The "Data Engineering on Microsoft Azure" course explores data engineering patterns and practices for building batch and real-time analytical solutions using Azure data platform technologies. Students will learn about core compute and storage technologies, design analytical serving layers, and manage data engineering considerations. The course covers interactive data exploration, ingestion techniques using Azure Synapse Analytics, Azure Databricks, Azure Data Factory, and transformation of data. It includes performance monitoring, security implementation, and utilizing data for creating dashboards or predictive models in Azure Synapse Analytics.
This course is tailored for data professionals, data architects, and business intelligence experts who aim to build and manage data engineering solutions and analytical platforms using Microsoft Azure. It is also relevant for data analysts and data scientists who work with analytical solutions built on Azure.
At Course Completion: You will be able to:
Explore Azure's compute and storage options for data engineering workloads.
Design and implement the serving layer for data solutions.
Understand considerations for effective data engineering.
Execute interactive queries using serverless SQL pools.
Explore, transform, and load data into the Data Warehouse with Apache Spark.
Perform data exploration and transformation in Azure Databricks.
Ingest and load data into the Data Warehouse.
Transform data using Azure Data Factory or Azure Synapse Pipelines.
Integrate data from notebooks with Azure Data Factory or Azure Synapse Pipelines.
Optimize query performance with dedicated SQL pools in Azure Synapse.
Analyze and optimize data warehouse storage.
Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link.
Implement end-to-end security with Azure Synapse Analytics.
Perform real-time stream processing with Stream Analytics.
Create a stream processing solution with Event Hubs and Azure Databricks.
Build reports using Power BI integration with Azure Synapse Analytics.
Conduct integrated machine learning processes within Azure Synapse Analytics.
Prerequisites:
Knowledge of cloud computing and core data concepts.
This course is tailored for data professionals, data architects, and business intelligence experts who aim to build and manage data engineering solutions and analytical platforms using Microsoft Azure. It is also relevant for data analysts and data scientists who work with analytical solutions built on Azure.
At Course Completion: You will be able to:
Explore Azure's compute and storage options for data engineering workloads.
Design and implement the serving layer for data solutions.
Understand considerations for effective data engineering.
Execute interactive queries using serverless SQL pools.
Explore, transform, and load data into the Data Warehouse with Apache Spark.
Perform data exploration and transformation in Azure Databricks.
Ingest and load data into the Data Warehouse.
Transform data using Azure Data Factory or Azure Synapse Pipelines.
Integrate data from notebooks with Azure Data Factory or Azure Synapse Pipelines.
Optimize query performance with dedicated SQL pools in Azure Synapse.
Analyze and optimize data warehouse storage.
Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link.
Implement end-to-end security with Azure Synapse Analytics.
Perform real-time stream processing with Stream Analytics.
Create a stream processing solution with Event Hubs and Azure Databricks.
Build reports using Power BI integration with Azure Synapse Analytics.
Conduct integrated machine learning processes within Azure Synapse Analytics.
Prerequisites:
Knowledge of cloud computing and core data concepts.
Professional experience with data solutions.
Completion of:
AZ-900 - Azure Fundamentals
DP-900 - Microsoft Azure Data Fundamentals
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