Row _Clause ]
')'.
Window_Function_Call :=
Aggregate_Function_Call
| Analytic_Function_Call
| Ranking_Function_Call.
Windowing Aggregate Functions
ANY_VALUE, AVG, COUNT, MAX, MIN, SUM, STDEV, STDEVP, VAR, VARP
Analytics Functions
CUME_DIST, FIRST_VALUE, LAST_VALUE, PERCENTILE_CONT,
PERCENTILE_DISC, PERCENT_RANK, LEAD, LAG
Ranking Functions
DENSE_RANK, NTILE, RANK, ROW_NUMBER

46. • Automatic "in-lining"
optimized out-of-
the-box
• Per job
parallelization
visibility into execution
• Heatmap to identify
bottlenecks

47. Visual Studio integration

48. Visualize and
replay progress
of job
Fine-tune query
performance
Visualize physical
plan of U-SQL
query
Browse metadata
catalog
Author U-SQL
scripts (with
C# code)
Create metadata
objects
Submit and cancel
U-SQL Jobs
Debug U-SQL and
C# code
48

49. Plug-in

50. Visual Studio fully supports
authoring U-SQL scripts
While editing, it provides:
IntelliSense
Syntax color coding
Syntax checking
…
Contextual
Menu
50

51. C# code to extend U-SQL can be authored and used directly in U-SQL Studio,
without having to first creating and registering an external assembly.
Custom
Processor
51

52. Jobs can be submitted directly from Visual Studio in two ways
You have to be logged into Azure and have to specify the target Azure Data Lake account.

53. Each job is broken into ‘n’ number
of vertices
Each vertex is some work that
needs to be done
Input
Output
Output
6 Stages
8 Vertexes
Vertexes are organized into stages
– Vertexes in each stage do the same
work on the same data
– Vertex in one stage may depend on a
vertex in a earlier stage
Stages themselves are organized into
an acyclic graph
53

54. Job execution graph
After a job is submitted
the progress of the
execution of the job as
it goes through the
different stages is
shown and updated
continuously
Important stats about
the job are also
displayed and updated
continuously
54

55. Diagnostics
information is shown
to help with
debugging and
performance issues

56. ADL Analytics creates and stores a set of metadata objects in a catalog
maintained by a metadata service
Tables and TVFs are created by DDL statements
(CREATE TABLE …)
Metadata objects can be created directly through the Server Explorer
Azure Data Lake Analytics account
Databases
– Tables
– Table valued functions
– Jobs
– Schemas
Linked storage

57. The metadata catalog can
be browsed with the Visual
Studio Server Explorer
Server Explorer lets you:
1. Create new tables, schemas
and databases
2. Register assemblies

58. What it is:
When to use it:
Microsoft’s implementation of apache Hadoop (as a
service) that uses Blobs for persistent storage
• When you need to process large scale data (PB+)
• When you want to use Hadoop or Spark as a service
• When you want to compute data and retire the servers,
but retain the results
• When your team is familiar with the Hadoop Zoo

59. Using the Hadoop Ecosystem to
process and query data

60. HDInsight Tools for Visual Studio

66. What it is:
When to use it:
A multi-platform environment and engine to create and
deploy Machine Learning models and API’s
• When you need to create predictive analytics
• When you need to share Data Science experiments
across teams
• When you need to create call-able API’s for ML functions
• When you also have R and Python experience on your
Data Science team

67. Development Environment
• Creating Experiments
• Sharing a Workspace
Deployment Environment
• Publishing the Model
• Using the API
• Consuming in various tools

68. Get/Prepare
Data
Build/Edit
Experiment
Create/Update
Model
Evaluate Model
Results
Build and ModelCreate
Workspace
Deploy
Model
Consume
Model

69. Import Data
Preprocess
Algorithm
Train Model
Split Data
Score Model

72. What it is:
When to use it:
Interactive Report and Visualization creation for
computing and mobile platforms
• When you need to create and view interactive reports
that combine multiple datasets
• When you need to embed reporting into an application
• When you need customizable visualizations
• When you need to create shared datasets, reports, and
dashboards that you publish to your team

73. Common architectural patterns

75. Business
apps
Custom
apps
Sensors
and devices
Events Events Transformed
Data
Raw Events
Azure Event
Hubs
Kafka

76. Business
apps
Custom
apps
Sensors
and devices
Bulk Load
Azure Data Factory

77. Data
Transformation
Data
Collection
Presentation
and action
Queuing
System
Data Storage
8
Azure Search
Data analytics (Excel,
Power BI, Looker,
Tableau)
Web/thick client
dashboards
Devices to take action
Event hub
Event & data
producers
Applications
Web and social
Devices
Live Dashboards
DocumentDB
MongoDB
SQL Azure
ADW
Hbase
Blob StorageKafka/RabbitMQ/
ActiveMQ
Event hubs Azure ML
Storm / Stream
Analytics
Hive / U-SQL
Data Factory
Sensors
Pig
Cloud gateways
(web APIs)
Field
gateways

78. Get started
today!
http://aka.ms/cisolutions
79
Cortana Intelligence Solutions

79. http://aka.ms/cisolutions