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DP-700 : Implementing Data Engineering Solutions Using Microsoft Fabric

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11 Labs

This track consists of 11 labs, each provided as a separate lab instance, and has to be completed in one uninterrupted session. Once you launch a lab, your session time begins. The estimated duration to complete each lab is around 3 Hours. This track and all associated labs will be available for 30 days once you redeem the voucher code. After 30 days, all labs and related data will be automatically deleted from the CloudLabs portal.

About Course

The DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric course
provides an introduction to designing and implementing data engineering solutions with Microsoft Fabric. It covers data ingestion, transformation, orchestration, and implementing architectures such as Lakehouse, data warehouses, and real-time intelligence. Learners will also explore how to secure, manage, and monitor data engineering solutions. This course is suitable for experienced data professionals interested in the Microsoft Certified: Fabric Analytics Engineer Associate certification.

Hands-on Labs Covered

  • Create a Microsoft Fabric Lakehouse
  • Analyze data with Apache Spark in Fabric
  • Ingest data with a pipeline in Microsoft Fabric
  • Create and use Dataflows (Gen2) in Microsoft Fabric
  • Use Delta Tables in Apache Spark
  • Create a medallion architecture in a Microsoft Fabric lakehouse
  • Get started with Real-Time Intelligence in Microsoft Fabric
  • Ingest real-time data with Eventstream in Microsoft Fabric
  • Work with data in a Microsoft Fabric eventhouse
  • Get started with Real-Time Dashboards in Microsoft Fabric
  • Analyze data in a data warehouse
  • Load data into a warehouse using T-SQL
  • Query a data warehouse in Microsoft Fabric
  • Monitor a data warehouse in Microsoft Fabric
  • Secure a Microsoft Fabric data warehouse
  • Monitor Fabric activity in the monitoring hub
  • Secure data access in Microsoft Fabric
  • Implement deployment pipelines in Microsoft Fabric

Audience

  • Data Engineer

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