Azure Synapse Analytics

• Basic understanding of cloud computing principles
• Knowledge of data warehousing principles and practices

Audience

• Data engineers
• Data scientists
• Cloud professionals

Azure Synapse Analytics, formerly known as SQL Data Warehouse, is a limitless analytics service that provides tools for both enterprise data warehousing and Big Data analytics.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data engineers who wish to become proficient in utilizing Azure Synapse Analytics for a wide range of data processing, analytics, and visualization.

By the end of this training, participants will be able to:

• Understand the core concepts, architecture, and components of Azure Synapse Analytics.
• Create, configure, and manage scalable data warehouses using Azure Synapse.
• Master the techniques for ingesting, transforming, and loading data (ETL) from various sources into Azure Synapse.
• Optimize query performance, secure data, and integrate Azure Synapse with Power BI and other tools to visualize data and share insights.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Azure Synapse Analytics

• Overview of Azure Synapse Analytics
• Key features and capabilities
• Comparison with other data analytics solutions

Setting up Azure Synapse Analytics

• Creating an Azure Synapse workspace
• Configuring data lake storage
• Managing users and access control

Data Integration and Management

• Data ingestion methods
• ETL processes with Azure Data Factory
• Data storage and organization strategies

Developing Big Data Solutions

• Introduction to Spark pools in Azure Synapse
• Processing large datasets with Spark
• Integrating with Azure Machine Learning

Querying Data with Serverless SQL Pools

• Overview of serverless SQL pools
• Querying across data sources
• Performance tuning and optimization

Security and Compliance

• Data protection and encryption
• Access control and data privacy
• Monitoring and auditing

Data Visualization and Analysis

• Connecting to Power BI and other visualization tools
• Building dashboards and reports
• Sharing insights within and outside an organization

Managing Resources and Best Practices

• Architectural best practices
• Managing and scaling resources

Summary and Next Steps