Azure Data Engineer Associate Certification Course
Boost Up Your Career
Talent Arabia’s Data Engineering using Azure certification training course is curated by subject matter experts to help you clear the official Microsoft Azure Data Engineering Exam: DP-203. This course will help you gain expertise in Data Engineering and throughout the training, you will master the concepts such as Design and implement data storage, Design and develop data processing pipelines, implement Data security, Data factory and many more with industry-relevant use cases. This Azure Data Engineering Certification training ensures that you get hands-on experience in real-time projects. Learn Data Engineering from 10+ years of experienced industry experts.
Why Azure Data Engineering Certification Course from Talent Arabia
Course overview
Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. They also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. This professional deals with unanticipated issues swiftly and minimizes data loss. An Azure data engineer also designs, implements, monitors, and optimizes data platforms to meet the data pipeline needs.
Key features
Skills you will learn
- Design data storage solutions
- Monitor data storage and data processing
- Design and develop data processing
- Optimize data storage and data processing
- Design and implement data security
- Create and manage data pipelines
Course curriculum
- Azure Subscriptions
- Azure Resources
- Azure Free Tier Account
- Azure Resource Manager
- Azure Resource Manager Template
- Azure Storage
- Types of Azure Storage
- Understand the evolving world of data
- Data abundance
- Understanding the Data Engineering Problem
- Understand job responsibilities
- Understanding Data Engineering Processing - Extract Transform and Load
- Overview of Azure Data Engineering Services
- Understand data storage in Azure Storage
- Understand data storage in Azure Data Lake Storage
- Understand Azure Cosmos DB
- Understand Azure SQL Database
- Understand Azure Synapse Analytics
- Understand Azure Stream Analytics
- Understand Azure HDInsight
- Understand other Azure data services
- How to choose an Azure Storage Service in Azure
- Create an Azure Storage Account
- Connect an app to Azure Storage API
- Connect to your Azure storage account
- Explore Azure Storage security features
- Understand storage account keys
- Understand shared access signatures
- Control network access to your storage account
- Understand Advanced Threat Protection for Azure Storage
- Explore Azure Data Lake Storage security features
- Introduction to Blob storage
- What are blobs?
- Design a storage organization strategy
- Integrate data with Azure Data Factory or Azure Synapse Pipeline
- Understand Azure Data Factory
- Describe data integration patterns
- Explain the data factory process
- Understand Azure Data Factory components
- Azure Data Factory security
- Set-up Azure Data Factory
- Create linked services
- Create datasets
- Create data factory activities and pipelines
- Manage integration runtimes
- Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipeline
- List the data factory ingestion methods
- Describe data factory connectors
- Understand data ingestion security considerations
- Explain Data Factory transformation methods
- Describe Data Factory transformation types
- Exercise - Author an Azure Data Factory mapping data flow
- Debug mapping data flow
- Exercise - Use Data Factory wrangling data
- Exercise - Use compute transformations within Data Factory
- Exercise - Integrate SQL Server Integration Services packages within Data Factory
- Describe slowly changing dimensions
- Choose between slowly changing dimension types
- Exercise - Design and implement a Type 1 slowly changing dimension with mapping data flows
- Understand data factory control flow
- Work with data factory pipelines
- Debug data factory pipelines
- Add parameters to data factory components
- Exercise - Integrate a Notebook within Azure Synapse Pipelines
- Execute data factory packages
- Describe SQL Server Integration Services
- Understand the Azure-SIS integration runtime
- Set-up Azure-SIS integration runtime
- Run SSIS packages in Azure Data Factory
- Migrate SSIS packages to Azure Data Factory
- Configure a git repository with a development factory
- Create and merge a feature branch
- Deploy a release pipeline
- Visually monitor pipeline runs
- Integrate with Azure Monitor
- Set up alerts
- Rerun pipeline runs
- What is Azure Synapse Analytics5 min
- How Azure Synapse Analytics works8 min
- When to use Azure Synapse Analytics4 min
- Exercise - Explore Azure Synapse Analytics
- Create Azure Synapse Analytics workspace4 min
- Exercise - Create and manage Azure Synapse Analytics workspace4 min
- Describe Azure Synapse Analytics SQL5 min
- Explain Apache Spark in Azure Synapse Analytics7 min
- Exercise - Create pools in Azure Synapse Analytics9 min
- Orchestrate data integration with Azure Synapse pipelines7 min
- Exercise-Identifying Azure Synapse pipeline components8 min
- Visualize your analytics with Power BI7 min
- Understand hybrid transactional analytical processing with Azure Synapse Link
- Use Azure Synapse Studio
- Understand the Azure Synapse Analytical processes
- Explore the Data hub
- Explore the Develop hub
- Explore the Integrate hub
- Explore the Monitor hub
- Explore the Manage hub
- Describe a Modern Data Warehouse
- Define a Modern Data Warehouse Architecture
- Design ingestion patterns for a Modern Data Warehouse
- Understand data storage for a Modern Data Warehouse
- Understand file formats and structure for a modern data warehouse
- Prepare and transform data with Azure Synapse Analytics
- Serve data for analysis with Azure Synapse Analytics
- Describe a modern data warehouse
- Define a modern data warehouse architecture
- Exercise - Identify modern data warehouse architecture components
- Design ingestion patterns for a modern data warehouse
- Understand data storage for a modern data warehouse
- Understand file formats and structure for a modern data warehouse
- Prepare and transform data with Azure Synapse Analytics
- Understand data load design goals
- Explain load methods into Azure Synapse Analytics
- Manage source data files
- Manage singleton updates
- Set-up dedicated data load accounts
- Implement workload management
- Simplify ingestion with the Copy Activity
- Understand performance issues related to tables
- Understand table distribution design
- Use indexes to improve query performance
- Understand query plans
- Create statistics to improve query performance
- Improve query performance with materialized views
- Use read committed snapshot for data consistency
- How does statistics affect a query plan?
