Microsoft Azure Data Engineer

Batch and Real-Time analytics | Azure Databricks | Azure Data Factory | Azure Synapse Analytics | Implementing Security | Design and Implement Data Storage| ETL & ELT Pipelines

Modules | + Lessons | 30 Labs | 40+ Projects | Mock Exams | CV Review| On-job Support



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Microsoft Azure Data Engineering Certificate


You will learn the various data platform technologies available, and how to take advantage of this technology to an organizations benefit.
This course will prepare you for exams DP-203 for implementing and designing Azure data solutions, enabling you to design and perform data management, monitoring, security and privacy using the complete Azure data services stack
You will learn how to connect Power BI with a data lake and how to use a data factory.
Each course teaches you the concepts and skills that are measured by the exam.
By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure

Five reasons why you should get this certification now

It makes you stand out from the crowd.

According to reports, around 1,500 new customers daily sign up to Azure which means every year over 540,000 new companies adopt Azure.

Updating your professional profiles with this certificate will elevate your job profile and increase the possibility of getting recruited.

Most of the people agree that certification has improved their earning and 86% of people have seen better job prospects after getting certified.

Azure is the fastest-growing enterprise Cloud platform leveraged by leading IT companies across the globe.

As a result of the increased demand for Microsoft Azure Data Engineers in the industry today, a CV with this certification holds a great advantage.

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What is Data Engineering ?

Data engineering is a vital aspect of company growth, network interactions, and predicting future trends Without data engineering, it would be impossible to make sense of the huge amounts of data that are available to businesses.

Why Microsoft Azure Data Engineer?

The world of data has evolved and the advent of cloud technologies is providing new opportunities for businesses to explore. In this course, you will learn the various data platform technologies available, and how a Data Engineer can take advantage of this technology to an organization’s benefit. This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure

You will be able to master these 12 critical core skills…

Design & Implement

Explore Compute & Storage

Azure Synapse Analytics

HTAP with Azure Synapse

Azure Data Factory

Explore & Transform

Data Exploration

Data Engineering Considerations

Ingest & Load Data

Orchestrate Data Movement

Optimize Query Performance

Optimize Data Warehouse

There are 12 Modules in this Professional Certificate



Course Introduction

  • What is Azure?
  • Why Is Azure Needed?
  • What is DP203?
  • Prerequisites
  • Learners
  • Learning Path with DP203
  • Demand for DP203
  • Companies Hiring for Data Engineers
  • Course Features
  • Course Outcomes
  • Course Components



Introduction to Azure

  • What Is Cloud Computing?
  • Advantages of Cloud Computing
  • Types of Cloud Computing
  • What is Azure?
  • Why Azure?
  • Service Domains in Azure
  • Role of Azure in Cloud Computing
  • Azure Vs. GCP Vs. AWS



Compute & Storage Options for Data Engineering Workloads

  • Azure Storage Account
  • Types of Storage Accounts
  • Need for Storage Account
  • General-Purpose Storage Accounts
  • Blob Storage
  • Queue
  • Table Storage
  • Azure Files
  • Blob Storage Account
  • Types of Access Tiers
  • Hot Access Tier
  • Cold Access Tier
  • Azure Storage Replication
  • Types of Azure Storage Replication
  • Locally Redundant Storage (LRS)
  • Zone Redundant Storage
  • Geo-Redundant Storage
  • Read Access Geo-Redundant Storage (RA-GRS)
  • Object Replication for Block Blob Storage
  • Azure SQL Database
  • Characteristics of Azure SQL Database
  • Types of Deployment Models
  • Purchasing Models
  • vCore-based Purchasing Model
  • DTU-based Purchasing Model
  • Azure Data Lake Storage
  • Azure Data Lake Storage: Features
  • Limitless Storage
  • Large Analytic Workloads
  • High Availability and Reliability
  • Security
  • Big Data Processing
  • Cost Efficient
  • Azure Storage: Redundancy
  • Azure Storage: Life cycle Management
  • Azure Data Lake Storage Gen-2
  • Azure Data Lake Storage Gen-2: Features
  • Azure Data Lake Storage Gen-2: Best Practices
  • Azure Synapse Analytics
  • Azure Synapse Analytics: Benefits
  • Azure Synapse Analytics: Features
  • Working of Azure Synapse Analytics
  • Azure Synapse Workspace
  • Azure Databricks
  • Benefits of Azure Databricks
  • Azure Databricks Components
  • Uses of Azure Databricks
  • Delta Lake
  • Features of Delta Lake
  • Use Cases of Delta Lake
  • Azure Stream Analytics
  • Uses of Azure Stream Analytics
  • Working of Azure Stream Analytics
  • Benefits of Azure Stream Analytics



