Become a Microsoft Azure Data Engineer?

The Examinations

You must clear the DP-203 exam to earn this certification. The DP-200 exam is about configuration & implementation. The DP-201 exam is about design.

Exam DP-200

The following is a list of the topics covered in the DP-200 exam and the relative weight of each section:

• Make use of data storage possibilities (40-45 percent)

• Improve and organize data processing (25-30 percent)

• Data solutions should be evaluated and improved regularly (30-35 percent)

I won't go through every detail in the exam guide, but I will go over the essential points.

The vital section of the exam guide is implementing data storage systems. Non-relational data stores and relational datastores are the two types of datastores.SQL Server was Microsoft's principal relational data solution for many years. You could run SQL Server in a virtual machine on Azure to migrate from on-premises SQL Server to Azure, but in most cases, you'd be better off using Azure SQL Database instead.

It has the advantages of being a managed service with several built-in capabilities that make scaling and providing high availability, disaster recovery, and worldwide distribution simple. You'll also need to know how to set up all those options. SQL Database isn't identical to SQL Server, but it's close enough that transferring to it shouldn't be too difficult. If you require full SQL Server compatibility, SQL Database Managed Instance is the way to go.

Azure Synapse Analytics, formerly known as Azure SQL Data Warehouse, is another relational data storage service. As its name suggests, it's intended for analytics rather than transaction processing. It gives you the ability to store and analyze massive amounts of data. Polybase is the quickest way to bring data into Synapse Analytics. Thus it's critical to understand how to use it. You must divide the datastore into numerous shards and apply the appropriate distribution mechanism to make searches as fast and efficient as possible.

Security is critical for SQL Database and Synapse Analytics, limiting access to data and things like masking for credit card numbers and encrypting a whole database.

That covers relational database services, but what about datastores that aren't relational? Unstructured data, such as documents or movies, can be stored in these systems. Blob storage, which is a highly available, extremely durable space to store digital things of any form, is the most mature Azure offering in this category. Blob storage, unlike a filesystem, has a flat structure. That is, the objects which are not adequately in order in a folder hierarchy. It's possible to make it appear that way using clever naming conventions, but it's simply a phony tree structure.

To create a proper hierarchical structure, you can utilize Azure Data Lake Storage Gen2, built on Blob storage. It's especially beneficial for systems that process large amounts of data, such as Azure Databricks.

Cosmos DB is the final non-relational datastore you'll need to know for the exam. This database system is remarkable because it can scale internationally without compromising performance or flexibility. It can also handle a variety of data models, such as document, key-value, graph, and wide column. Another intriguing aspect is the capacity to support five various levels of consistency, ranging from firm to eventual.

You must understand how to configure partitioning, security, high availability, disaster recovery, and global distribution for Cosmos DB, just as you do for SQL Database and Synapse Analytics.

The next portion of the exam guide covers data processing solutions management and development. Batch processing and stream processing both are subsections. Azure Data Factory and Azure Databricks are the two most vital batch processing services.

Data Factory facilitates copying data from one datastore to another, such as Blob storage to SQL Database, simple. Thanks to services like Databricks behind the scenes, it also makes data transformation simple. You may even link together a series of transformation operations started by a trigger that responds to an event to develop complicated automated processing pipelines.

Databricks is a managed data analytics solution provided by Azure. It builds on Apache Spark, well-known open-source analytics and machine learning technology. Although Spark tasks will execute on Azure HDInsight, Databricks is the preferred solution. Thus you'll need to be comfortable with it for the exam. Data ingestion, clusters, notebooks, jobs, and autoscaling are just a few of the Databricks topics discussed.

Azure Stream Analytics is the essential stream processing service. You must understand how to import data from other services, process data streams using various windowing techniques, and export the results to another service.

Monitoring and optimizing data solutions is the final component of the exam guide. Azure Monitor is the essential service for this segment, as it allows you to monitor and configure alerts for practically every other Azure service. Log Analytics is a significant feature of Azure Monitor that you may utilize to implement auditing.

New services are not in the optimization section. Instead, you'll need to know how to make services like Stream Analytics, SQL Database, and Synapse Analytics function better. One of the most potent optimization approaches is to choose the appropriate partitioning mechanism.

Finally, because the DP-200 exam is all about implementation and configuration, you must configure data services in the Azure portal. Therefore the exam includes tasks that you must do in a live lab! If you're concerned about how you'll get the requisite amount of hands-on experience, see the section below on Exam Preparation.

Exam DP-201

The following are the topics covered in the DP-201 exam, as well as the relative weight of each part:

• Develop Azure data storage solutions (40-45 percent )

• Develop data processing solutions (25-30 percent )

• Data security and compliance are built-in features (25-30 percent )

The DP-201 exam focuses on design, whereas the DP-200 exam focuses on execution. As a result, it emphasizes planning and concepts more than getting things set up.

The crucial section of the exam guide is about designing data storage systems. You'll need to know which Azure services to promote to meet your company goals. The relational data stores like Azure SQL Database and Azure Synapse Analytics and non-relational data stores like Cosmos DB, Data Lake Storage Gen2, and Blob storage. You'll need to know how to design for all of the above services:

  • Partitioning and data distribution
  • High scalability, taking multiple areas, latency, and throughput into account
  • recovery from disasters, and
  • Availability is high.

Designing data processing solutions is the subject of the next portion of the exam guide. Batch processing and stream processing are the two types of processing. You'll need to know how to use Azure Data Factory and Azure Databricks to create batch processing solutions. You'll need to know how to use Stream Analytics and Azure Databricks to develop solutions for stream processing. As you can see, Azure Databricks is a critical data processing service because it utilizes batch and stream processing. You must also understand how to consume data from other Azure services and output the results to other Azure services.

The exam guide concludes with a section on data security and compliance. To begin, you must understand how to safeguard your datastores. The most crucial decision is which authentication mechanism to utilize for different scenarios. For example, relying on Azure Active Directory authentication rather than embedding an access key in your application code is usually preferable. ACLs (Access Control Lists) and role-based access control are also crucial.

The second half of this essay is about creating data regulations and standards that are secure. The following are some of the themes covered:

  • Encryption, for example, Data Transparency Encryption
  • Auditing of data
  • Data masking, such as the obfuscation of credit card numbers, is an example.
  • Data classification and data privacy
  • Retention of data
  • Archiving
  • Purging

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