Skip to content
ictcLogo
  • About
  • Training
  • Learning Paths
  • Training Center
  • News
  • Contact
Menu
  • About
  • Training
  • Learning Paths
  • Training Center
  • News
  • Contact
Microsoft

Course DP-203T00-A: Data Engineering on Microsoft Azure

  • Duration: 4 days
  • Job Role: Data Engineer
  • Exam: DP-203

Course DP-203T00-A: Data Engineering on Microsoft Azure

Share This Learning Path

Need more info? Contact us

In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

Audience Profile

The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Prerequisites

  • Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
  • Specifically completing: AZ-900 - Azure Fundamentals, DP-900 - Microsoft Azure Data Fundamentals.

Course outline

Module 1: Explore compute and storage options for data engineering workloads

Module Overview

This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

Lessons

Introduction to Azure Synapse Analytics
Describe Azure Databricks
Introduction to Azure Data Lake storage
Describe Delta Lake architecture
Work with data streams by using Azure Stream Analytics

Lab Sessions

Explore compute and storage options for data engineering workloads

Lab Lessons

Combine streaming and batch processing with a single pipeline
Organize the data lake into levels of file transformation
Index data lake storage for query and workload acceleration

After completing this module, students will be able to:

Describe Azure Synapse Analytics.
Describe Azure Databricks.
Describe Azure Data Lake storage.
Describe Delta Lake architecture.
Describe Azure Stream Analytics.

Module 2: Design and implement the serving layer

Module Overview

This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

Lessons

Design a multidimensional schema to optimize analytical workloads
Code-free transformation at scale with Azure Data Factory
Populate slowly changing dimensions in Azure Synapse Analytics pipelines

Lab Sessions

Designing and Implementing the Serving Layer

Lab Lessons

Design a star schema for analytical workloads
Populate slowly changing dimensions with Azure Data Factory and mapping data flows

After completing this module, students will be able to:

Design a star schema for analytical workloads.
Populate a slowly changing dimensions with Azure Data Factory and mapping data flows.

Module 3: Data engineering considerations for source files

Module Overview

This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

Lessons

Design a Modern Data Warehouse using Azure Synapse Analytics
Secure a data warehouse in Azure Synapse Analytics

Lab Sessions

Data engineering considerations

Lab Lessons

Managing files in an Azure data lake
Securing files stored in an Azure data lake

After completing this module, students will be able to:

Design a Modern Data Warehouse using Azure Synapse Analytics.
Secure a data warehouse in Azure Synapse Analytics.

Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

Module Overview

In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

Lessons

Explore Azure Synapse serverless SQL pools capabilities
Query data in the lake using Azure Synapse serverless SQL pools
Create metadata objects in Azure Synapse serverless SQL pools
Secure data and manage users in Azure Synapse serverless SQL pools

Lab Sessions

Run interactive queries using serverless SQL pools

Lab Lessons

Query Parquet data with serverless SQL pools
Create external tables for Parquet and CSV files
Create views with serverless SQL pools
Secure access to data in a data lake when using serverless SQL pools
Configure data lake security using Role-Based Access Control (RBAC) and Access Control List

After completing this module, students will be able to:

Understand Azure Synapse serverless SQL pools capabilities.
Query data in the lake using Azure Synapse serverless SQL pools.
Create metadata objects in Azure Synapse serverless SQL pools.
Secure data and manage users in Azure Synapse serverless SQL pools.

Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

Module Overview

This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

Lessons

Understand big data engineering with Apache Spark in Azure Synapse Analytics
Ingest data with Apache Spark notebooks in Azure Synapse Analytics
Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Lab Sessions

Explore, transform, and load data into the Data Warehouse using Apache Spark

Lab Lessons

Perform Data Exploration in Synapse Studio
Ingest data with Spark notebooks in Azure Synapse Analytics
Transform data with DataFrames in Spark pools in Azure Synapse Analytics
Integrate SQL and Spark pools in Azure Synapse Analytics

After completing this module, students will be able to:

Describe big data engineering with Apache Spark in Azure Synapse Analytics.
Ingest data with Apache Spark notebooks in Azure Synapse Analytics.
Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics.
Integrate SQL and Apache Spark pools in Azure Synapse Analytics.

