Unlock Your Data Potential: Comprehensive Azure Data Engineering Training for Best Results. Expert Tips Revealed.

Azure Data Engineering

Azure data engineering encompasses a suite of tools and services designed to streamline the entire data lifecycle on Microsoft’s cloud platform. From ingestion to processing and visualization, Azure offers scalable solutions like Azure Data Factory for data integration, Azure Databricks for big data analytics, and Azure Synapse Analytics for data warehousing and real-time analytics. These services leverage cloud computing power to handle large volumes of data efficiently, ensuring high availability and security through Azure’s robust infrastructure. Whether managing structured or unstructured data, Azure’s comprehensive ecosystem supports seamless data pipelines, enabling businesses to derive insights swiftly and make data-driven decisions effectively in today’s fast-paced digital landscape.

Overview of Azure Data Engineering

The objective of Azure data engineering training typically revolves around equipping individuals with the skills and knowledge needed to design and implement data solutions using Microsoft Azure services. Key objectives may include:

  • Understanding Azure Data Services: Learning about Azure’s suite of data services including Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and others.

  • Data Integration and ETL Processes: Mastering techniques for data integration, transformation, and loading (ETL) using Azure Data Factory and related tools.

  • Data Storage and Management: Understanding how to store and manage data effectively using Azure Data Lake Storage, Azure SQL Database, and other Azure storage solutions.

  • Big Data and Analytics: Gaining expertise in handling big data scenarios and performing analytics using Azure services like Azure Synapse Analytics and HDInsight.

  • Data Pipelines and Automation: Building efficient data pipelines and automating data workflows to ensure reliability and scalability.

  • Data Security and Compliance: Ensuring data security, privacy, and compliance with regulations through Azure’s built-in security features and governance tools.

Overall, Azure data engineering training aims to prepare professionals to leverage Azure’s cloud platform for building robust, scalable, and efficient data solutions that meet modern business demands.

In Azure data engineering training, you can expect to learn a variety of skills and concepts essential for designing, building, and managing data solutions on Microsoft Azure. Here are some of the key topics typically covered:

  • Azure Data Services Overview: Understanding the range of Azure data services available, such as Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure Cosmos DB, and more.

  • Data Storage and Management: Learning how to store and manage data effectively using Azure Storage options like Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database.

  • Data Integration and ETL: Mastering techniques for data integration, transformation, and loading (ETL) using Azure Data Factory, Azure Databricks, and other tools.

  • Big Data Processing: Exploring methods for processing and analyzing large-scale data using Azure Synapse Analytics, HDInsight, and other big data technologies.

  • Data Pipelines and Workflow Automation: Building and orchestrating data pipelines to automate data movement, transformation, and processing tasks.

  • Data Security and Compliance: Ensuring data security, privacy, and compliance with regulations using Azure’s built-in security features and governance tools.

  • Real-time Analytics and Machine Learning: Implementing real-time data analytics and integrating machine learning models into Azure data solutions.

  • Monitoring and Optimization: Monitoring the performance of data solutions and optimizing them for efficiency and scalability.

  • Collaboration and Integration: Working with other Azure services and integrating data solutions with existing systems and applications.

Taking the Azure data engineering course offers several compelling reasons:

  • Career Advancement: Acquiring Azure data engineering skills can enhance your career prospects, as businesses increasingly rely on cloud-based data solutions. This training demonstrates your proficiency in designing and managing modern data architectures.

  • Industry Relevance: Azure is one of the leading cloud platforms, widely adopted by organizations worldwide. Learning Azure data engineering ensures you are proficient in tools and services that are in high demand across industries.

  • Hands-on Experience: The course typically includes practical exercises and projects that provide hands-on experience with Azure data services. This practical knowledge is valuable for applying concepts in real-world scenarios.

  • Scalability and Flexibility: Azure’s scalability allows you to handle diverse data workloads efficiently. Learning Azure data engineering equips you to build scalable data solutions that can grow with business needs.

  • Integration with Microsoft Ecosystem: If your organization uses Microsoft technologies like Office 365, Dynamics 365, or Windows Server, integrating Azure data solutions can provide seamless data management and analytics capabilities.

  • Cost Efficiency: Azure offers various pricing models, including pay-as-you-go and reserved instances, allowing organizations to optimize costs based on their usage patterns. Understanding these models through training can lead to cost-effective data solutions.

  • Community and Support: Microsoft Azure has a large community of users and extensive documentation and support resources. Being trained in Azure data engineering gives you access to this community and ongoing support for troubleshooting and best practices.

Azure data engineering training is typically suitable for individuals who are interested in learning how to design and implement data solutions using Microsoft Azure services. This includes:

  • Data Engineers: Professionals responsible for designing and managing data processing systems, data pipelines, and data storage solutions.

