Problem
Being a Data Engineer is one of the most popular professions for the Microsoft SQL Server Data Platform. Is there a Microsoft certification to become a Data Engineer?
Solution
This article covers what to know and how to prepare for the Data Engineering on Microsoft Azure certification exam. The Exam DP-203 is one of the most popular exams for one of the most popular jobs. In this article, we will show some books, links, courses, and videos to help you pass the exam.
What is the Microsoft DP-203 Exam?
It is a professional exam to become certified as a Data Engineer working with Azure technologies, such as Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage, Azure Databricks, and other related technologies.
What Does a Data Engineer Do?
Data Engineers are responsible for designing how to store, collect, process, and analyze data. They build the data pipelines, integrate the data, work with Big Data technologies, help improve scalability and performance, protect the data, automate the process, and coordinate with Data Scientists.
What are the Main Differences Between a Data Engineer and a Data Scientist?
The main difference is that the Data Scientist is focused on data analysis, while the Data Engineer focuses on extracting, processing, and protecting the data. The engineer is in charge of the data maintenance, updates, etc., and the scientist gets insights and knowledge from the data.
Is the Exam DP-203 Difficult?
It is a challenging exam as it covers many topics like Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage, Azure Databricks, etc. If you have a lot of experience with those Azure technologies and many years working as a Data Engineer on Azure, this exam may not be so difficult. However, if you don’t have experience with those technologies, I strongly recommend trying easier exams first, like the AZ-900.
What Books Do You Recommend to Study for the Exam?
The following books can help you:
- Azure Data Engineer Associate Certification Guide: A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam
- MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 1st Edition
- DP 203: Data Engineering on Microsoft Azure +290 Exam Practice Questions with detail explanations and reference links: Second Edition – 2022 Kindle Edition
- Azure Data Engineering Cookbook: Get well versed in various data engineering techniques in Azure using this recipe-based guide, 2nd Edition 2nd ed. Edition
- The Definitive Guide to Azure Data Engineering: Modern ELT, DevOps, and Analytics on the Azure Cloud Platform 1st ed.
- Data Engineering on Azure
What Courses Would You Recommend for this Exam?
The following courses will help you prepare:
- Course DP-203T00: Data Engineering on Microsoft Azure
- DP-203 – Data Engineering on Microsoft Azure 2023
- DP-203 Exam Preparation: Data Engineering on Microsoft Azure
- Data Engineering on Microsoft Azure (DP-203)
Which Links Can Help Me to Pass this Exam?
These links should be researched:
Design and Implement Data Storage
Devise and Execute a Data Storage Plan
- Devise a plan for partitioning files
- Establish a partitioning plan for analytical workloads
- Create a partitioning plan for streaming workloads
- Implement a partitioning strategy for Azure Synapse Analytics
- Identify situations where partitioning is necessary in Azure Data Lake Storage Gen2
Architect and Implement the Data Exploration Layer
- Create and execute queries using SQL serverless and a Spark cluster
- Propose and implement database templates for Azure Synapse Analytics
- Upload new or modified data lineage to Microsoft Purview
- Browse and search through metadata using Microsoft Purview Data Catalog
Develop and Processing Data
Ingest and Transform the Data
- Plan and implement incremental loading processes
- Transform data using Apache Spark
- Transform data using T-SQL in Azure Synapse Analytics
- Ingest and transform data using Azure Synapse Pipelines or Azure Data Factory
- Transform data with Azure Stream Analytics
- Cleanse data
- Manage duplicate data
- Prevent duplicate data using Azure Stream Analytics Exactly Once Delivery
- Handle missing data
- Manage late-arriving data
- Divide data
- Break down JSON
- Decode and encode data
- Set up error handling for a transformation
- Standardize and denormalize data
- Conduct exploratory data analysis
Develop a Batch Processing Solution
- Create solutions for batch processing using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory.
- Use PolyBase to handle data in a SQL pool
- Develop an Azure Synapse Link and query replicated data
- Establish data pipelines
- Adjust resource scaling
- Set up batch sizes
- Develop tests for data pipelines
- Integrate Python or Jupyter notebooks into a data pipeline
- Upsert data
- Revert data to previous states
- Set up exception handling
- Set up batch retention
- Read and write to a delta lake
Implement a Stream Processing Solution
- Formulate a stream processing solution using Stream Analytics and Azure Event Hubs
- Handle data using Spark structured streaming
- Create aggregated windows
- Manage schema drift
- Analyze data in a time series format
- Handle data distributed across partitions
- Operate on data within a single partition
- Set up checkpoints and watermarking during processing
- Adjust resource scaling
- Develop tests for data flow systems
- Enhance pipelines for analytical or transactional efficiency
- Manage disruptions
- Configure procedures for handling exceptions
- Update existing data with upsert operations
- Replay previously stored streaming data
Manage Batches and Pipelines
- Initiate batches
- Manage failed batch loads
- Verify batch loads
- Administer data pipelines in Azure Synapse Pipelines or Azure Data Factory
- Schedule data pipelines in Azure Synapse Pipelines or Data Factory
- Use version control in pipeline artifacts
- Administer Spark jobs in pipelines
Secure, Optimize, and Monitor Data Storage and Data Processing
Develop Data Security
- Encrypt data at rest and in transit
- Enforce security at the row and column levels
- Apply Azure Role-Based Access Control (RBAC)
- Employ access control lists (ACLs) similar to POSIX for Data Lake Storage Gen2
- Define a policy for retaining data
- Set up secure endpoints, both private and public
- Utilize resource tokens within Azure Databricks
- Populate a DataFrame with confidential information
- Store encrypted data in tables or Parquet files
- Administer sensitive information
Monitor Data Processing and Data Storage
- Set up logging functionality for Azure Monitor usage
- Customize monitoring services configuration
- Supervise the processing of data streams
- Evaluate the efficiency of data transfer
- Keep track of and refresh statistics regarding data throughout a system
- Evaluate the performance of data pipelines
- Assess query performance
- Plan and oversee tests for scheduled pipelines
- Interpret metrics and logs from Azure Monitor
- Establish a strategy for pipeline alerts
Improve and Troubleshoot Data Processing and Data Storage
- Condense small files
- Handle data skew
- Manage data spill
- Improve resource management
- Fine-tune queries using indexers
- Fine-tune queries using cache
- Troubleshoot a failed Spark job
- Address issues in a failed pipeline run, which includes examining activities performed in external services.
Next Steps
For more information about Azure, check out these links:
- DP 500 Certification Exam Preparation for Microsoft Azure and Power BI
- Study material for exam AZ-203 Developing Solutions for Microsoft Azure
- Create Azure Data Lake Database, Schema, Table, View, Function and Stored Procedure
- Azure Data Factory Pipeline to fully Load all SQL Server Objects to ADLS Gen2