Module 6: Data exploration and transformation in Azure Databricks

Module Overview

This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

Lessons

Describe Azure Databricks
Read and write data in Azure Databricks
Work with DataFrames in Azure Databricks
Work with DataFrames advanced methods in Azure Databricks

Lab Sessions

Data Exploration and Transformation in Azure Databricks

Lab Lessons

Use DataFrames in Azure Databricks to explore and filter data
Cache a DataFrame for faster subsequent queries
Remove duplicate data
Manipulate date/time values
Remove and rename DataFrame columns
Aggregate data stored in a DataFrame

After completing this module, students will be able to:

Describe Azure Databricks.
Read and write data in Azure Databricks.
Work with DataFrames in Azure Databricks.
Work with DataFrames advanced methods in Azure Databricks.

Module 7: Ingest and load data into the data warehouse

Module Overview

This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

Lessons

Use data loading best practices in Azure Synapse Analytics
Petabyte-scale ingestion with Azure Data Factory

Lab Sessions

Ingest and load Data into the Data Warehouse

Lab Lessons

Perform petabyte-scale ingestion with Azure Synapse Pipelines
Import data with PolyBase and COPY using T-SQL
Use data loading best practices in Azure Synapse Analytics

After completing this module, students will be able to:

Use data loading best practices in Azure Synapse Analytics.
Petabyte-scale ingestion with Azure Data Factory.

Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

Module Overview

This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

Lessons

Data integration with Azure Data Factory or Azure Synapse Pipelines
Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Lab Sessions

Transform Data with Azure Data Factory or Azure Synapse Pipelines

Lab Lessons

Execute code-free transformations at scale with Azure Synapse Pipelines
Create data pipeline to import poorly formatted CSV files
Create Mapping Data Flows

After completing this module, students will be able to:

Perform data integration with Azure Data Factory.
Perform code-free transformation at scale with Azure Data Factory.

Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

Module Overview

In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

Lessons

Orchestrate data movement and transformation in Azure Data Factory

Lab Sessions

Orchestrate data movement and transformation in Azure Synapse Pipelines

Lab Lessons

Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

After completing this module, students will be able to:

Orchestrate data movement and transformation in Azure Synapse Pipelines.

Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

Module Overview

In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.

Lessons

Optimize data warehouse query performance in Azure Synapse Analytics
Understand data warehouse developer features of Azure Synapse Analytics

Lab Sessions

Optimize Query Performance with Dedicated SQL Pools in Azure Synapse

Lab Lessons

Understand developer features of Azure Synapse Analytics
Optimize data warehouse query performance in Azure Synapse Analytics
Improve query performance

After completing this module, students will be able to:

Optimize data warehouse query performance in Azure Synapse Analytics.
Understand data warehouse developer features of Azure Synapse Analytics.

Module 11: Analyze and Optimize Data Warehouse Storage

Module Overview

In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.

Lessons

Analyze and optimize data warehouse storage in Azure Synapse Analytics

Lab Sessions

Analyze and Optimize Data Warehouse Storage

Lab Lessons

Check for skewed data and space usage
Understand column store storage details
Study the impact of materialized views
Explore rules for minimally logged operations

After completing this module, students will be able to:

Analyze and optimize data warehouse storage in Azure Synapse Analytics.

Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Module Overview

In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

Lessons

Design hybrid transactional and analytical processing using Azure Synapse Analytics
Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark pools
Query Azure Cosmos DB with serverless SQL pools

Lab Sessions

Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Lab Lessons

Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark for Synapse Analytics
Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics

After completing this module, students will be able to:

Design hybrid transactional and analytical processing using Azure Synapse Analytics.
Configure Azure Synapse Link with Azure Cosmos DB.
Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics.
Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics.

