1. 9/12/ :12 PM
BRK3323
Modernizing ETL with Azure Data Lake: Hyperscale, multi-format, multi-platform, and intelligent
Michael Rys
Principal Program Manager, Big Data Team
@MikeDoesBigData
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Agenda Modern Data Warehouse architectures with Data Lake:
From ETL to LETS
Introduction to Azure Data Lake and U-SQL
Job submission with Azure Data Factory
Core U-SQL “LETS”:
Schematizing unstructured data
Other data formats
Scaling out over many files
Sharing schematized data
Examples of scaling out your custom ETL processing code with U-SQL:
JSON Processing
Custom Image processing

3. The Traditional Data Warehouse
Microsoft Analytics Platform System
9/12/2018
The Traditional Data Warehouse
BI and analytics
Dashboards
Reporting
Real-time
data 2
Data warehouse
ETL
Increasing
data volumes 1
New data
sources and types 3
Cloud-born
data 4
Data sources
OLTP
ERP
CRM
LOB
Non-relational data
Devices
Web
Sensors
Social
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.

4. The Data Lake Approach Designed for the Questions you don’t YET know!
Ingest all data regardless of requirements
Store all data
in native format without schema definition
Do analysis
Hadoop, Spark, R, Azure Data Lake Analytics (ADLA)
Devices
Social
Batch queries
Devices
LOB applications
Video
Interactive queries
Social
LOB applications
Real-time analytics
Sensors
Web
Sensors
Video
Machine Learning
Relational
Web
Clickstream
Data warehouse
Relational
Clickstream
Designed for the Questions you don’t YET know!

5. Azure Data Lake An on-demand, real-time stream processing service with no-limits data lake built to support massively parallel analytics
Performance at scale
Optimized for analytics
Multiple analytics engines
Single repository sharing
HDFS Compatible REST API 1
ADL Store
.NET, SQL, Python, R scaled out by U-SQL
ADL Analytics
Open Source Apache Hadoop ADL Client
HDInsight
Hive

6. Machine Learning & Data Science Conference
9/12/ :12 PM
Big data pipeline and data flow in Azure
Ingestion
Preparation, analytics and machine learning
Discovery
Azure
Data Catalog
Bulk ingestion
Business apps
Custom apps
Sensors and devices
Machine Learning
People
Data Lake Analytics
Visualization
Power BI
HDInsight
(Hadoop and Spark)
Stream Analytics
SQL DW
Azure Data Lake Store
Event ingestion
DATA
INTELLIGENCE
ACTION
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7. Big Data Warehouse ETL Azure Data Lake Store Analyst BI Models
Power User
Data Engineer
Data Scientist
ERP, CRM, and other LOB Data
Reports and Dashboards
ETL
Azure SQL Data Warehouse
BI Models
OLTP and other RDBMS
Governance and
Master Data Management
Data Quality and Lineage
Polybase
Clickstream Logs and Events
Azure Data Lake Analytics (U-SQL)
Apache Hadoop on HDInsight
Apache Spark on HDInsight
Sensors, Social, Weather, and other un-structured data
Azure Data Lake Store

8. Realtime Processing with Lambda Architecture
IoT Sensors and/or User activity streams
Realtime Dashboards
Streaming Layer
Event Broker
(Event Hubs, Apache Kafka)
Analyst
Clean, Curate, Aggregate
Combine reference data
Perform Scoring from ML models
Event Broker
Social, Trends, Weather etc.
Automated Systems
Reference Data
Trained Machine Learning Models
Clickstream, Batch Files, server logs,
Azure Data Lake Analytics (U-SQL)
Apache Hadoop on HDInsight
Apache Spark on HDInsight
Data Engineer
Images, videos, and other unstructured data
Azure Data Lake Store
Data Scientist

9. Load-Extract-Transform-Store & Share
Schematizing unstructured data (Load—Extract—Transform—Store) for analysis
Cook data for other users (LETS and Share)
As unstructured data
As structured data
Large-scale custom processing with custom code
Cloud-scale Cognitive Processing
Augment big data with high-value data from where it lives

