As a candidate for this certification, you should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions.
As an Azure data engineer, you help stakeholders understand the data through exploration, and build and maintain secure and compliant data processing pipelines by using different tools and techniques. You use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including:
- Modern data warehouse (MDW)
- Big data
- Lakehouse architecture
As an Azure data engineer, you also help to ensure that the operationalization of data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. You help to identify and troubleshoot operational and data quality issues. You also design, implement, monitor, and optimize data platforms to meet the data pipelines.
As a candidate for this certification, you must have solid knowledge of data processing languages, including:
- SQL
- Python
- Scala
You need to understand parallel processing and data architecture patterns. You should be proficient in using the following to create data processing solutions:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Stream Analytics
- Azure Event Hubs
- Azure Data Lake Storage
- Azure Databricks
- Design and implement data storage
- Develop data processing
- Secure, monitor, and optimize data storage and data processing
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 includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
There aren't specific prerequisites listed for the DP-203 exam, but Microsoft generally recommends that candidates have practical experience with Azure data services and a good understanding of data engineering concepts. The following two courses can be considered as recommendations before going for DP-203
- Lesson: Introduction to data engineering on Azure
- Lesson: Introduction to Azure Data Lake Storage Gen2
- Lesson: Introduction to Azure Synapse Analytics
- Exercise: Explore Azure Synapse Analytics
- Lesson: Use a serverless SQL pool to query files in a data lake
- Lesson: Use a serverless SQL pool to transform data
- Lesson: Create a lake database
- Exercise: Transform files using a serverless SQL pool
- Lesson: Analyze data with Apache Spark in Azure Synapse Analytics
- Lesson: Transform data with Apache Spark in Azure Synapse Analytics
- Lesson: Use Delta Lake in Azure Synapse Analytics
- Exercise: Transform data using Spark in Synapse Analytics
- Exercise: Use Delta Lake in Azure Synapse Analytics
- Lesson: Analyze data in a relational data warehouse
- Lesson: Load data into a relational data warehouse
- Exercise: Load data into a data warehouse
- Lesson: Build a data pipeline in Azure Synapse Analytics
- Lesson: Use Spark Notebooks in an Azure Synapse Pipeline
- Exercise: Build a data pipeline in Azure Synapse Analytics
- Exercise: Use an Apache Spark notebook in a pipeline
- Lesson: Plan hybrid transactional and analytical processing
- Lesson: Implement Azure Synapse Link with Azure Cosmos DB
- Lesson: Implement Azure Synapse Link for SQL
- Exercise: Implement Azure Synapse Link for Cosmos DB
- Lesson: Get started with Azure Stream Analytics
- Lesson: Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
- Lesson: Visualize real-time data with Azure Stream Analytics and Power BI
- Exercise: Ingest streaming data into Azure Synapse Analytics
- Lesson: Introduction to Microsoft Purview
- Lesson: Integrate Microsoft Purview and Azure Synapse Analytics
- Exercise: Integrate Azure Synapse Analytics and Microsoft Purview
- Lesson: Explore Azure Databricks
- Lesson: Use Apache Spark in Azure Databricks
- Lesson: Run Azure Databricks notebooks in Azure Data Factory
- Exercise: Use Spark in Azure Databricks
Microsoft Azure Data Engineering Training (DP-203) teaches participants how to design and implement data solutions using Azure services.
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 is data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
The course covers a variety of topics related to data engineering, including designing data storage solutions, ingesting and processing data, implementing data security, and monitoring and optimizing data solutions.
Participants will gain skills in designing and implementing data solutions using Azure services, as well as in data processing and transformation, data security, and monitoring and optimization.
Participants should have experience working with data, including data processing and transformation. Some familiarity with Azure services is also helpful. Specifically, you should have completed Microsoft Azure Fundamentals Training (AZ-900T00) and Microsoft Azure Data Fundamentals Training (DP-900).