Module 13: End-to-end security with Azure Synapse Analytics

Module Overview

In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

Lessons

Secure a data warehouse in Azure Synapse Analytics
Configure and manage secrets in Azure Key Vault
Implement compliance controls for sensitive data

Lab Sessions

End-to-end security with Azure Synapse Analytics

Lab Lessons

Secure Azure Synapse Analytics supporting infrastructure
Secure the Azure Synapse Analytics workspace and managed services
Secure Azure Synapse Analytics workspace data

After completing this module, students will be able to:

Secure a data warehouse in Azure Synapse Analytics.
Configure and manage secrets in Azure Key Vault.
Implement compliance controls for sensitive data.

Module 14: Real-time Stream Processing with Stream Analytics

Module Overview

In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

Lessons

Enable reliable messaging for Big Data applications using Azure Event Hubs
Work with data streams by using Azure Stream Analytics
Ingest data streams with Azure Stream Analytics

Lab Sessions

Real-time Stream Processing with Stream Analytics

Lab Lessons

Use Stream Analytics to process real-time data from Event Hubs
Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
Scale the Azure Stream Analytics job to increase throughput through partitioning
Repartition the stream input to optimize parallelization

After completing this module, students will be able to:

Enable reliable messaging for Big Data applications using Azure Event Hubs.
Work with data streams by using Azure Stream Analytics.
Ingest data streams with Azure Stream Analytics.

Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Module Overview

In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

Lessons

Process streaming data with Azure Databricks structured streaming

Lab Sessions

Create a Stream Processing Solution with Event Hubs and Azure Databricks

Lab Lessons

Explore key features and uses of Structured Streaming
Stream data from a file and write it out to a distributed file system
Use sliding windows to aggregate over chunks of data rather than all data
Apply watermarking to remove stale data
Connect to Event Hubs read and write streams

After completing this module, students will be able to:

Process streaming data with Azure Databricks structured streaming.

Module 16: Build reports using Power BI integration with Azure Synpase Analytics

Module Overview

In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.

Lessons

Create reports with Power BI using its integration with Azure Synapse Analytics

Lab Sessions

Build reports using Power BI integration with Azure Synpase Analytics

Lab Lessons

Integrate an Azure Synapse workspace and Power BI
Optimize integration with Power BI
Improve query performance with materialized views and result-set caching
Visualize data with SQL serverless and create a Power BI report

After completing this module, students will be able to:

Create reports with Power BI using its integration with Azure Synapse Analytics.

Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Module Overview

This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.

Lessons

Use the integrated machine learning process in Azure Synapse Analytics

Lab Sessions

Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Lab Lessons

Create an Azure Machine Learning linked service
Trigger an Auto ML experiment using data from a Spark table
Enrich data using trained models
Serve prediction results using Power BI

After completing this module, students will be able to:

Use the integrated machine learning process in Azure Synapse Analytics.

Book Your Seat​

Find Learning Paths​

  • Search Paths

  • Vendors

Latest Learning Paths​

Microsoft

Course MB-920T00-A: Microsoft Dynamics 365 Fundamentals (ERP)

  • Dynamics-365
  • Beginner

Microsoft

Course PL-600T00-A: Power Platform Solution Architect

  • Power-Platform
  • Advanced

Microsoft

Course 20703-1-B: Administering System Center Configuration Manager

  • Windows
  • Advanced

Join our community of certified professionals

Sign Up to our newsletter, and stay always up to date with latest IT certifications

About Us

ICTC is the leader in technical certification courses and exams. Our labs consist of a latest tech PCs and our instructors are certified from each vendor

Facebook Linkedin

Learn

View all the provided certifications and there relevant courses. Book online for a certification exam.

Explore

Contact Us

  • +30 211 500 29 00
  • info@ictc.gr
  • Lagoumitzi 24, Kallithea
ictcLogo

International Computer Training Center

  • Copyright reserved to ICTC
  • Proudly Crafted by GTP Works

Copyright reserved to ICTC. Proudly Crafted by GTP Works

Choose how to get more info...

Give as a call

211 500 2 900

Let us, call you

Send us an email