- Describe the integration methods between SQL and spark pools in Azure Synapse Analytics
- Understand the use-cases for SQL and spark pools integration
- Exercise: Integrate SQL and spark pools in Azure Synapse Analytics
- Externalize the use of spark pools within Azure Synapse Workspace
- Transfer data outside the synapse workspace using the PySpark connector
- Explore the development tools for Azure Synapse Analytics
- Understand transact-SQL language capabilities for Azure Synapse Analytics
- Scale compute resources in Azure Synapse Analytics
- Pause compute in Azure Synapse Analytics
- Manage workloads in Azure Synapse Analytics
- Use Azure Advisor to review recommendations
- Use dynamic management views to identify and troubleshoot query performance
- Understand skewed data and space usage
- Understand network security options for Azure Synapse Analytics
- Configure Conditional Access
- Configure authentication
- Manage authorization through column and row level security
- Exercise - Manage authorization through column and row level security
- Manage sensitive data with Dynamic Data Masking
- Implement encryption in Azure Synapse Analytics
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Use Apache Spark in Azure Databricks
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Get Started with Delta Lake
- Create Delta Lake tables
- Create and query catalog tables
- Use Delta Lake for streaming data
- Get started with SQL Warehouses
- Create databases and tables
- Create queries and dashboards
- Understand Azure Databricks notebooks and pipelines
- Create a linked service for Azure Databricks
- Use a Notebook activity in a pipeline
- Use parameters in a notebook
Course pricing
We provide following options for payment
- Internet Banking
- Direct Bank Transfer
- Credit/Debit Card
Azure Data Engineering Certification Description
You will need to know the fundamentals of Azure. As part of this course, you will get free Azure Fundamentals self-paced videos.
- Azure Databricks
- Azure Data Factory
- Azure Synapse
- Creating ETL Pipelines
- Data Warehousing using Azure SQL
- Azure Storage
- Integration with PySpark, PowerBI and Delta Lakes
Anyone with a zeal to learn and wants to become a Data Engineer or Data Architect can join this training.
You need a basic configuration with minimum 4GB RAM and i3 processor, with a working internet connection.
You will be using Azure Free Tier Account to do the practicals.
Azure Data Engineering Certification
Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. They also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. This professional deals with unanticipated issues swiftly and minimizes data loss. An Azure data engineer also designs, implements, monitors, and optimizes data platforms to meet the data pipeline needs.
Microsoft Azure Data Engineer Certification assist users in understanding the data through exploratory research. They design and maintain safe and legally compliant data processes employing various tools and methods. They employ a variety of Azure language and data service options to create and store data that is cleansed and improved to be analyzed. Azure Data Engineer help ensure data pipelines and data stores run at a high level and are efficient, organized, reliable, and reliable for business requirements and limitations. They address unexpected problems rapidly and reduce the loss of data. They also create, set up, monitor, analyze, and optimize data platforms to meet the requirements of the data pipeline.
Candidates for this exam should have subject matter expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.
This is an online exam and cost for the exam is $165 ( Price based on the country or region in which the exam is proctored).
Candidates are allowed 2 hours to complete the DP-203 Exam.
All technical exam scores are reported on a scale of 1 to 1,000. A passing score is 700 or greater. As this is a scaled score, it may not equal 70% of the points. A passing score is based on the knowledge and skills needed to demonstrate competence as well as the difficulty of the questions.
The exam can taken in the following languages: English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia).
- Design and implement data storage (40—45%)
- Design and develop data processing (25—30%)
- Design and implement data security (10—15%)
- Monitor and optimize data storage and data processing (10—15%)
- If you don’t pass an exam the first time, you must wait 24 hours before retaking it.
- A 14-day waiting period is imposed between all subsequent attempts (up to 5).
