Data Engineering on Microsoft Azure DP-203

The DP-203 Data Engineering on Microsoft Azure Course is an educational program that guides participants through the skills and knowledge needed to become expert data engineers in the Microsoft Azure ecosystem. The course is designed to teach participants how to design, build, operate, and secure data processing solutions on Azure. During the course, participants will focus on using various Azure services and technologies to build and operate scalable and efficient data engineering solutions. They will learn how to work with Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Stream Analytics, among other services. The course also covers designing and implementing data storage solutions, real-time and batch data processing, and managing data security and compliance. The classes are structured to provide a balance of theory and practice, allowing participants to understand key concepts and immediately apply the skills they have learned through exercises and projects. This approach ensures that participants have a solid theoretical foundation and solid practical experience in working with data engineering solutions on Azure. This course helps prepare you for the Azure Data Engineer Associate Certification exam.

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Course Objectives

Below is a summary of the main objectives of the Data Engineering on Microsoft Azure DP-203 Course :

Learn how to use Azure services for data engineering.
Design and manage data processing solutions.
Work with tools like Azure Data Factory and Azure Databricks.
Manage data security and compliance.
Apply the skills acquired through exercises and projects.
Data Storage and Management: Learn strategies for efficient data storage and management on Azure, including use cases for Azure Blob Storage, Azure SQL Database, and Azure Cosmos DB.
Real-time Data Processing: Explore techniques and tools for implementing real-time data processing solutions on Azure, leveraging services like Azure Stream Analytics and Event Hubs.
Big Data and Advanced Analytics: Gain insights into handling big data workloads on Azure, including integrating with services such as Azure Synapse Analytics and applying advanced analytics techniques for data-driven decision-making

Course Certification

This course helps you prepare to take the:
Exam DP-203 Azure Data Engineer Associate ;

Course Outline

Implement a partition strategy

Implement a partition strategy for files
Implement a partition strategy for analytical workloads
Implement a partition strategy for streaming workloads
Implement a partition strategy for Azure Synapse Analytics
Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design and implement the data exploration layer

Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
Recommend and implement Azure Synapse Analytics database templates
Push new or updated data lineage to Microsoft Purview
Browse and search metadata in Microsoft Purview Data Catalog

Ingest and transform data

Design and implement incremental loads
Transform data by using Apache Spark
Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics
Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
Transform data by using Azure Stream Analytics
Cleanse data
Handle duplicate data
Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery
Handle missing data
Handle late-arriving data
Split data
Shred JSON
Encode and decode data
Configure error handling for a transformation
Normalize and denormalize data
Perform data exploratory analysis

Develop a batch processing solution

Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
Use PolyBase to load data to a SQL pool
Implement Azure Synapse Link and query the replicated data
Create data pipelines
Scale resources
Configure the batch size
Create tests for data pipelines
Integrate Jupyter or Python notebooks into a data pipeline
Upsert data
Revert data to a previous state
Configure exception handling
Configure batch retention
Read from and write to a delta lake

Develop a stream processing solution

Create a stream processing solution by using Stream Analytics and Azure Event Hubs
Process data by using Spark structured streaming
Create windowed aggregates
Handle schema drift
Process time series data
Process data across partitions
Process within one partition
Configure checkpoints and watermarking during processing
Scale resources
Create tests for data pipelines
Optimize pipelines for analytical or transactional purposes
Handle interruptions
Configure exception handling
Upsert data
Replay archived stream data

Manage batches and pipelines

Trigger batches
Handle failed batch loads
Validate batch loads
Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
Schedule data pipelines in Data Factory or Azure Synapse Pipelines
Implement version control for pipeline artifacts
Manage Spark jobs in a pipeline

Implement data security

Implement data masking
Encrypt data at rest and in motion
Implement row-level and column-level security
Implement Azure role-based access control (RBAC)
Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
Implement a data retention policy
Implement secure endpoints (private and public)
Implement resource tokens in Azure Databricks
Load a DataFrame with sensitive information
Write encrypted data to tables or Parquet files
Manage sensitive information

Monitor data storage and data processing

Implement logging used by Azure Monitor
Configure monitoring services
Monitor stream processing
Measure performance of data movement
Monitor and update statistics about data across a system
Monitor data pipeline performance
Measure query performance
Schedule and monitor pipeline tests
Interpret Azure Monitor metrics and logs
Implement a pipeline alert strategy

Optimize and troubleshoot data storage and data processing

Compact small files
Handle skew in data
Shop computer games
Optimize resource management
Tune queries by using indexers
Tune queries by using cache
Troubleshoot a failed Spark job
Troubleshoot a failed pipeline run, including activities executed in external services

Course Mode

Instructor-Led Remote Live Classroom Training;

Trainers

Trainers are authorized Instructors in Microsoft and certified in other IT technologies, with years of hands-on experience in the industry and in Training.

Lab Topology

For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.

Course Details

Course Prerequisites

Participation in the Azure Administrator course is recommended .

Course Duration

Intensive duration 4 days;

Course Frequency

Course Duration: 4 days (9.00 to 17.00) - Ask for other types of attendance.

Course Date

Azure Security Technologies Course (Intensive Formula) – On request – 09:00 – 17:00

Steps to Enroll

Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com