Intermediate Level

Microsoft Certified Azure Data Engineer Associate (DP-203)

★ 4.8 (300 learners) 60 hrs total 16 modules

Microsoft Azure Entra ID Power BI Databricks Apache Spark Azure Data Factory +2 more

Only 7 seats left · Next batch starts 12 Sept 2026

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60 hrs: of content
16 modules: in curriculum
16 projects: hands-on builds
Intermediate: difficulty

Curriculum covers

Frameworks you'll master

8 tools & frameworks

Microsoft Azure Entra ID Power BI Databricks Apache Spark Azure Data Factory Python Microsoft Fabric

Course Curriculum

16 modules · 60 hours · 24 live sessions

Introduction 1 sessions

Introduction to Azure
The Azure Free Account
Creating an Azure Free Account
Tour around the Azure Portal
Disabling security defaults

Azure Basic Services 1 sessions

Create Azure Account
Account, Subscription, Resources Groups & Resources
Different Ways to Access Azure
Azure Portal Walkthrough
Getting Ready with Azure CLI
Azure Cloud Shell
Create VM from Portal
Create VM From CLI

Design and implement Data Storage Basics 3 sessions

Understanding Data
Different Services for Azure Storage
Azure Storage Platform
Provision Azure Storage Account
Explore Azure Storage Account
What is Azure Blob Storage
Explore Azure Blob Storage Part 1
Explore Azure Blob Storage Part 2
Data Replication in Storage
Manual Failover
Blob Access Tier
Secure Storage Account
Azure Table
Azure Queue
Azure File Share
Azure Disk Storage
Introduction to Azure Data Lake Gen 2
Lifecycle Management
Azure Storage Explorer
Manage Azure Storage via Azure CLI Part 1
Manage Azure Storage via Azure CLI Part 2
Manage Azure Storage via Azure CLI Part 3
Manage Azure Storage via Azure CLI Part 4
Manage Azure Storage via Azure CLI Part – 5
The different data formats
We need different types of storage services
Lab Creating an Azure Storage Account
Lab Working with the Azure Storage account
Example of an application accessing data
Example of an application accessing data – Resources
The Azure Data Lake Gen2 storage account
Lab Azure Data Lake Gen2 storage accounts
Lab Uploading data to Azure Data Lake Gen2
Getting PowerBI
Using PowerBI to view your data

Design and implement Data Storage – Overview on TransactSQL 2 sessions

Section Introduction
Introduction to Azure SQL Database
Azure SQL IaaS & PaaS Offering
Different Paas Deployment
[HandsOn] Azure SQL Database Part 1
[HandsOn] Azure SQL Database Part 2
[HandsOn] Azure SQL Database Part 3
[HandsOn] Azure SQL Database Part 4
SQL Pricing Model & Service Tier
Manage Azure SQL via Azure CLI Part – 1
Manage Azure SQL via Azure CLI Part 2
The Azure SQL database service
Lab Setting up a new Azure SQL database
Lab Installing Azure Data Studio
Lab Using Azure Data Studio on macOS
Lab TSQL SELECT clause
Lab TSQL WHERE clause
Lab TSQL ORDER BY clause
Lab TSQL Aggregate Functions
Lab TSQL GROUP BY clause
Lab Using PARTITION BY
Lab LEAD and LAG functions
Lab WITH Clause
Lab TSQL Create Table command
Lab TSQL Foreign Key constraints

Azure NoSQL Solution: Cosmos DB 1 sessions

Introduction to Azure Cosmos DB
APIs in Azure Cosmos DB
Azure Cosmos DB for NoSQL
Components of Azure Cosmos DB for NoSQL

Azure NoSQL Solution: Cosmos DB Part 2 1 sessions

Lab1: Creating Azure Cosmos DB Account for NoSQL Part 1
Lab1: Creating Azure Cosmos DB Account for NoSQL Part 2
Lab1: Creating Azure Cosmos DB Account for NoSQL Part 3
Overview: Azure Cosmos DB Account for NoSQL
Lab 2: Creating a Database Part 1
Lab 2: Creating a Database Part 2
Lab 3: Creating a Container
Lab 4: Adding Items to Container
Request Units
Understand Throughput
Cost Management
Cosmos DB Horizontally Scalable
Partition and Partition Key
Single vs Cross Partition
Avoid Hot Partitions
TimetoLive (TTL)
Serverless Capacity Mode
Serverless vs Provisioned Throughput
Auto scale vs Standard Throughput
Azure Cosmos DB Pricing

