Data Engineering Best Practices Using Azure Data Factory by Microsoft (Recordings)

Data Engineering Best Practices Using Azure Data Factory by Microsoft (Recordings)

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Data Engineering Best Practices Using Azure Data Factory

Speakers: Abhishek Narain, Sunil Sabat, Linda Wang

8-hrs Video Course (Recorded Class)

Subscription Period: Lifetime Access

Abstract: In this workshop, we will cover data engineering best practices while using Azure Data Factory – Performance, Security, and Scalability being the key focus areas. We will build ETL pipelines as part of the workshop for hands-on learning.

Modules

1. Data pipeline overview

  • What is data factory?
  • Data engineering common scenario
  • Getting started and environment setup

2. Data Integration: Connectivity and code-free transformation

  • Bringing data to data lake on Azure: On-premises and SaaS/PaaS datastore connectivity with a Copy activity
  • Transforming data using data flows

3. Best practices for operationalizing data pipelines

  • Govern data using Azure Purview Integration
  • Scalability and Performance considerations
  • Security
  • External integrations with compute engines (Databricks, SProc)
  • Continuous Integration and Continuous Deployment (CICD)

Description

Data Engineering Best Practices Using Azure Data Factory

Abstract: In this workshop, we will cover data engineering best practices while using Azure Data Factory – Performance, Security, and Scalability being the key focus areas. We will build ETL pipelines as part of the workshop for hands-on learning.

Modules:

1. Data pipeline overview

  • What is data factory?
  • Data engineering common scenario
  • Getting started and environment setup

2. Data Integration: Connectivity and code-free transformation

  • Bringing data to data lake on Azure: On-premises and SaaS/PaaS datastore connectivity with a Copy activity
  • Transforming data using data flows

3. Best practices for operationalizing data pipelines

  • Govern data using Azure Purview Integration
  • Scalability and Performance considerations
  • Security
  • External integrations with compute engines (Databricks, SProc)
  • Continuous Integration and Continuous Deployment (CICD)

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