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Azure Data Factory Training: Designing and Implementing Data Integration Solutions

Nivå: Intermediate

This course covers all key aspects of the Azure Data Factory v2 platform.

It is ideal for architects, developers, administrators, IT managers, and anyone else who would like to make the best possible use of this Azure service. Key areas covered include ADF v2 architecture, UI-based and automated data movement mechanisms, 10+ data transformation approaches, control-flow activities, reuse options, operational best-practises, and a multi-tiered approach to ADF security.

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.

6 hands-on instructor-led labs are included with the course. These allow students to practise applying ADF v2 concepts and prepare them for real-world Azure data integration projects.

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Nyckelfunktioner:

  • After-course instructor coaching benefit
  • Hands-on labs included

Du kommer lära dig att:

  • 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 practises available for the ADF v2 platform.

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I klass & Live, Online-utbildning

  • 3-day instructor-led training course
  • One-on-one after-course instructor coaching
  • Tuition fee can be paid later by invoice -OR- at the time of checkout by credit card
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  • Coaching av ett expertteam

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Utveckla dig och ditt team med anpassade eller öppna kurser alternativt e-learning

Learning Tree erbjuder kundanpassad utbildning hos er, öppna kurser i Stockholm, London eller Washington, möjlighet att delta via våra Anywhere centers (Malmö, Göteborg, Linköping, Stockholm eller Borlänge) eller olika former av e-learning med lärarstöd. Läs mer på www.learningtree.se/priser .

I klass & Live, Online-utbildning

Note: This course runs for 3 dagar *

*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.

  • 10 - 12 feb 9:00 - 4:30 CET Stockholm / Online (AnyWare) Stockholm / Online (AnyWare) Boka Din Kursplats

  • 2 - 4 jun 9:00 - 4:30 CEST Stockholm / Online (AnyWare) Stockholm / Online (AnyWare) Boka Din Kursplats

  • 2 - 4 dec 9:00 - 4:30 EST Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 6 - 8 jan 9:00 - 4:30 EST New York / Online (AnyWare) New York / Online (AnyWare) Boka Din Kursplats

  • 3 - 5 mar 9:00 - 4:30 EST Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Boka Din Kursplats

  • 7 - 9 apr 9:00 - 4:30 EDT New York / Online (AnyWare) New York / Online (AnyWare) Boka Din Kursplats

  • 5 - 7 maj 9:00 - 4:30 EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Boka Din Kursplats

  • 7 - 9 jul 9:00 - 4:30 EDT New York / Online (AnyWare) New York / Online (AnyWare) Boka Din Kursplats

  • 4 - 6 aug 9:00 - 4:30 EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Boka Din Kursplats

  • 8 - 10 sep 9:00 - 4:30 EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Boka Din Kursplats

Kurs med startgaranti

När du ser symbolen för “Guaranteed to Run” vid ett kurstillfälle vet du att kursen blir av. Garanterat.

Partial Day Event

Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.

Important Azure Data Factory Training Information

Azure Data Factory Training Outline

  • Introduction to ADF

    • Historical background: SSIS, ADF v1, other ETL/ELT tools
    • Key capabilities and benefits of ADF v2
    • Recent feature updates and enhancements

  • 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

  • Building and Executing Your First Pipeline

    • Creating ADF v2 instance
    • Creating a pipeline and associated activities
    • Executing the pipeline
    • Monitoring execution
    • Reviewing results

  • 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 behaviour: append, upsert, overwrite, write with custom logic
    • Schema and data type mapping
    • Fault tolerance options

  • 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

  • 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

  • Runtime and Operations

    • Debugging
    • Monitoring: visual, Azure Monitor, SDKs, runtime-specific best practises
    • Scheduling execution with triggers: event-based, schedule, tumbling window
    • Performance, scalability, tuning
    • Common troubleshooting scenarios in activities, connectors, data flows and integration runtimes

  • 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 practises
    • Continuous Integration (CI) pipelines
    • Continuous Delivery (CD) pipelines

  • Promoting Reuse

    • Templates: out-of-the-box and organisational
    • Parameters
    • Naming convention

  • Security

    • Data movement security
    • Azure Key Vault
    • Self-hosted IR considerations
    • IP address blocks
    • Managed identity

Teamträning

Azure Data Factory Training FAQs

  • What is Azure Data Factory?

    Azure Data Factory (ADF) v2 is an Azure data integration service which allows creation of data-driven workflows to orchestrate and automate data movement and transformation across cloud, on-prem and hybrid environments.

  • How much ADF experience do I need to sit for this course?

    While this new course is designed to bring students from zero expertise with ADF v2 to an intermediate or even advanced level of knowledge, Microsoft Azure Fundamentals Training (AZ-900T00) or equivalent is expected.

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