This slide deck will walk you through introduction to Azure Data Factory with the help of a small demo and Power BI updates for November month

1. Power BI : Deep Dive
NOVEMBER 2015
Presented By:
Ashish Jagdale - Data Analyst
Ankita Anchan - BI Developer
Anil Shah - Managing Partner

2. Agenda
• Quick Recap on Previous Power BI Webinar
• Azure Data Factory
• Introduction to Azure Data Factory
• Supported Data Stores
• Azure Data Factory Demo
• Pricing
• Data Factory Limits
• Power BI Updates
• Power BI Desktop Tool
• Power BI Online
• Q & A

3. Quick Recap
What’s New in Power BI – JUNE 2015
Video Recording: https://www.youtube.com/watch?v=ROQ_MFgjTUA
Power BI Deep Dive & Real-Time Analytics – AUGUST 2015
Video Recording: https://www.youtube.com/watch?v=MkIM2aVqRa8
Power BI : Features Deep Dive – SEPTEMBER 2015
Video Recording: https://www.youtube.com/watch?v=4QcrfNJvRVo
Power BI : Features Deep Dive –OCTOBER 2015
Video Recording: https://www.youtube.com/watch?v=wIpwziw2Y4s&feature=youtu.be

4. Introduction to Azure Data Factory
What is Azure Data Factory?
• Cloud-based, highly scalable data movement and transformation tool
• Build on Azure for integrating all kinds of data
• ADF can be accessed using Azure Preview Portal (portal.azure.com)
Key Concepts in Azure Data Factory
• Dataset – Identify data structures within different data stores including tables, files, folders, and documents
• Linked Service – Define the information needed for Data Factory to connect to external resources
• Pipeline – Used to group activities into a unit that together perform a task
• Activity – Define the actions to perform on your data

5. Contd..
Fig. Relationships between Dataset, Activity, Pipeline, and Linked service

6. Contd..
Fig. Collect data from many different on-premises data sources, ingest and prepare it, organize and
analyze it with a range of transformations, then publish ready-to-use data for consumption

7. Supported Data Stores
• Copy activity copies data from a source data store to a sink data
store. Data factory supports the following data stores:
Azure Data Factory - Supported Data Stores
Cloud On-Premises
Azure Blob File System
Azure Table SQL Server
Azure SQL Database Oracle Database
Azure SQL Data Warehouse MySQL Database
Azure DocumentDB DB2 Database
Azure Data Lake Store Teradata Database
SQL Server on IaaS Sybase Database

8. Azure Data Factory Demo
Demo consists of copy data activity from csv file stored in Azure Blob
Storage to Azure SQL Database using Azure Data Factory Editor
Steps for creating Azure Data Factory using Data Factory Editor
• Step 1: Create an Azure Data Factory
• Step 2: Create linked services
• Step 3: Create input and output tables
• Step 4: Create and run a pipeline
• Step 5: Monitor the datasets and pipeline

9. Pricing
• Azure Data Factory is priced by the frequency of activities (high or
low) and where the activities run (cloud or on-premises)
• Data Movement
• Inactive Pipelines - $0.80/month
Source/Target Low Frequency High Frequency
Cloud $0.60 per activity per month $0.80 per activity per month
On-Premises $1.50 per activity per month $2.50 per activity per month
Cloud On-Premises
$0.25 per hour $0.10 per hour

10. Data Factory Limits
Resource Default Limit Maximum Limit
pipelines within a data factory 100 2500
datasets within a data factory 500 5000
concurrent slices per dataset 10 10
• Data Factory is available in US West and North Europe. The compute
and storage services used by data factories can be in other regions
Azure Data Factory Limits

11. Power BI Updates
Power BI Desktop Tool
• Refresh single table (vs. all) for reports and data views
• Improvements in slicer visualization
• The new word cloud visualization

12. Contd..
Power BI Online
• Group: Member/Admin
• Duplicate report page

13. ANY QUERIES?
Our next Power BI webinar to be scheduled on
17th December 2015
Contact Us -
www.cloudfronts.com
info@cloudfronts.com
support@cloudfronts.com