Design and implement Data Storage – Azure Synapse Analytics 3 sessions

A quick view on the costing aspect when using Azure Synapse
A data warehouse
Welcome to Azure Synapse Analytics
Lab Let's create an Azure Synapse workspace
About the serverless SQL pool
Quick note on Microsoft Entra ID and permissions
Lab Using External tables CSV Part 1
Lab Using External tables CSV Part 2
Lab External Tables Parquet file
Lab External Tables Multiple Parquet files
Lab OPENROWSET JSON files
The dedicated SQL pool
Lab Creating a SQL pool
Lab SQL Pool External Tables CSV
Lab SQL Pool External Tables Parquet
Lab External table Hidden files and folders
Pausing the SQL Pool
Lab Loading data into a SQL pool using PolyBase
Lab Loading data into a table COPY Command CSV
Lab Loading data into a table COPY Command Parquet
Lab Loading data Pipelines Storage accounts
Lab Loading data Pipelines Azure SQL database
Designing a data warehouse
Fact and Dimension Tables
Lab Building a Fact Table
Lab Building a dimension table
Lab Transfer data to our SQL Pool
Lab Using Power BI for Star Schema
Understanding Azure Synapse Architecture
Understanding table types
Lab Creating Hashdistributed Tables
Lab Creating Replicated Tables
Fact as Hash and Dimensions as Replicate
More on designing tables
Lab Surrogate keys for dimension tables
Slowly Changing dimensions
Indexes in Azure Synapse
Which Load Method to use
Partitions in Azure Synapse
Lab Creating a table with partitions
Lab Switching partitions
Lab CASE statement
What about the Spark Pool
Using the dedicated SQL pool

Design and implement Data Processing – Azure Data Factory 3 sessions

Costing aspect when it comes to Azure Data Factory
Extract, Transform and Load
What is Azure Data Factory
Starting with Azure Data Factory
Lab Azure Data Lake to Azure Synapse
Lab Generating a Parquet file
Lab Creating a pipeline from scratch CSV to Parquet
Lab Creating a pipeline from scratch Parquet to Synapse
About the Copy data activity
Lab Transferring data from our Azure SQL database
Mapping Data Flow
Lab Mapping Data Flow Fact Table
Lab Pipeline Mapping data flows
Lab Mapping Data Flow Dimension Table DimCustomer
Lab Mapping Data Flow Dimension Table DimProduct
Lab Derived Column
Lab Surrogate Keys Dimension tables
Data Flow Debug feature
Lab Azure Data Factory Cache Sink Setup
Lab Azure Data Factory Cache Sink Implementation
Lab Generating JSON data
Lab Loading JSON into SQL Pool
Lab Processing JSON Arrays
Lab Processing JSON objects
SelfHosted Integration Runtime
Lab SelfHosted runtime Building the machine
Lab SelfHosted runtime Setting up a web server
Lab SelfHosted runtime Installing the runtime
Lab SelfHosted Runtime Copy Activity
Lab SelfHosted Runtime Mapping Data Flow
Lab Conditional Split
Using Aggregate Transformation
About Schema Drift
Lab Get Metadata Activity
Lab For Each Activity
Lab Using the Stored Procedure Activity
Lab Using the Lookup Activity
Lab Running a pipeline based on a storage event
Lab Running a pipeline based on a schedule
Lab Running a pipeline based on a tumbling window
Azure Data Factory and Git
Integrating Azure Data Factory and GitHub
Azure Data Factory Git Creating a pipeline
Azure Data Factory Git Pull request

Design and Develop Data Processing – Azure Event Hubs, Stream Analytics 2 sessions

Costing aspect when it comes Streaming services
Batch and RealTime Processing
What are Azure Event Hubs
Lab Creating an instance of Event hub
Lab Azure Event Hubs Sending data
What are Azure Stream Analytics
Lab Creating a Stream Analytics job
Lab Azure Stream Analytics Defining the input
Lab Azure Stream Analytics Defining the output
Lab Azure Stream Analytics Defining the query
Lab Reading Blob data
Lab Sending Diagnostic log data
Lab Formulating our query
Lab Azure Event Hubs Capture
Debugging your jobs
About Windowing functions
Lab Tumbling Window Setup
Lab Tumbling Window Implementation
Having multiple queries in the job
Quick note on other windowing functions
Lab Reference data
Lab Reading Network Security Group Logs Server Setup
Lab Reading Network Security Group Logs Enabling NSG Flow Logs
Understanding the NSG Flow Log structure
Starting with the query
Formulating our query
Finalizing our query
Signing up for Power BI service
Lab Power BI Output
Using Azure Data Factory Mapping Data Flow
Using Azure Data Factory Pipelines
Common query patterns

Design and Develop Data Processing – A Look at Spark 1 sessions

Why Spark
Spark Architecture
Installing Spark Virtual Machine setup
Installing Spark Implementation
Installing Spark Implementation Resources
Running Python on Spark
Running Python on Spark Resources
Using Notebooks
Python Data types
Python List collection
Spark Python Data Frames
Spark Python Data Frames from file
Using Scala in notebooks
Scala Using Variables
Spark Scala Data Frames
Spark Scala Data Frame from file