10. Azure Data Lake Analytics
9/12/ :12 PM
Azure Data Lake Analytics
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Data Lake Analytics Workloads
9/12/2018
Data Lake Analytics Workloads
With BATCH workload, Data Lake Analytics is ideal for
The transformation and preparation of data for use in other systems
Analytics on VERY LARGE amounts of data
Massively Parallel programs written in .NET, Python and R, scaled out with U-SQL
Performing Cognition at Scale on large collections
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. Introducing U-SQL A framework for Big Data
Scales out your custom code in .NET, Python, R over your Data Lake
Familiar syntax to millions of SQL & .NET developers
Unifies
Declarative nature of SQL with the imperative power of C#
Processing of structured, semi-structured and unstructured data
Querying multiple Azure Data Sources (Federated Query)
Analyzing with Batch, Interactive, Streaming, & Machine Learning in one language
A framework for Big Data

13. Query data where it lives
Easily query data in multiple Azure data stores without moving it to a single store
Azure
Data Lake Storage
U-SQL can query data from multiple sources in Azure.
Where possible data transformation is pushed close to the remote query engine to minimize data transfer and maximize performance.
Azure
Storage Blobs
Query
Write
Query
Write
Azure SQL in VMs
Azure Data
Lake Analytics
Query
U-SQL
Query
Query
Query
Azure SQL DB
Azure SQL Data Warehouse

14. Embedded Artificial Intelligence
Host Deep Neural Networks (DNNs)
6 Built-in Cognitive Functions
Face API
Image Tagging
Emotion analysis
OCR
Text Key Phrase Extraction
Text Sentiment Analysis `

15. 9/12/ :12 PM
Azure Data Factory
Compose, orchestrate & monitor data services at scale
Fully managed service to support orchestration of data movement and transformation
Connect to relational or non-relational data that is on-premises or in the cloud
Single pane of glass to monitor and manage data processing pipelines
Globally deployed service infrastructure
Cost Effective
No SQL DB
ADL
Stored Procedures
Hadoop on Azure
Trusted data
BI & analytics
Data Lake Analytics
Custom Code
Machine Learning VM
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. File System* HDFS* Amazon S3
Azure Data Factory
Connects ADL Store out-of-the-box to all your stores
Category
Data store
Supported as source
Supported as sink
Azure
Azure Data Lake Store
Azure Blob storage Azure SQL Database Azure SQL Data Warehouse Azure Table storage Azure DocumentDB
✓ ✓ ✓ ✓ ✓ ✓
Databases
SQL Server* Oracle* MySQL* DB2* Teradata* PostgreSQL* Sybase* Cassandra* MongoDB* Amazon Redshift
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
✓ ✓                
File
File System* HDFS* Amazon S3
✓ ✓ ✓
✓    
Others
Salesforce Generic ODBC* Generic OData Web Table (table from HTML) GE Historian*
✓ ✓ ✓ ✓ ✓
   
* Can be on-premises or on Azure IaaS, enabled using Data Management Gateway

17. Submit Azure Data Lake Jobs with Azure Data Factory
9/12/ :12 PM
Submit Azure Data Lake Jobs with Azure Data Factory
ADF v1:
Data Lake Analytics U-SQL Activity against ADLA linked service
Service Principal or OAuth AAD credential
Provides Parameter model
Can specify special runtime
ADF v2 (in preview):
Service Principal (via Application entity in AAD)
Simplified with same power to ADF v1
Adds support for ADLA pipeline and recurring job insights
{ "name": "ComputeEventsByRegionPipeline",
"properties": { "description": "This is a U-SQL pipeline.",
"activities": [ { "type": "DataLakeAnalyticsU-SQL",
"typeProperties": { "scriptPath": "scripts\\kona\\SearchLogProcessing.txt",
"scriptLinkedService": "StorageLinkedService",
"degreeOfParallelism": 3,
"priority": 100,
"parameters": { "in": "/datalake/input/SearchLog.tsv",
"out": "/datalake/output/Result.tsv" } },
"inputs": [ { "name": "DataLakeTable" } ],
"outputs": [ { "name": "EventsByRegionTable" } ],
"policy": { "timeout": "06:00:00",
"concurrency": 1,
"executionPriorityOrder": "NewestFirst",
"retry": 1 },
"scheduler": { "frequency": "Day", "interval": 1 },
"name": "EventsByRegion",
"linkedServiceName": "AzureDataLakeAnalyticsLinkedService" } ],
"start": " T00:00:00Z",
"end": " T01:00:00Z",
"isPaused": false } }
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18. Gain insights from ADLA pipeline & recurring jobs
MICROSOFT CONFIDENTIAL
Gain insights from ADLA pipeline & recurring jobs
Original Jobs View
Original
List jobs submitted in the last 30 days
Aggregate trends of jobs over 30 days
Order and filter list of jobs
New Pipeline Jobs View
New
Superset of original jobs view
Adds grouping of jobs by pipelines & recurrences
Jobs and consumption trends per pipeline
Quickly identify pipelines and jobs to troubleshoot
Quickly compare failed jobs with “last known good” instance
Manage pipeline cost, improve efficiency and predict future cost
How to use
Create ADF v2 pipelines containing ADLA U-SQL activities
Pipelines and Recurrences automatically appear in ADLA portal
Submit and monitor pipeline/recurring jobs using Azure PowerShell, ADLA SDK and REST APIs

