For business inquiries : (+971) 561803315
For special requests : (+971) 561803315
For business inquiries : (+971) 561803315
For special requests : (+971) 561803315
Master Microsoft Azure Data Engineering for impactful analytics. Ideal for data professionals and BI experts, this course covers cloud computing, core data concepts, and advanced skills in designing and implementing data solutions on Azure. Elevate your data analytics game with our tailored program, perfect for data analysts and scientists in the Azure ecosystem.
Module 1: Get started with data engineering on Azure
Overview of Azure compute and storage options
Structuring the data lake for exploration, streaming, and batch workloads
Creating indexes on datasets (CSV, JSON, Parquet) for query acceleration
Introduction to Azure Data Lake Storage Gen2 and Synapse Analytics
Module 2: Build data analytics solutions using Azure Synapse serverless SQL pools
Querying Parquet and CSV files in a data lake using serverless SQL
Transforming data using a serverless SQL pool
Creating a lake database for structured querying
Implementing security through Role-Based Access Control (RBAC)
Module 3: Perform data engineering with Azure Synapse Apache Spark Pools
Analyzing and transforming data with Apache Spark
Using Delta Lake for transactional data storage
Exploring Spark notebooks for ETL activities
Performing tasks like removing duplicates, renaming columns, and aggregating data
Module 4: Ingest and load data into the data warehouse
Loading data into Synapse dedicated SQL pools with PolyBase and COPY
Analyzing data in a relational data warehouse
Implementing workload management for petabyte-scale data ingestion
Module 5: Transfer and transform data with Azure Synapse Analytics Pipelines
Building data integration pipelines to ingest and transform data
Using mapping data flows and data movement into data sinks
Implementing pipelines for data from multiple sources
Module 6: Work with hybrid transactional and analytical (HTAP) solutions using
Azure Synapse Analytics
Planning hybrid transactional and analytical processing
Implementing Azure Synapse Link with Azure Cosmos DB for real-time analytics
Scaling Stream Analytics jobs for increased throughput
Module 7: Implement a data streaming solution with Azure Stream Analytics
Processing streaming data with Azure Stream Analytics
Ingesting vehicle telemetry data into Event Hubs
Visualizing real-time data with Power BI
Module 8: Govern data across an enterprise
Introduction to Microsoft Purview for data cataloging and governance
Integrating Purview with Azure Synapse Analytics
Module 9: Recap of using Azure Databricks and data lakes
Exploring Azure Databricks for scalable data engineering and analysis
Using Apache Spark in Azure Databricks for various tasks
Running Databricks notebooks in Azure Data Factory