Design and Develop Data Processing – Azure Synapse and Azure Databricks 3 sessions

Azure Synapse Spark Pool Concepts
Lab Azure Synapse Creating a Spark pool
Spark Pool Starting out with Notebooks Scala
Spark Pool Starting out with Notebooks Python
Lab Spark Pool Load data
Lab Spark Pool Working with data
6Lab Spark Pool Grouping data
Lab Spark Pool Saving to a delta table
Lab Spark Pool Write data to Azure Synapse
Lab Spark Pool Shared tables
Azure Synapse Database templates
Spark Pool Write data to Azure Synapse Python code
What is Azure Databricks
Concepts with Azure Databricks
Lab Creating a workspace
Lab Creating a cluster
Note on Compute for Azure Databricks
Lab Loading data from a file
Lab Reading from Azure Data Lake
Lab Working with data
Lab Group By and Visualizations
Lab Few functions on dates
Lab JSONbased files
Lab Saving to a table
Lab Using the COPY INTO command
Lab Streaming data from DataLake
Lab Performing transformation on the streaming data
Lab Specifying the schema
Lab Writing data to Azure Synapse SQL Dedicated Pool
Lab Reading data from Azure Synapse
Lab Versioning of tables
Lab Running an automated job
Lab Azure Data Factory Running a notebook
Lab Streaming from Azure Event Hub Setup
Lab Streaming from Azure Event Hub Implementation
Deleting the Azure Databricks resource

Getting Started with Microsoft Fabric 1 sessions

Start Free Trial on Microsoft Fabric
Microsoft Fabric UI Overview
Creating ADLS Gen 2 Storage Account
File Formats: Parquet & Delta

Ingest Data with Microsoft Fabric 1 sessions

Create and Use Dataflows (Gen2) in Microsoft Fabric Part 1
Create and Use Dataflows (Gen2) in Microsoft Fabric Part 2
Ingest Data with a Pipeline in Microsoft Fabric Part 1
Ingest Data with a Pipeline in Microsoft Fabric Part 2
Analyze Data with Apache Spark: Create Lakehouse and Notebook
Analyze Data with Apache Spark: Create a DataFrame
Analyze Data with Apache Spark: Explore Data in a DataFrame
Analyze Data with Apache Spark: Use Spark to Transform Data Files
Analyze Data with Apache Spark: Work with Tables and SQL
Analyze Data with Apache Spark: Visualize Data with Spark

Implement Lakehouse with Microsoft Fabric 1 sessions

Create Microsoft Fabric Lakehouse Part 1
Create Microsoft Fabric Lakehouse Part 2
Use Delta Tables in Apache Spark Part 1
Use Delta Tables in Apache Spark Part 2
Use Delta Tables in Apache Spark Part 3
Use Delta Tables in Apache Spark Part 4
Use Delta Tables in Apache Spark Part 5
Medallion Architecture in Fabric Lakehouse Part 1
Medallion Architecture in Fabric Lakehouse Part 2
Medallion Architecture in Fabric Lakehouse Part 3
Medallion Architecture in Fabric Lakehouse Part 4
Medallion Architecture in Fabric Lakehouse Part 5
Medallion Architecture in Fabric Lakehouse Part 6

Implement RealTime Intelligence with Microsoft Fabric 1 sessions

Create an Eventstream
Eventhouse, KQL Database, and KQL Queryset
Eventstream to Eventhouse
Real Tile Analysis and Dashboards

Monitor and Optimize Data Storage and Data Processing 1 sessions

Microsoft Purview
Creating a Microsoft Purview account
Microsoft Purview Azure Data Lake
Microsoft Purview Azure Synapse
Microsoft Purview Azure Data Factory
Best practices for data storage Azure Data Lake
Azure Data Lake Gen2 Access tiers
Azure Data Lake Gen2 Look at Access tiers
Azure Data Lake Gen2 lifecycle policies
View on Azure Monitor
Lab Azure Data Factory Alert Rules
Azure Data Factory Logging
Azure Data Factory Annotations
Azure Data Factory Note Scaling up the runtime
Azure Data Factory Note Troubleshooting
Azure Synapse Monitoring Quick Overview
Azure Synapse System Views
Azure Synapse Workload Management
Azure Synapse Result set caching
Note Azure Synapse Data Skew
Azure Synapse Log Analytics
Azure Stream Analytics Optimization
Azure Stream Analytics The importance of time
Azure Databricks Monitoring

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Batch Schedule

⚡ Seats filling fast — only 7 remaining across all batches

Available Batches
Batch Type	Schedule	Duration	Next Start Date	Availability
Weekend	Sat & Sun, 10am–1pm	10 weeks	12 Sept 2026	Available
Weekday	Mon–Fri, 7–9pm	8 weeks	14 Sept 2026	Available
Fast-track	Mon–Sun, 6 hours/day	2 weeks	21 Sept 2026	Available

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