Azure Data Factory is one of the newer data services in Microsoft Azure and is part of the Cortana Analyics Suite, providing data orchestration and movement capabilities. This session will describe the key components of Azure Data Factory and take a look at how you create data transformation and movement activities using the online tooling. Additionally, the new tooling that shipped with the recently updated Azure SDK 2.8 will be shown in order to provide a quickstart for your cloud ETL projects.

1. Sponsored & Brought to you by
A Lap around Azure Data Factory
Martin Abbott
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2. A Lap around Azure Data Factory
Martin Abbott
@martinabbott

3. About me
10+ years experience
Integration, messaging and cloud person
Organiser of Perth Microsoft Cloud User Group
Member of GlobalAzure Bootcamp admin team
BizTalk developer and architect
Identity management maven
IoT enthusiast
Soon to be Australian Citizen

4. Agenda
Overview
Data movement
Data transformation
Development
Monitoring
Demonstration
General information

5. Overview of an Azure Data Factory

6. Overview of an Azure Data Factory
• Cloud based data integration
• Orchestration and transformation
• Automation
• Large volumes of data
• Part of Cortana Analytics Suite Information Management
• Fully managed service, scalable, reliable

7. Anatomy of an Azure Data Factory
An Azure Data Factory is made up of:

8. Linked services
• Represents either
• a data store
• File system
• On-premises SQL Server
• Azure storage
• Azure DocumentDB
• Azure Data Lake Store
• etc.
• a compute resource
• HDInsight (own or on demand)
• Azure Machine Learning Endpoint
• Azure Batch
• Azure SQL Database
• Azure Data Lake Analytics

9. Data sets
• Named references to data
• Used for both input and output
• Identifies structure
• Files, tables, folders, documents
• Internal or external
• Use SliceStart and SliceEnd
system variables to create
distinct slices on output data
sets, e.g., unique folder based on
date

10. Activities
• Define actions to perform on data
• Zero or more input data sets
• One or more output data sets
• Unit of orchestration of a pipeline
• Activities for
• data movement
• data transformation
• data analysis
• Use WindowStart and WindowEnd
system variables to select relevant
data using a tumbling window

11. Pipelines
• Logical grouping of activities
• Provides a unit of work that
performs a task
• Can set active period to run
in the past to back fill data
slices
• Back filling can be performed
in parallel

12. Scheduling
• Data sets have an availability
"availability": { "frequency": "Hour", "interval": 1 }
• Activities have a schedule (tumbling window)
"scheduler": { "frequency": "Hour", "interval": 1 }
• Pipelines have an active period
"start": "2015-01-01T08:00:00Z"
"end": "2015-01-01T11:00:00Z“ OR
“end” = “start” + 48 hours if not specified OR
“end”: “9999-09-09” for indefinite

13. Data Lineage / Dependencies
• How does Azure Data Factory know how to link
Pipelines?
• Uses Input and Output data sets
• On the Diagram in portal, can toggle data lineage on and
off
• external required (and externalData policy optional) for
data sets created outside Azure Data Factory
• How does Azure Data Factory know how to link data
sets that have different schedules?
• Uses startTime, endTime and dependency model

14. Functions
• Rich set of functions to
• Specify data selection queries
• Specify input data set dependencies
• [startTime, endTime] – data set slice
• [f(startTime, endTime), g(startTime, endTime)] – dependency
period
• Use system variables as parameters
• Functions for text formatting and date/time selection
• Text.Format('{0:yyyy}',WindowStart)
• Date.AddDays(SliceStart, -7 - Date.DayOfWeek(SliceStart))

15. Data movement

16. Data movement
SOURCE SINK
Azure Blob Azure Blob
Azure Table Azure Table
Azure SQL Database Azure SQL Database
Azure SQL Data Warehouse Azure SQL Data Warehouse
Azure DocumentDB Azure DocumentDB
Azure Data Lake Store Azure Data Lake Store
SQL Server on-premises / Azure IaaS SQL Server on-premises / Azure IaaS
File System on-premises / Azure IaaS File System on-premises / Azure IaaS
Oracle Database on-premises / Azure IaaS
MySQL Database on-premises / Azure IaaS
DB2 Database on-premises / Azure IaaS
Teradata Database on-premises / Azure IaaS
Sybase Database on-premises / Azure IaaS
PostgreSQL Database on-premises / Azure IaaS

17. Data movement
• Uses the Copy activity and Data Movement Service or Data Management Gateway (for on-premises or
Azure IaaS)
• Globally available service for data movement (except Australia)
• executes at sink location, unless source is on-premises (or IaaS) then uses Data Management Gateway
• Exactly one input and exactly one output
• Support for securely moving between on-premises and the cloud
• Automatic type conversions from source to sink data types
• File based copy supports binary, text and Avro formats, and allows for conversion between formats
• Data Management Gateway supports multiple data sources but only a single Azure Data Factory
Source
Data Movement Service
WAN Serialisation-
Deserialisation
Compression
Column
Mapping
…
WAN Sink
Source
Data Management Gateway
LAN/
WAN Serialisation-
Deserialisation
Compression
Column
Mapping
…
SinkLAN/
WAN

