This course: Microsoft Fabric Analytics Engineer (DP-600), covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric.
Participants in the Microsoft Fabric Analytics Engineer (DP-600) training will develop their analytics experience and learn to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, data streams, data pipelines, and semantic models, to create and deploy analytics assets.
Module 1: Data Ingestion with Dataflows Gen2 in Microsoft Fabric
Module 2: Data Ingestion with Spark and Microsoft Fabric Notebooks
Module 3: Using Data Factory Pipelines in Microsoft Fabric
Module 4: Getting Started with Lakehouses in Microsoft Fabric
Module 5: Organizing a Fabric Lakehouse using Medal Architecture Design
Module 6: Using Apache Spark in Microsoft Fabric
Module 7: Working with Delta Lake Tables in Microsoft Fabric
Module 8: Getting Started with Data Warehouses in Microsoft Fabric
Module 9: Loading Data into a Microsoft Fabric Data Warehouse
Module 10: Querying a Data Warehouse in Microsoft Fabric
Module 11: Monitoring a Microsoft Fabric Data Warehouse
Module 12: Understanding Scalability in Power BI
Module 13: Creating Model Relationships in Power BI
Module 14: Using Tools to Optimize Power BI Performance
Module 15: Applying Security to Power BI Models
In the constantly evolving world of data science, standing out as a Data Analytics Engineer requires mastering the most advanced tools. Microsoft Fabric, with its second-generation (Gen2) Dataflows and integrated pipelines, provides a robust platform for manipulating and ingesting data on a large scale. Understanding and leveraging these Dataflows enables you to effectively orchestrate complex data streams, ensuring smooth integration within your analytics pipelines.
Data ingestion is reimagined with Spark and notebooks within Microsoft Fabric. The ability to easily connect to data, write into a lakehouse, and consider practical applications opens new horizons for data analytics engineers. This approach allows for unmatched flexibility and analytical power, propelling the capabilities of data exploration and transformation.
Creating and managing efficient pipelines with Data Factory under Microsoft Fabric transcends mere technical understanding. The judicious use of the Copy Data activity, pipeline templates, and the ability to execute and monitor these pipelines transform raw data into valuable insights, crucial for any strategic decision-making.
The concept of a Lakehouse, a significant innovation in the Microsoft Fabric ecosystem, revolutionizes the way data is stored, accessed, and analyzed. By exploring and transforming data within a Lakehouse, engineers unlock advanced analytical perspectives, going well beyond the traditional capabilities of data lakes and data warehouses.
Adopting the medal architecture in managing Microsoft Fabric’s Lakehouses is not just about structure, but a comprehensive strategy to optimize access, security, and the efficiency of data analysis. This methodology enables sophisticated querying and reporting, essential for navigating the modern data ocean.
Mastering Apache Spark within Microsoft Fabric equips data analytics engineers with a powerful arsenal for data processing. Whether it’s running Spark code, manipulating dataframes, or using Spark SQL, the possibilities are vast and allow for high-level data analysis and visualization.
Delta Lake introduces a transactional management layer to your data lakes, enabling more efficient and secure creation and manipulation of delta tables. This innovation opens the door to real-time analytics and data streaming applications, essential for today’s dynamic data environments.
By embracing these key skills and technologies within Microsoft Fabric, data analytics engineers position themselves at the forefront of data science innovation, ready to tackle the challenges of tomorrow and transform data into strategic decisions.
Monday to Friday
8:30 AM to 6:00 PM
Tel. 058 307 73 00
ITTA
Route des jeunes 35
1227 Carouge, Suisse
Monday to Friday, from 8:30 am to 06:00 pm.