19. Schematize unstructured data
SMSG Readiness
9/12/2018
Schematize unstructured data
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20. Expression-flow Programming Style
9/12/ :12 PM
Expression-flow Programming Style
Automatic "in-lining" of U-SQL expressions – whole script leads to a single execution model.
Execution plan that is optimized out-of-the-box and w/o user intervention.
Per job and user driven level of parallelization.
Detail visibility into execution steps, for debugging.
Heatmap like functionality to identify performance bottlenecks.
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

21. “Unstructured” Files EXTRACT Expression OUTPUT 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, Parquet
Custom Extractors: e.g., JSON, XML, etc. (see
OUTPUT Expression
OUTPUT @s
TO "filepath/file.csv"
USING Outputters.Csv();
Built-in Outputters: Csv, Tsv, Text, Parquet
Custom Outputters: e.g., JSON, XML, etc. (see
Filepath URIs
Relative URI to default ADL Storage account: "filepath/file.csv"
Absolute URIs:
ADLS: "adl://account.azuredatalakestore.net/filepath/file.csv"
WASB:
Schema on Read
Write to File
Built-in and custom Extractors and Outputters
ADL Storage and Azure Blob Storage

22. Announcing: Processing Parquet in Azure Data Lake with U-SQL
SMSG Readiness
9/12/2018
Announcing: Processing Parquet in Azure Data Lake with U-SQL
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

23. Scale out over many files: File Sets!
SMSG Readiness
9/12/2018
Scale out over many files: File Sets!
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

24. File Sets Simple pattern language on filename and path Virtual columns
@pattern string =
"/input/{date:yyyy}/{date:MM}/{date:dd}/{*}.{suffix}";
Binds two columns date and suffix
Wildcards the filename
Limits on number of files and file sizes can be improved with
SET =
"FileSetV2Dot5:on,GroupedInputArray:on";
(Will become default between now and end of year)
Virtual columns
EXTRACT name string
, suffix string // virtual column
, date DateTime // virtual column
USING Extractors.Csv();
Refer to virtual columns in query predicates to get partition elimination
Warning gets raised if no partition elimination was found
File Sets
Simple Patterns
Virtual Columns
Only on EXTRACT for now (On OUTPUT by early 2018)

25. Cook Data and share with others
SMSG Readiness
9/12/2018
Cook Data and share with others
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

26. Meta Data Object Model ADLA Account/Catalog [1,n] C# Extractors
C# Reducers
Database
C# Processors
C# Fns
C# UDTs
C# UDAgg
C# Applier
[1,n]
C# Combiners
C# Outputters
Creden-tials
Data Source
Schema
C# Assemblies
[0,n]
Ext. tables
tables
views
TVFs
Procedures
Table Types
Packages
Statistics
Clustered Index
Legend
User objects
MD Name
C# Name
partitions
Contains
Refers to
Implemented
and named by

27. U-SQL Catalog Naming Discovery Sharing Securing Naming Discovery
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
Across ADLA accounts that share same Azure Active Directory:
Referencing Assemblies
Calling TVFs, Procedures and referencing tables and views
Inserting into tables
Securing
Secured with AAD principals at catalog and Database level
Naming
Discovery
Sharing
Securing

28. VIEWs and TVFs Views Table-Valued Functions (TVFs)
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 string = "default") [TABLE ( … )]
AS BEGIN = … 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
Views for simple cases
TVFs for parameterization and most cases

29. Procedures Allows encapsulation of U-SQL scripts
CREATE PROCEDURE P string = "default“) AS
BEGIN
…; 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)

30. Tables CREATE TABLE CREATE TABLE AS SELECT 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)
CREATE TABLE
CREATE TABLE AS SELECT

31. When to use Tables 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
If in doubt: use sampling (e.g., SAMPLE ANY(x)) and test.