  • Database Administrators: Those involved in managing and maintaining databases and data warehouses.

  • Data Analysts: Individuals who analyze data to derive insights and make data-driven decisions.

  • Data Scientists: Professionals who develop models and algorithms to extract knowledge and insights from data.

  • Software Developers: Those interested in integrating data solutions into applications using Azure services.

  • IT Professionals: Anyone looking to expand their skills in cloud-based data engineering solutions.

  • Business Intelligence Professionals: Individuals working with business intelligence tools and interested in leveraging cloud platforms for data solutions.

  • Anyone with a keen interest in Azure and data engineering: Individuals looking to transition into a career involving cloud-based data engineering or expand their knowledge in this domain.

The training typically covers various Azure services related to data engineering, such as Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Cosmos DB, and more. It’s designed to equip learners with the skills needed to work effectively with big data and analytics in the Azure cloud environment.

To effectively learn Azure data engineering, it’s beneficial to have a solid understanding of the following prerequisites:

  • Fundamental Knowledge of Data Concepts: A basic understanding of data concepts such as databases, data warehouses, data modeling, and data manipulation is essential.

  • Programming Skills: Proficiency in at least one programming language is helpful. Python is particularly useful in Azure data engineering due to its popularity in data manipulation and analysis.

  • Understanding of SQL: Familiarity with SQL (Structured Query Language) is important, as many Azure data services use SQL for querying and data manipulation.

  • Understanding of Cloud Computing: A foundational understanding of cloud computing concepts and how cloud services work is beneficial. Familiarity with Azure basics is a plus but not always required at the outset.

  • Knowledge of Data Integration and ETL Processes: Basic understanding of data integration concepts and ETL (Extract, Transform, Load) processes will help in grasping concepts related to Azure Data Factory and similar services.

  • Basic Understanding of Big Data Technologies: Awareness of big data technologies like Hadoop, Spark, and data lakes can be advantageous, especially when working with Azure Databricks and Azure Data Lake Storage.

Azure data engineering offers a range of job opportunities across various industries, especially as organizations increasingly adopt cloud-based data solutions. Some common job roles and opportunities in Azure data engineering include:

  • Azure Data Engineer: Responsible for designing, implementing, and managing data solutions using Azure services such as Azure Data Factory, Azure SQL Database, Azure Databricks, Azure Cosmos DB, and others. They build data pipelines, manage data storage, and ensure data quality and reliability.

  • Cloud Data Architect: Focuses on designing and implementing cloud-based data solutions, including architecture design, data integration strategies, and ensuring scalability and performance using Azure services.

  • Data Warehouse Engineer: Specializes in designing and managing data warehouses in Azure, using services like Azure Synapse Analytics (formerly Azure SQL Data Warehouse) to handle large-scale data processing and analytics.

  • Big Data Engineer: Works with big data technologies in Azure, leveraging services like Azure HDInsight (for Hadoop, Spark, etc.) and Azure Databricks for data processing, machine learning, and analytics at scale.

  • Data Integration Specialist: Focuses on integrating data from various sources into Azure environments, utilizing Azure Data Factory and other integration tools to ensure seamless data flow and transformation.

  • Business Intelligence Developer: Develops reports, dashboards, and visualizations using Azure services like Power BI, integrating with Azure data sources to provide insights and analytics to business stakeholders.

  • Machine Learning Engineer: Utilizes Azure Machine Learning services to build, deploy, and manage machine learning models and pipelines, integrating with Azure data sources and services for data-driven decision-making.

  • Data Scientist: Applies machine learning and statistical techniques to analyze data in Azure environments, collaborating closely with Azure data engineers to access and prepare data for analysis.

  • Data Analyst: Uses Azure data services to extract, transform, and analyze data for insights, reporting, and decision support, often working with Azure SQL Database, Azure Cosmos DB, and other Azure data tools.

  • Cloud Solution Architect (with Data Focus): Designs end-to-end solutions on Azure, focusing on data aspects such as architecture, integration, security, and scalability, to meet business requirements.

These roles span across industries such as finance, healthcare, retail, technology, and more, reflecting the growing demand for professionals who can leverage Azure’s capabilities to manage and derive value from data effectively. As cloud adoption continues to rise, the demand for Azure data engineering skills is expected to grow, offering diverse career paths and opportunities for specialization in various aspects of data management and analytics.