Working with Azure Synapse Analytics serverless SQL pools

  • SQL Pool
  • Types of SQL Pools
  • Dedicated SQL Pool
  • Benefits of a Dedicated SQL Pool
  • Serverless SQL Pool
  • Serverless SQL Pool: Features
  • Uses of Serverless SQL Pool
  • Best Practices of Serverless SQL Pool
  • Files Used in Azure
  • Querying a CSV File
  • Querying a Parquet File
  • Querying a JSON File
  • External Table
  • Shared Metadata of Azure Synapse Analytics
  • Characteristics of
  • Shared Metadata Model
  • Shared Metadata of Azure Synapse Analytics
  • Serverless SQL Pool Authentication
  • Serverless SQL Pool Authorization
  • Forms of Authorization
  • Managing User Permissions in Azure Synapse
  • User Permissions in Azure Synapse
  • Types of Access Control Lists (ACLs)
  • Types of Permissions in Azure Synapse



Data exploration and transformation in Azure Databricks

  • Databricks
  • Features of Databricks
  • Databricks in Data Science and Engineering
  • Reading Data in CSV Format
  • Reading Data in JSON Format
  • Reading Data in Parquet Format
  • Writing Data
  • DataFrame
  • DataFrame in Azure Databricks

Advanced DataFrames methods in Azure Databricks

  • Databricks File System
  • Advantage of Databricks File System
  • Databricks File System Permissions



Explore, transform, and load data into the Data Warehouse using Apache Spark

  • Azure Synapse Notebook
  • Benefits of Azure Synapse Notebook
  • What is Data Exploration?
  • Apache Spark
  • Features of Apache Spark
  • Usecase of Apache Spark
  • Working with Apache Spark Notebooks
  • Spark Pools
  • Spark Instances
  • Apache Spark Pool Auto-Scaling
  • Apache Spark Pool in Azure Synapse Analytics



Ingest and load data into the data warehouse

  • Azure Synapse Analytics
  • Loading data in Azure Synapse Analytics
  • What is PolyBase
  • Loading Data with PolyBase
  • Loading Data with Copy Statement
  • What is Azure Data Factory?
  • Working of Azure Data Factory
  • Connect and Collect
  • Transform and Enrich
  • CI/CD and Publish
  • Monitor
  • Components of Azure Data Factory
  • Benefits of Azure Data Factory
  • What Is Petabyte-Scale Ingestion?
  • Ingesting Data Using Copy Activity
  • Ingesting Data using the Compute Resources
  • Ingesting Data using the SSIS Package



Data Integration with Azure Data Factory or Azure Synapse Pipelines

  • What is Data Integration?
  • Data Integration Patterns
  • Data Integration Runtime
  • Types of Integration Runtime
  • Transformation Data Using Mapping Data Flow
  • Transformation Data Using Compute Resources
  • Transformation Data Using SSIS Package
  • Types of Azure Data Factory Transformation
  • What is Mapping Data Flows?
  • Transforming Data with Mapping Data Flows
  • What is Data Orchestration?
  • Benefits of Data Orchestration
  • What is Control Flow?
  • Common Control Flow Activities
  • Other Control Flows



