Explore the data science process and learn how to train machine learning models to accomplish artificial intelligence in Microsoft Fabric.

Stream processing enables you to capture and analyze data in real-time. Azure Stream Analytics is a cloud-based stream processing engine that you can use to build highly scalable real-time analytics solutions.

Explore the data warehousing process and learn how to load, monitor, secure, and query a warehouse in Microsoft Fabric.

This learning path introduces the foundational components of implementing a data lakehouse with Microsoft Fabric.

Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.

Implement SQL Server-based options for authentication and authorization, as well as Azure options for securing Azure SQL databases. Explore encryption, firewalls and advanced threat protection.

This learning path takes analysts through visualizing data in Power BI. You'll learn about visuals in Power BI reports, using Power BI to visualize real-time data, and visualizing data in paginated reports. This learning path helps you prepare for the Azure Enterprise Data Analyst Certification.

Learn how to work with Azure Arc enabled servers, data services and Kubernetes.

Azure Synapse Link for SQL enables low-latency synchronization of operational data in a relational database to Azure Synapse Analytics.

Explore data classification capabilities and degrees of confidentiality. Implement security options to maintain private data safe, including Azure SQL auditing, Microsoft Defender for SQL, row-level security, Dynamic Data Masking and Azure SQL Database Ledger.

Transact-SQL is a powerful declarative language that lets you explore and manipulate your database. As the complexity of your programs increase, so does the risk of errors occurring.

Generative Artificial Intelligence (AI) engineering with Azure Databricks uses the platform's capabilities to explore, fine-tune, evaluate, and integrate advanced language models. By using Apache Spark's scalability and Azure Databricks' collaborative environment, you can design complex AI systems.