- You may not take a given exam more than five (5) times within a 12-month period from the first attempt. If you failed the exam 5 times, you’ll be eligible to retake it again 12 months from the date of your first attempt.
- You cannot retake an exam you’ve passed unless your certification has expired.
- You must pay to retake the exam (if applicable.)
- For most exams, you’ll have results within minutes of finishing the exam. You’ll also get a report with your exam score and feedback on your performance. Exams with labs take about 30 minutes to score, so you’ll have to wait a bit longer. Your score will be available in your Learn profile within 24 hours.
Once you earn your certification, don’t let it expire. When you have an active certification that’s expiring within six months, you should renew it—at no cost—by passing a renewal assessment on Microsoft Learn. Remember to renew your certification annually if you want to retain it.
This certificate will be provided by Microsoft to professionals who are able to prove their expertise and knowledge through successfully passing DP-203 exam.
- Completely participate in this Azure Data Engineering Training Training Course.
- Evaluation and completion of the quizzes and projects listed.
Data engineering has become one of the most lucrative career options, with ample opportunities. Hence, the demand for data engineers is skyrocketing. AIMResearch estimates that the data engineering market will grow at a CAGR of 36.7% in the next five years, growing from USD 18.2 billion in 2022 to USD 86.9 billion in 2027.
Beginners can become familiar with Azure data engineering services easily as it is a user-friendly cloud based platform. Learning its capabilities and functionality requires appropriate direction and a well-structured training path. Beginners interested in a career in data engineering using Azure can sign up for our training and earn certificates to demonstrate their expertise in this domain.
It is a globally popular certification that endorses your capability on building efficient data pipelines and analytics solutions.The demand for Azure Data Engineering is on the rise, and there are many profitable employment possibilities and positions in tech organizations, making this the ideal time for candidates to enroll and earn certification. Due to the wide range of job options and prospects, learning Azure Data Engineering skills and starting to work straight away is also strongly recommended.
It is a globallOur Azure Data Engineering Training certification course is designed to develop skills and evaluate candidates' knowledge. Following the completion of this certification, you will have access to a wide range of job possibilities. Some of the most important employment roles include Azure Data Engineer, Data Engineering Manager, Data Engineer, Azure Architect, and others.y popular certification that endorses your capability on building efficient data pipelines and analytics solutions.The demand for Azure Data Engineering is on the rise, and there are many profitable employment possibilities and positions in tech organizations, making this the ideal time for candidates to enroll and earn certification. Due to the wide range of job options and prospects, learning Azure Data Engineering skills and starting to work straight away is also strongly recommended.
Microsoft Azure Data Engineer Certification FAQs
The skills of a data engineer are highly sought-after at present. You could be qualified for jobs such as data engineer or data warehouse manager, database administrator and business intelligence analyst, and even data architect. Start the beginning step of registering for the Data Engineering course.
There isn't a specific college degree required to become a Data Engineer. People in this field typically hold a bachelor's or master's degree in physics, computer science, applied math, statistics, or other related fields. Even if you don't possess the educational qualifications, this Data Engineer course will help you start your career.
Data engineers are paid an average salary that is more than 830k annually for India and $116K for those in the U.S. With the addition of relevant experience and getting an industry-respected certification, such as Talent Arabia's Data Engineer course, there is no reason to believe that you won't make more money.
The difficulty level in the Microsoft Azure Data Engineer Certification depends entirely on the individual's prior experience and commitment to learning the concepts. Our highly trained instructors explain everything in a way that is easy to comprehend. They are also aware of the students' requirements.
Although not necessary, it is highly recommended that you have a basic understanding of programming before pursuing a job in Data Engineering.
Java, Python, R, SQL, and Scala are the most widely utilized languages in the area of Data Engineering
To become a data engineer, individuals must be proficient in programming languages such as Java, Python, or C++. They must be proficient in relational databases like MySQL and Oracle Database and writing SQL queries. Creating a data warehouse operating system, operating systems Apache Spark, data mining, and data modeling are the other crucial skills for an engineer in data. This Data Engineer course will assist you in developing these abilities.
- India - INR 795,486
- The UK - £43,401
- Canada - CA $81,288
- The US - $94,358
The Azure DP 203 certification exam is for candidates working in the Data engineer role on Microsoft Azure. To be a Microsoft Certified Azure Data Engineer, you must be proficient in data computation languages such as SQL, Python, or Scala and parallel processing, as well as data architecture concepts.
Data engineers can quickly complete large tasks due to the computing power in BigQuery, Snowflake, Firebolt, Databricks, and other cloud warehousing solutions. This shift from open-source and on-prem solutions to cloud and managed SaaS allows engineers to focus on tasks unrelated to managing databases. Since data engineers are not responsible for managing storage and computing, their role has changed in the direction of the infrastructure development to more performance-focused elements in the information stack or even more specialized functions.