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Azuresatpn19 - An Introduction To Azure Data Factory
- 1. #azuresatpn Azure Saturday 2019 An introduction to Azure Data Factory Riccardo Perico
- 2. #azuresatpn Nice to meet you Riccardo Perico | rperico@solidq.com | @R1k91 SolidQ Data Platform & BI Specialist 10 years working, training and speaking in Microsoft «Data Realm» MCP: MTA, MCSA https://www.linkedin.com/in/riccardo-perico-8b942384/
- 3. #azuresatpn Agenda • Introduction to ADF v2 • Integration Runtime • Mapping Data Flows • Demo • Useful information
- 5. #azuresatpn What ADF really is? Cloud based Data integration service Orchestrates & Automates Data movement and transformation Allows Monitoring and Debugging Programmable
- 6. #azuresatpn The Big Data “problem”
- 7. #azuresatpn Sample Workflow On-premises data mart Customer web logs Product table Azure DB Product recommendations Visualize Azure Blob storage Customer web Logs Product table Data set (Collection of files, DB table, etc.) Pipeline: A sequence of activities (logical group) Activity: A processing step (Hadoop job, custom code, ML model, etc.) … Data sources Ingest Transform and analyze Publish Combined input table Mapping Transform, combine, etc. Analyze Move
- 8. #azuresatpn Devices Device Connectivity Storage Analytics Presentation & Action Event Hubs SQL Database Machine Learning App Service IoT Hubs Table/Blob Storage Stream Analytics Power BI Service Bus Cosmos DB HDInsight Notification Hubs External Data Sources External Data Sources Data Factory Mobile Services BizTalk Services Data Lake Analytics
- 10. #azuresatpn Linked Services Data Stores Compute Input Dataset Output Dataset
- 11. #azuresatpn Activities & Pipelines An Activity is a single task in workflow: • Copy from input to output • Transform • C# • Stored Procedure • Hadoop (Map/Reduce, Hive, Pig) • ML, Data Lake Analytics • Databricks • Control • IF, ForEach, Until, Wait, Execute Pipeline • Web Pipeline groups activities SQL Serve r SQL DB SQL Server VMs
- 12. #azuresatpn Integration Runtime • Bridge between Activity and Linked Service • Compute environment where activity runs or it’s dispatched from 3 types of IR: • IR Azure • IR Self-hosted • IR Azure-SSIS
- 14. #azuresatpn ADF Location vs IR Location • ADF location metadata store and triggering pipeline start • IR location backend compute engine location (data movement, activity dispatch and SSIS execution) ADF Location and IR location could be different IR can use “Auto Resolve”
- 15. #azuresatpn Mapping Data Flows • Based on Spark • Use Databricks behind the scene • A lot of transformations already available • Few sources available for now • This week GA announced!
- 16. #azuresatpn Azure Saturday 2019 Demo: let’s put everything togheter
- 17. #azuresatpn Developer Tools • Azure Portal: Create, Edit. Visual and Textual • Visual Studio: Integrated in VS project • Powershell: cmdlets https://docs.microsoft.com/en- us/powershell/module/azurerm.datafactories/?view=azurermps- 6.13.0 • Azure RM Template
- 18. #azuresatpn Pricing Multiple factors affect pricing • Number of Activities run • Volume of data moved • SQL Server Integration Services Compute Hours • Whether you re-running an activity https://azure.microsoft.com/en-us/pricing/details/data-factory/v2/
- 19. #azuresatpn Useful Links • Overview: http://tiny.cc/domwdz) • ADF Channel 9: http://tiny.cc/pdnwdz • Blog posts: http://tiny.cc/6smwdz • Quick start and tutorials: http://tiny.cc/wumwdz) • GitHub repository – Code and examples http://tiny.cc/1vmwdz • GitHub repository – Hands-on labs (http://tiny.cc/4wmwdz • v1 and v2 comparison http://tiny.cc/txmwdz
Editor's Notes
- Enterprises have data of various types that are located in disparate sources on-premises, in the cloud, structured, unstructured, and semi-structured, all arriving at different intervals and speeds. The first step in building an information production system is to connect to all the required sources. Without Data Factory, enterprises must build custom data movement components, they often lack the enterprise-grade monitoring, alerting, and the controls that a fully managed service can offer. After data is present in a centralized data store in the cloud, process or transform the collected data by using compute services such as HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning. After the raw data has been refined into a business-ready consumable form, load the data into Azure Data Warehouse, Azure SQL Database, Azure CosmosDB, or whichever analytics engine your business users can point to from their business intelligence tools.
- The Data Set is a view of input/output data
- Data sets identify the data from different data stores.
- Azure: public accessible endpoints, serverless, fully managed, pay for use only, scaled up automagically according to copy activity properties Self-hosted: everything works in a private network behind corporate firewall, only HTTP outbound. A Windows server is needed and IR must be installed. Supports active-active load balancing. Azure SSIS: Set of VMs natively executes SSIS. Supports BYO SSISDB on Azure SQL DB or Managed Instance. To On-prem use Azure Virtual Network with VPN site-to-site.
- Mapping Data Flows are visually designed data transformations in Azure Data Factory
- Copy activity from S3 to SQL Rest to SQL Datasets transform with SP vs MDF Trigger & Monitor