32. SMSG Readiness
9/12/2018
Large-scale custom processing with custom code (more details in BRK3350!)
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

33. U-SQL extensibility Built-in operators, function, aggregates
Extend U-SQL with C#/.NET
Built-in operators, function, aggregates
C# expressions (in SELECT expressions)
User-defined functions (UDFs)
User-defined aggregates (UDAGGs)
User-defined operators (UDOs)

34. What are UDOs? Custom Operator Extensions in language of your choice
User-Defined Extractors
Converts files into rowset (see BRK3323 for more examples)
User-Defined Outputters
Converts rowset into files (see BRK3323 for more examples)
User-Defined Processors
Take one row and produce one row
Pass-through versus transforming
User-Defined Appliers
Take one row and produce 0 to n rows
Used with OUTER/CROSS APPLY
User-Defined Combiners
Combines rowsets (like a user-defined join)
User-Defined Reducers
Take n rows and produce m rows (normally m<n)
Scaled out with explicit U-SQL Syntax that takes a UDO instance (created as part of the execution):
EXTRACT
OUTPUT
CROSS APPLY
Custom Operator Extensions in language of your choice
Scaled out by U-SQL
PROCESS
COMBINE
REDUCE

35. JSON Processing How do I extract data from JSON documents?

36. Microsoft.Analytics.Samples.Formats
JSON Processing
Architecture of Sample Format Assembly
Single JSON document per file: Use JsonExtractor
Multiple JSON documents per file:
Do not allow row delimiter (e.g., CR/LF) in JSON
Use built-in Text Extractor to extract
Use JsonTuple to schematize (with CROSS APPLY)
Currently loads full JSON document into memory
better to use JSONReader Processing if docs are large
Microsoft.Analytics.Samples.Formats
Assembly provides Extractors and UDOs/UDFs for:
JSON
XML
AVRO
NewtonSoft.Json
System.Xml
Microsoft.Hadoop.Avro

37. JSON Processing Key to field relative to objects in JsonExtractor
EXTRACT personid int,
name string,
addresses string
USING new Json.JsonExtractor(“[*].person");
@person =
SELECT personid,
name,
Json.JsonFunctions.JsonTuple(addresses)["address"] AS address_array
@addresses = SELECT personid, name, Json.JsonFunctions.JsonTuple(address) AS address
CROSS APPLY
EXPLODE (Json.JsonFunctions.JsonTuple(address_array).Values) AS A(address);
@result =
address["addressid"]AS addressid,
address["street"]AS street,
address["postcode"]AS postcode,
address["city"]AS city
Key to field relative to objects in JsonExtractor
JPath Expression mapping objects to Row
Generates 1-level key value-pairs as SqlMap
Gets value from map as string
Get object map for array item
Convert string array into Map and pivot all Values into rows
Get desired keys from object map

38. Cloud-scale Cognitive Processing
Racing
Parked
Car
Green
Outdoor
See my session BRK3350 for more details and many more examples

39. Related Ignite Presentations
BRK Understanding big data on Azure - structured, unstructured and streaming, Tuesday, September 26, 10:45 AM - 12:00 PM, OCCC W307
BRK Data on Azure: The big picture, Wednesday, September 27, 12:30 PM - 1:45 PM, Hyatt Plaza International G
BRK Run Python, R and .NET code at Data Lake scale with U-SQL in Azure Data Lake, Thursday, September 28, 10:45 AM - 12:00 PM, Hyatt Plaza International D-F
Stop by the booth and the Hands On-Labs!

40. Additional Resources Blogs and community page:
(U-SQL Github)
Documentation, presentations and articles:
Getting Started with R in U-SQL
ADL forums and feedback
Continue your education at Microsoft Virtual Academy online.

41. Please evaluate this session
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9/12/2018
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© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

42. 9/12/ :12 PM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.