End-to-end security with Azure Synapse Analytics

  • Azure Key Vault
  • Benefits of Azure Key Vault
  • Azure Key Vault Roles
  • Encryption in Azure Data Factory
  • Encrypting credentials
  • What are Customer-Managed Keys?
  • Firewall Rules
  • Virtual Networks
  • Benefits of a Virtual Network
  • Private Endpoints
  • What is Transparent Data Encryption?
  • Service-Managed Transparent Data Encryption
  • Dynamic Data Masking
  • Dynamic Data Masking Policy
  • Auditing
  • Auditing for SQL Server
  • Limitation of Auditing
  • Data Discovery and Classification
  • Purpose of Data Discovery and Classification
  • Data Discovery and Classification Capabilities
  • What is Authentiication?
  • Types of Security
  • Column-Level Security in Synapse Analytics
  • Row-Level Security in Synapse Analytics
  • Role-Based Access Control (Azure RBAC)?
  • Purpose of RABC
  • Security Principal
  • Role Definition
  • Scope
  • Role Assignment



Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

  • What is Transactional Processing
  • What is Analytical Processing
  • Designing Hybrid Transactional and Analytical Processing
  • Azure Synapse Link
  • what is Cosmos DB
  • Key-points of Cosmos DB
  • Features of Cosmos DB
  • Implement Configuration of Azure Synapse Link with Cosmos DB
  • Configuring the Synapse Link Steps
  • Querying Azure Cosmos DB with Apache Spark Pool
  • Querying Azure Cosmos DB with Serverless SQL Pool



Real-time Stream Processing with Stream Analytics

  • Event Hub
  • Partitions and Consumers
  • Throughput Units
  • Batch Processing
  • Batch Processing Steps
  • Real-Time Processing
  • Benefits of Real Time Processing
  • Publishing Events
  • Sending Events with Python
  • Receiving Events with Python
  • Enabling Reliable Messaging Using Azure Event Hub
  • Azure Functions: Event Consumption
  • Azure Stream Analytics
  • Creating Stream Analytics Job
  • What is Stream Processing?
  • Data Stream Ingestion
  • Advantages of Azure Stream Analytics



Create a Stream Processing Solution with Event Hubs and Azure Databricks

  • What is Structured Streaming
  • What is Unstructured Streaming
  • Features of structured streaming
  • Benefits of structured streaming
  • Benefits of unstructured streaming
  • Structured Streaming Workflow
  • Structured Streaming Model

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Satheesh Pabbathi

CEO of LevelUp - Teaching 5,000+ Students


Instructor ratings

1,250 Reviews

10,000 + Students

Sateesh Pabbathi is a solutions architect, consultant and software developer who has an interest in all things related to DevOps, Big Data, Cloud & API. He is recognized as Azure Certified Trainer and is an AWS Certified Solutions Architect Professional & AWS Certified , DevOps Professional. He loves to teach people how to use AWS, prepare for AWS certifications, and most importantly for the real world.

Sateesh Pabbathi is a solutions architect, consultant and software developer who has an interest in all things related to DevOps, Big Data, Cloud & API. He is recognized as Azure Certified Trainer and is an AWS Certified Solutions Architect Professional & AWS Certified , DevOps Professional. He loves to teach people how to use AWS, prepare for AWS certifications, and most importantly for the real world.

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Frequently Asked Questions.

You’ll learn about several of the database and big data services that are available on Microsoft Azure. You'll also learn how to take advantage of several virtualization services in Microsoft Azure compute, which can help your applications scale out quickly and efficiently to meet increasing demands. Finally, you’ll learn about the different storage and virtual network options available in Microsoft Azure.

Although anybody with basic knowledge in Azure can take up this course, it is recommended for:

  •  IT professionals who are having experience working with Azure.
  • Data Engineers with minimum 1-year experience.
  •  IT professionals who want to upskill and upscale their career as an Azure Data Engineer.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit

Using Azure Stream Analytics, you can manage real-time event processing applications by scaling it. It promotes greater performance through partitioning, so complicated queries can be parallelized and run on many stream nodes.

Azure data engineer enables companies to convert all their big data from storage systems, relational & non-relational databases into data-driven workflows. This helps them put up concrete strategies, accomplish goals, and improve the market value of the data they own! For this, they require certified professionals.

In the United States, the average salary for a newbie in this position is about US$85,000, and in India, it is around ₹700,000 (rough estimate). In addition, experienced candidates with recognized DP-203 certification can earn up to US$120,000 in the United States and up to ₹1,500,000 in India!

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