Master Data Engineering using Azure Data Analytics
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Master Data Engineering using Azure Data Analytics Course
Learn Azure Storage for Data Lake, ADF for ETL, BigQuery for Data Warehouse, Databricks for Big Data Pipeline, etcs
Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lakes or Data Warehouses and then from Data Lakes or Data Warehouses to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using Azure Data Analytics Stack. It includes services such as Azure Storage (both Blob and ADLS), ADF Data Flow, ADF Pipeline, Azure SQL, Azure Synapse, Azure Databricks, and many more.
Best Seller Course: Data Engineering using AWS Data Analytics
What you’ll learn
- Data Engineering leveraging Services under Azure Data Analytics such as Azure Storage, Data Factory, Azure SQL, Synapse, Databricks, etc.
- Setup Development Environment using Visual Studio Code on Windows
- Building Data Lake using Azure Storage (Blob and ADLS)
- Build Data Warehouse using Azure Synapse
- Implement ETL Logic using ADF Data Flow with Azure Storage as Source and Target
- In Depth Coverage of Orchestration using ADF Pipeline
- Overview of Azure SQL and Azure Synapse Serverless and Dedicated Pool Features
- Implement ETL Logic using ADF Data Flow with Azure SQL as Source and Azure Synapse as Target
- Using Data Copy to copy data between different sources and targets
- Performance Tuning Scenarios of ADF Data Flow and Pipelines
- Build Big Data Solutions using Azure Databricks
- Overview of Spark SQL and Pyspark Data Frame APIs
- Build ELT Pipelines using Databricks Jobs and Workflows
- Orchestrate Databricks Notebooks using ADF Pipelines
Recommended Course: Database Management System (DBMS) & SQL: Complete Pack-2023
