Azure Data Factory Training: Designing and Implementing Data Integration Solutions
Price:
$2,507.00
$2,507.00
Course Outline
This Azure Data Factory Training covers all key aspects of the Azure Data Factory v2 platform. Special attention is paid to covering Azure services which are commonly used with ADF v2 solutions. These services are Azure Data Lake Storage Gen 2, Azure SQL Database, Azure Databricks, Azure Key Vault, Azure Functions, and a few others.
Azure Data Factory Training: Designing and Implementing Data Integration Solutions Benefits
-
In this Azure Data Factory course, you will learn how to:
- Build end-to-end ETL and ELT solutions using Azure Data Factory v2
- Architect, develop and deploy sophisticated, high-performance, easy-to-maintain and secure pipelines that integrate data from a variety of Azure and non-Azure data sources.
- Apply the latest DevOps best practices available for the ADF v2 platform.
-
Prerequisites
Learning Tree course 8566, Microsoft Azure Fundamentals Training (AZ-900T00), or equivalent experience.
Azure Data Factory Training Outline
Module 1: Introduction to ADF
- Historical background: SSIS, ADF v1, other ETL/ELT tools
- Key capabilities and benefits of ADF v2
- Recent feature updates and enhancements
Module 2: Core Architectural Components
- Connectors: Azure services, databases, NoSQL, files, generic protocols, services & apps, custom
- Pipelines
- Activities: data movement, data transformation, control flow
- Datasets: source, sink
- Integration Runtimes: Azure, Self-Hosted, Azure-SSIS
Module 3: Building and Executing Your First Pipeline
- Creating ADF v2 instance
- Creating a pipeline and associated activities
- Executing the pipeline
- Monitoring execution
- Reviewing results
Module 4: Data Movement
Copying Tools and SDKS
- Copy Data Tool/Wizard
- Copy activity
- SDKs: Python, .NET
- Automation: PowerShell, REST API, ARM Templates
Copying Considerations
- File formats: Avro, binary, delimited, JSON, ORC, Parquet
- Data store support matrix
- Write behavior: append, upsert, overwrite, write with custom logic
- Schema and data type mapping
- Fault tolerance options
Module 5: Data Transformation
Transformation with Mapping Data Flows
- Introduction to mapping data flows
- Data flow canvas
- Debug mode
- Dealing with schema drift
- Expression builder & language
- Transformation types: Aggregate, Alter row, Conditional split, Derived column, Exists, Filter, Flatten, Join, Lookup, New branch, Pivot, Select, Sink, Sort, Source, Surrogate key, Union, Unpivot, Window
Transformation with External Services
- Databricks: Notebook, Jar, Python
- HDInsight: Hive, Pig, MapReduce, Streaming, Spark
- Azure Machine Learning service
- SQL Stored procedures
- Azure Data Lake Analytics U-SQL
- Custom activities with .NET or R
Module 6: Control Flow
- Purpose of activity dependencies: branching and chaining
- Activity dependency conditions: succeeded, failed, skipped, completed
- Control flow activities: Append Variable, Azure Function, Execute Pipeline, Filter, ForEach, Get Metadata, If Condition, Lookup, Set Variable, Until, Wait, Web
Module 7: Runtime and Operations
- Debugging
- Monitoring: visual, Azure Monitor, SDKs, runtime-specific best practices
- Scheduling execution with triggers: event-based, schedule, tumbling window
- Performance, scalability, tuning
- Common troubleshooting scenarios in activities, connectors, data flows and integration runtimes
Module 8: DevOps with ADF
- Quick introduction to source control with Git
- Integration with GitHub and Azure DevOps platforms
- Environment management: Development, QA, Production
- Iterative development best practices
- Continuous Integration (CI) pipelines
- Continuous Delivery (CD) pipelines
Module 9: Promoting Reuse
- Templates: out-of-the-box and organizational
- Parameters
- Naming convention
Module 10: Security
- Data movement security
- Azure Key Vault
- Self-hosted IR considerations
- IP address blocks
- Managed identity
- choosing a selection results in a full page refresh