18. Data analysis and transformation

19. Data analysis and transformation
TRANSFORMATION ACTIVITY COMPUTE ENVIRONMENT
Hive HDInsight [Hadoop]
Pig HDInsight [Hadoop]
MapReduce HDInsight [Hadoop]
Hadoop Streaming HDInsight [Hadoop]
Machine Learning activities: Batch Execution and
Update Resource
Azure VM
Stored Procedure Azure SQL Database
Data Lake Analytics U-SQL Azure Data Lake Analytics
DotNet HDInsight [Hadoop] or Azure Batch

20. Data analysis and transformation
• Two types of compute environment
• On-demand: Data Factory fully manages environment, currently HDInsight only
• Set timeToLive to set allowed idle time once job finishes
• Set osType for windows or linux
• Set clusterSize to determine number of nodes
• Provisioning an HDInsight cluster on-demand can take some time
• Bring your own: Register own computing environment for use as a linked service
• HDInsight Linked Service
• clusterUri, username, password and location
• Azure Batch Linked Service
• accountName, accessKey and poolName
• Machine Learning Linked Service
• mlEndpoint and apiKey
• Data Lake Analytics Linked Service
• accountName, dataLakeAnalyticsUri and authorization
• Azure SQL Database Linked Service
• connectionString

21. Development

22. Development
• JSON for all artefacts
• Ease of management by source control
• Can be developed using:
• Data Factory Editor
• In Azure Portal
• Create and deploy artefacts
• PowerShell
• Cmdlets for each main function in PS ARM
• Visual Studio
• Azure Data Factory Templates
• .NET SDK

23. Visual Studio
• Rich set of templates including
• Sample applications
• Data analysis and transformation using Hive
and Pig
• Data movement between typical
environments
• Can include sample data
• Can create Azure Data Factory, storage
and compute resources
• Can Publish to Azure Data Factory
• No toolbox, mostly hand crafting JSON

24. Tips and Tricks with Visual Studio Templates
• Something usually fails
• Issues with sample data
• Run once to create Data Factory and storage accounts
• Usually first run will also create a folder containing Sample Data but NO JSON
artifacts
• May need to manually edit PowerShell scripts or perform manual upload
• Once corrected, deselect Sample Data and run again creating new solution
• Ensure Publish to Data Factory is deselected and JSON artifacts are created
• Issues with Data Factory deployment
• Go to portal and check what failed
• May need to manually create item but deleting published item and recreating with
JSON from project
• When deploying, may need to unselect item that is failing
• You cannot delete from the project
• Need to Exclude From Project
• Once excluded can delete from disk

25. Deployment
• Add Config files to your Visual Studio
project
• Deployment files contain, for instance,
connection strings to resources that
are replaced at Publish time
• Add deployment files for each
environment you are deploying to,
e.g., Dev, UAT, Prod
• When publishing to Azure Data
Factory choose appropriate Config file
to ensure correct settings are applied
• Publish only artefacts required

26. Monitoring

27. Monitoring
• Data slices may fail
• Drill in to errors, diagnose, fix and rerun
• Failed data slices can be rerun and all
dependencies are managed by Azure
Data Factory
• Upstream slices that are Ready stay
available
• Downstream slices that are dependent
stay Pending
• Enable diagnostics to produce logs,
disabled by default
• Add Alerts for Failed or Successful Runs to
receive email notification

28. Demonstration

29. General information

30. Pricing – Low frequency ( <= 1 / day )
USAGE PRICE
Cloud First 5 activities/month Free
6 – 100 activities/month $0.60 per activity
>100 activities/month $0.48 per activity
On-Premises First 5 activities/month Free
6 – 100 activities/month $1.50 per activity
>100 activities/month $1.20 per activity
* Pricing in USD correct at 4 December 2015

31. Pricing – High frequency ( > 1 / day )
USAGE PRICE
Cloud <= 100 activities/month $0.80 per activity
>100 activities/month $0.64 per activity
On-Premises <= 100 activities/month $2.50 per activity
>100 activities/month $2.00 per activity
* Pricing in USD correct at 4 December 2015

32. Pricing – Data movement
Cloud $0.25 per hour
On-Premises $0.10 per hour
Pricing – Inactive pipeline
$0.80/month
* Pricing in USD correct at 4 December 2015

33. Summary
• Use Azure Data Factory if:
• Dealing with Big Data
• Source or destination is in the cloud
• Cut down environment cost
• Cut down administration cost
• Azure is on one side of the movement / transformation
• Consider hybrid scenarios with other data management tools, for example
SQL Server Integration Services

34. More Information
• Documentation portal
• https://azure.microsoft.com/en-us/documentation/services/data-
factory/
• Learning map
• https://azure.microsoft.com/en-us/documentation/articles/data-
factory-learning-map/
• Samples on github
• https://github.com/Azure/Azure-DataFactory

35. Thank you!