Key Features

Course Circullum

  1. Introduction to Relation Databases
  • Introduction to database concepts like Data, Database, Entity, attribute, and ER Diagrams.
  • How to connect SQL Server using SSMS (SQL Server Management Studio)
  • Installation of SQL Server and SSMS
  1. DDL (Data Definition Language)
  1. DQL (Data Query Language)
  1. DML (Data Manipulation Language)
  • INSERT – is used to insert data into a table.
  • UPDATE – is used to update existing data within a table.
  • DELETE – is used to delete records from a database table.
  1. Constraints
  1. Data Types
  • Different Types of data types available in SQL Server
  1. Various Clauses
  • Where, Group By, Order By, TOP N, DISTINCT, ROW_NUMBER, RANK (), CASE
  1. Functions
  • Aggregate Functions (SUM, MIN, Max, AVG, COUNT), String Functions
  1. Operators
  1. Joins
  • Inner Joins, Left Join (Left Outer Join), Right Join (Right Outer Join)
  1. Set Operations
  • Union, Union ALL, Intersect
  1. Views, Functions, and Stored Procedures
  2. Usage of Temp table, Table variable, CTE.


Azure BI (Azure Blob Storage, Azure Data lake, AZURE SQL DB, AZURE data factory & Azure Synapse analytics).
  1. Introduction to Azure.
  • Introduction to Azure portal, Subscriptions, and services available in azure.
  • How to create free subscriptions and various services in azure.
  1. Introduction to Azure Blob Storage.
  • Introduction
  • How to create blob storage.
  • Blob storage resources
    • The storage accounts
    • A container in the storage account
    • A blob in a container
  • Transfer data to and from Azure Storage
  1. Introduction to Azure Data Lake.
  • Introduction
  • How to create Azure Data Lake.
  • Data lake features
  • Transfer data to and from Azure Storage
  1. Azure SQL Database.
  • What is Azure SQL DB
  • Different deployments of SQL in Azure.
  • Azure SQL Managed Instance
  • How to create Azure SQL Server
  • Create database, schema, table, view, and stored procedures.
  • Elastic Pool, Different kinds of pricing tiers
  • How to query data from Azure SQL server using on-premises SSMS and cloud.
  • Data exchange between azure SQL database to azure storage like a blob and Data Lake & Vice -versa.
  • What is poly base in Azure SQL?
  • What is an external table?
  • Data factory use case for data exchange from azure SQL database to various destinations.
  1. Azure Data Factory.
  • Overview of ETL/ELT processes.
  • Cloud & On-premises ETL tool comparison.
  • Azure data factory components like Pipeline, Dataset, Linked service, Activities, and data flows.
  • Integration run time- Different Types of integration run times (Auto, Self-hosted & SSIS).
  • Develop pipelines using various activities like copy activity, stored procedure activity, Lookup Activity, Get Metadata activity, Delete activity, Wait for activity, Execute package activity, etc.
  • Iteration & Conditions – IF, For Each, until…
  • Working with different sources and sinks.
  • Data flow overview and transformation in the data flow.
  • Integration Runtime, different Types of Integration Run times
  • Logic apps- how to call logic app (RESTAPI) from ADF Web activity.
  • Pipeline debug and monitoring
  • Parameters & Variables usage in pipelines.
  • Error handling
  • Schedule and trigger the pipelines
  • What are ARM Templates using for deployment?
  • How to deploy data factory code from dev to q to prod environment.
  1. Azure Synapse analytics.
  • Introduction to Synapse analytics.
  • Synapse analytics architecture.
  • Synapse SQL: Complete T-SQL based analytics
    1. Dedicated SQL pool (pay per DWU provisioned)
    2. Serverless SQL pool (pay per TB processed)
    3. Spark: Deeply integrated Apache Spark.
    4. Synapse Pipelines: Hybrid data integration
    5. Studio: Unified user experience
  • Features of Azure Synapse Analytics
  • Table distribution – Replicate, Round-Robin & HASH
  • External tables, Polybase
  • Load data into synapse SQL using various options like poly base, Bulk copy
  • Power BI data refresh from Azure synapse data warehouse.
  • ADF pipeline use case for data extraction.
  1. Azure Data bricks.
  • Introduction to Azure Data bricks
  • How to create Data bricks-free account.
  • Workspace, Notebooks
  • Basics of Spark
  • How to mount Data lake to Data bricks using Scala
  • Reading data from Blob Storage and writing into Azure SQL/synapse
  • Reading data from Data Lake Storage and writing into Azure SQL/synapse
  • Explore, analyze, Clean, Transform and load data in data bricks using Python and SQL
  • Use cases.
  1. Real-Time Project explanation and Practice.
  • Introduction to different data warehouses, on-premises and cloud data warehouses, dimension modeling (star & Schema), Dimension, and fact tables.
  • How to gather project requirements, Scrum task details.
  • Tools used in projects like SQL Server on-premises, ETL(ADF), and destination (Synapse)
  • How to handle delta and full loads in the ETL process.
  • Using Power BI how to prepare using Synapse data warehouse.

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Azure Data Engineering

30 Days
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Azure Data Engineering

Life Time Access
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Expert Led Training
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