What you’ll learn
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Data Engineering leveraging Services under Azure Data Analytics such as Azure Storage, Data Factory, Azure SQL, Synapse, Databricks, etc.
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Setup Development Environment using Visual Studio Code on Windows
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Building Data Lake using Azure Storage (Blob and ADLS)
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Build Data Warehouse using Azure Synapse
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Implement ETL Logic using ADF Data Flow with Azure Storage as Source and Target
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In Depth Coverage of Orchestration using ADF Pipeline
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Overview of Azure SQL and Azure Synapse Serverless and Dedicated Pool Features
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Implement ETL Logic using ADF Data Flow with Azure SQL as Source and Azure Synapse as Target
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Using Data Copy to copy data between different sources and targets
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Performance Tuning Scenarios of ADF Data Flow and Pipelines
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Build Big Data Solutions using Azure Databricks
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Overview of Spark SQL and Pyspark Data Frame APIs
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Build ELT Pipelines using Databricks Jobs and Workflows
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Orchestrate Databricks Notebooks using ADF Pipelines
Who this course is for:
- Beginner or Intermediate Data Engineers who want to learn Key Azure Analytics Services for Data Engineering such as Azure Storage, ADF, Synapse, Databricks, etc
- Intermediate Application Engineers who want to explore Data Engineering using Azure Analytics Services for Data Engineering such as Azure Storage, ADF, Synapse, Databricks, etc
- Data and Analytics Engineers who want to learn Data Engineering Azure Analytics Services for Data Engineering such as Azure Storage, ADF, Synapse, Databricks, etc
- Testers who want to learn key skills to test Data Engineering applications built using Azure Analytics Services for Data Engineering such as Azure Storage, ADF, Synapse, Databricks, etc
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