Empire Data Systems

Microsoft Azure Fabric and Data Engineering Training(Online)

By Pavan, Microsoft Certified Azure Fabric and Data Engineer!!! (*****)

Course Description

Our Azure Data Engineering course will help IT professionals become subject matter experts in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures suitable for building analytics solutions.

Key topics covered include:
  • Azure Data Factory (ADF): Learn how to create, schedule, and orchestrate data pipelines to move and transform data at scale.
  • Azure Databricks: Gain expertise in using this powerful analytics platform to process big data and perform advanced analytics with Apache Spark.
  • Microsoft Fabric: Understand how to leverage this unified platform for data integration, data engineering, and data analytics.

Responsibilities for this role include helping stakeholders understand the data through exploration, building, and maintaining secure and compliant data processing pipelines by using different Azure tools and techniques.

An Azure Data Engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. This professional deals with unanticipated issues swiftly and minimizes data loss. An Azure Data Engineer also designs, implements, monitors, and optimizes data platforms to meet the data pipeline needs.

This course prepares you for the Microsoft Certification DP-700 (Fabric Data Engineer Associate) & DP-600 (Fabric Analytics Engineer Associate). Students will also get Hands-on Labs plus over 250+ Practice questions for the exam.

For course details and registration, please get in touch with Daniel at +1 267 718 1533 (Mobile & Whatsapp). We are based in Philadelphia, USA, and host affordable and comprehensive SQL Server/Azure/AWS/DevOps training programs for students around the globe.

Student Demographic

Course Introduction Video

Course Information

Pavan's Profile - Microsoft Certified Azure Data Engineer

Mr. Pavan, a Microsoft Certified Azure Data Engineer (DP 203) with five years of hands-on Microsoft Azure Data stack experience. His expertise is with Azure Data Platform includes Azure Databricks, Delta Lake, Data Factory, Synapse, HDInsight, Data Catalog, and Cosmos DB. Besides, he also has significant experience in Big Data infrastructure and Software development. He has a great passion for mentoring students, and he will take you deep into the Azure Data Platform domain.

Pavan's Certifications

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Live Training Videos

We believe in letting our prospective students to watch recorded videos of our live training classes and decide for themselves. If you would still like to attend a one-on-one live demo session, please give call Daniel @ 267 718 1533 and he can schedule one for you at your convenience.

Course Content

Module 1: Introduction to Azure Data factory

  • Understand Azure Data Factory components
  • Create linked services
  • Create datasets
  • Manage integration runtime

Module 2: Data Movement using Data Factory Control Flow

  • Understand data factory control flow activities
  • Develop the data factory pipelines using copy activity for data movement
  • Add parameters to data factory components
  • Debug data factory pipelines & data flows
  • Setup triggers & Alerts for pipelines

Module 3: Data transformation using Data Factory

  • Build Mapping Data Flow for data transformation
  • Add parameters to mapping data flows
  • Debug data flows from pipelines
  • Implement Slowly Changing Dimension using mapping data flows

Module 4: Introduction to Azure Databricks

  • Provision an Azure Databricks workspace
  • Ingest data using Azure Databricks.
  • Using the different data exploration tools in Azure Databricks.
  • Analyse data with Data Frame APIs.
  • Use Spark to process and analyse data stored in files.
  • Use Spark to visualize data

Module 5: : Manage data with Databricks Delta Lake

  • What is Databricks Delta Lake
  • How to manage ACID transactions using Delta Lake
  • How to use schema versioning and time travel in Delta Lake
  • How to maintain data integrity with Delta Lake

Module 6: SQL Warehouses in Azure Databricks

  • Create and configure SQL Warehouses in Azure Databricks
  • Create databases and tables
  • Create queries and dashboards

Module 7: Run Azure Databricks Notebooks with Azure Data Factory

  • Run Azure Databricks notebooks from ADF Pipelines.
  • Create an Azure Data Factory linked service for Azure Databricks.
  • Use a Notebook activity in a pipeline.
  • Pass parameters to a notebook.

Module 8: Introduction to Microsoft Fabric

  • Overview of Microsoft Fabric
  • Microsoft Fabric Terminology
  • Copilot in Microsoft Fabric
  • Microsoft Fabric settings
  • Working with Workspaces
  • Discovering data in OneLake catalog
  • Managing a Workspace with git

Module 9: Getting started with Data Engineering Experiences using Microsoft Fabric

  • Overview of Data Engineering in Microsoft Fabric
  • Working with Lakehouse
  • Data Factory in Microsoft Fabric
  • Working with Data Pipelines in Microsoft Fabric
  • Ingesting Data with Dataflows Gen2

Module 10: Data Engineering using Apache Spark, Delta Lakes and Notebooks

  • Introduction to Spark compute in Microsoft Fabric
  • Apache Spark job definition
  • Apache Spark monitoring in Microsoft Fabric
  • Delta Lake tables optimization and V-Order
  • Using Microsoft Fabric Notebooks

Module 11: Real-Time Intelligence in Microsoft Fabric

  • Work with Eventhouse
  • Get, process and route data in Eventstreams
  • Get data in KQL Database
  • Real-Time data processing using Event Processor
  • Query data from a KQL Queryset
  • Monitor and visualize your data
  • Drive alerts and actions from your data with Activator

Module 12: Implement Microsoft Fabric Data Warehousing

  • Create a Data Warehouse in Microsoft Fabric
  • Data ingestion options
  • Pipelines and Data flows
  • Mirror databases in Fabric
  • Open Mirroring & Clone tables
  • Dimensional modelling in Microsoft Fabric Warehouse
  • Query and monitor the Data Warehouse

Module 13: Power BI semantic models in Microsoft Fabric

  • Choose appropriate storage modes for your semantic model
  • Create relationships between tables in a semantic model
  • Design dynamic elements to extend calculations in a semantic model
  • Enable large semantic model storage format and incremental refresh
  • Optimize DirectQuery models with table level storage.
  • Restrict access to Power BI model data with RLS.
  • Restrict access to Power BI model objects with OLS.

Course Statistics

4

Years of Experience

2156

Gratified Students

26

Training Batches

6542

Training Hours

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