Coursera

Prep for Microsoft Azure Data Engineer Associate Cert DP-203 (Coursera)

Offered by SkillUp EdTech,

This course will guide you on how to prepare for the DP-203: Data Engineering on Microsoft Azure certificate exam.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

By the end of this course, you will be able to:

Explain how to design data for analysis using Azure Databricks, Apache Spark, and Azure Synapse pipelines.
Describe how to ingest, clean, and transform data using Azure Synapse.
Identify data processing solutions using Azure Databricks and manage pipelines in Azure Synapse pipelines.
Discuss the steps to secure, optimize, and monitor data storage using Azure Synapse Analytics.

This course is designed for IT professionals who want to prepare for the Microsoft DP-203 exam and demonstrate their expertise in creating analytical solutions by integrating, transforming, and consolidating data from multiple data sources, such as structured, unstructured, and streaming data systems.
According to Microsoft, candidates for the DP-203 exam should have experience with operationalization of data pipelines and ensure that data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. You should be able to identify and troubleshoot operational and data quality issues, and design, implement, monitor, and optimize data platforms to meet the data pipelines.

What you'll learn

Explain how to design data for analysis using Azure Databricks, Apache Spark, and Azure Synapse pipelines.
Describe how to ingest, clean, and transform data using Azure Synapse.
Identify data processing solutions using Azure Databricks and manage pipelines in Azure Synapse pipelines.
Discuss the steps to secure, optimize, and monitor data storage using Azure Synapse Analytics.

Syllabus

Designing and implementing data storage and data exploration layer
In this module, you will be introduced to the Azure tools available for designing and implementing data storage and data exploration layers. You will gain insight into the three important data engineering tools: Azure Synapse Analytics, Azure Databricks, and Azure Data Lake. You will gain insight into the key concepts and strategies of data partitioning for files, focusing on both Azure Synapse Analytics and Azure Data Lake Storage Gen2. Additionally, you will learn to browse and search metadata in Microsoft Purview Data Catalog for data exploration. The module also delves into the process of creating and executing a compute solution in Azure, which will enable you to make informed decisions regarding data partitioning, enhancing data cataloging capabilities, and efficiently managing data storage and processing resources in Azure.

Developing data processing
In this module, you will gain an understanding of data ingestion and transformation techniques. You will also learn about Transact SQL for data transformation, Azure Synapse Pipelines for ETL, Apache Spark for processing, and exploratory data analytics. The module also discusses data loading with PolyBase, the creation of data pipelines, and integration with Jupyter and Python notebooks. Additionally, it delves into configuring data snapshots with Delta Lake and building stream processing solutions using Spark Structured Streaming. Finally, you will gain a comprehensive understanding of data processing options and techniques.

Securing, monitoring, and optimizing data storage and data processing
In this module, you will learn about the importance of implementing robust data security measures in Azure, encompassing data encryption, access control through Azure RBAC, and secure data handling. Additionally, you can learn the steps to monitor data storage, optimize query performance through indexers and caching, and troubleshoot Spark job and pipeline run failures effectively. This knowledge is vital for maintaining efficient data storage and retrieval, contributing to improved data processing and analytics. You will also gain insight into two critical areas of Azure data management: Data security and performance optimization. You will also learn some insights on implementing robust data security measures to protect sensitive information effectively.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

Alibaba Cloud Academy

Big Data Analysis Deep Dive (Coursera)

CS: Information & Technology

The job market for architects, engineers, and analytics professionals with Big Data expertise continues to increase. The Academy’s Big Data Career path focuses on the fundamental tools and techniques needed to pursue a career in Big Data. This course includes: data processing with python, writing and reading SQL queries, transmitting data with MaxCompute, analyzing data with Quick BI, using Hive, Hadoop, and spark on E-MapReduce, and how to visualize data with data dashboards. Work through our course material, learn different aspects of the Big Data field, and get certified as a Big Data Professional!

Sep 28th 2026

5-12 Weeks

Python SQL Big Data

Coursera

Talend

Talend Data Integration Certification Preparation training (Coursera)

CS: Information & Technology

Talend certification exams measure candidates’ skills to ensure that they have the knowledge to successfully implement quality projects. It is recommended to have at least 6 months experience using Talend products and general knowledge of data integration architecture and advanced features before preparing for a Talend certification.

Sep 21st 2026

3 Weeks

Data Integration Data Manipulation Data Migration

Coursera

LearnQuest

Developing AI Applications on Azure (Coursera)

Data Science

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning.

Sep 21st 2026

5-12 Weeks

ML Artificial Intelligence Machine Learning

Coursera

Università Bocconi

A Scientific Approach to Innovation Management (Coursera)

Business

How can innovators understand if their idea is worth developing and pursuing? In this course, we lay out a systematic process to make strategic decisions about innovative product or services that will help entrepreneurs, managers and innovators to avoid common pitfalls. We teach students to assess the feasibility of an innovative idea through problem-framing techniques and rigorous data analysis labelled ‘a scientific approach’.

Sep 21st 2026

5-12 Weeks

Innovation Management Data Analysis

Coursera

Northeastern University

Machine Learning in Healthcare: Fundamentals & Applications (Coursera)

Health & Society Data Science

Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.

Sep 28th 2026

4 Weeks

Healthcare Artificial Intelligence Machine Learning

Coursera

Icahn School of Medicine at Mount Sinai

Network Analysis in Systems Biology (Coursera)

Sci: Biology & Life Sciences

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools.

Sep 28th 2026

5-12 Weeks

Biology Network Analysis

Coursera

Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

Statistics & Data Analysis Data Science

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Sep 21st 2026

4 Weeks

NoSQL Machine Learning Big Data

Coursera

Edureka

Learn SQL with Databricks (Coursera)

CS: Software Engineering

Welcome to the Learn SQL with Databricks course, where you'll embark on a journey to acquire essential skills in database management, data analysis, and advanced data manipulation techniques. This course is meticulously designed to guide you through the intricacies of SQL, leveraging the powerful and versatile Databricks platform.

Sep 28th 2026

5-12 Weeks

Databases SQL Apache Spark

Coursera

Eindhoven University of Technology

Process Mining: Data science in Action (Coursera)

Statistics & Data Analysis Data Science

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Sep 21st 2026

5-12 Weeks

Business Data Mining Data Analysis

Coursera

University of Maryland, College Park

Combining and Analyzing Complex Data (Coursera)

Statistics & Data Analysis

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.

Sep 21st 2026

4 Weeks

Ethics Models Data Analysis

Coursera

University of London,University College London,CORE

Doing Economics: Measuring Climate Change (Coursera)

Statistics & Data Analysis Data Science

This course will give you practical experience in working with real-world data, with applications to important policy issues in today’s society. Each week, you will learn specific data handling skills in Excel and use these techniques to analyse climate change data, with appropriate readings to provide background information on the data you are working with. You will also learn about the consequences of climate change and how governments can address this issue.

Sep 21st 2026

4 Weeks

Economics Climate Change Data Analysis

Coursera

Google Cloud

Serverless Data Processing with Dataflow: Foundations (Coursera)

Statistics & Data Analysis Data Science

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend.

Sep 21st 2026

2 Weeks

Data Processing Dataflow Serverless