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.
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.
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:
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:
An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.
You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.
Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.
That’s because you’ll need to complete the projects correctly. Here are the most important ones:
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_Do you wonder how companies use the data they collect? _why does it matter?
How do they convert their collected data into useful information? How do they develop solutions for using this data?
If such questions pique your curiosity, then the field of big data engineering will undoubtedly interest you.
It’s a vast field with a bright scope in India, that covers data collection, data processing, and many other areas.
In this article, we’ll discuss the field of data engineering and help you find out how to become a big data engineer.
Ready? Let’s get started.
Table of Contents
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Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.
Here, we list some of the important skills that one should possess to build a successful career in big data.
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