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Azure Data Engineering Training in Ahmedabad, IN

312 Learners 30 Hours (5.0)

Learn to create master Azure Data Engineering practically and with Mastery. Acquire such important modules as Azure Data Factory, Azure Synapse, Databricks, Data Lake, and Power BI. Learn the higher-level skills needed to develop, combine, and operate data pipelines to have an efficient business intelligence solution.
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Azure Data Engineer Training

Key Highlights

Live interactive Sessions

24/7 Support

Job Assistance

Mentor Support

Project Based Learning

Recognised Certification

Flexible Batches

7th March 2026

Saturday

6:00 AM to 10 PM

8th March 2026

Sunday

6:00 AM to 10 PM

9th March 2026

Monday

6:00 AM to 10 PM

10th March 2026

Tuesday

6:00 AM to 10 PM

7th Mar

Sat

6:00 AM to 10 PM

8th Mar

Sun

6:00 AM to 10 PM

9th Mar

Mon

6:00 AM to 10 PM

10th Mar

Tue

6:00 AM to 10 PM

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Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.

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Azure Data Engineer Course Details

GoLogica presents the comprehensive Azure Data Engineering Training, meticulously designed to transform you into a skilled cloud data professional. This course is your gateway to seizing these incredible career opportunities right here in Ahmedabad and globally. We understand that practical, job-ready skills are essential, which is why our program moves far beyond simple theory.

 

Our complete training curriculum is structured by industry experts to cover all the essential aspects of modern data engineering. You will gain deep expertise in key Azure services, starting with data storage solutions like Azure Data Lake Storage and Azure Blob Storage. You’ll master the art of building scalable and reliable data pipelines using Azure Data Factory (ADF), a crucial tool for any Azure Data Engineer. Furthermore, you will delve into advanced analytics using Azure Synapse Analytics for data warehousing and Azure Databricks for processing massive datasets using Apache Spark. We make sure you are fully prepared for the certification, which is highly valued in the industry.

 

What essentially differentiates this Azure Data Engineering Training is our unwavering dedication to its applicability outside the classroom. Contrarily, to only concept understanding, you will be engrossed in hands-on, topically industry projects that intend to emulate real business scenarios. This experiential learning strategy guarantees that you can safely take up the position and make your first-day contributions right away. The technical support team, which is very committed to serving you, is always available to solve your doubts and, therefore, will be your partner throughout a very simple and efficient learning journey. How-to-experts or novices from Ahmedabad, turning your professional path has never been that simple and that much supported.

 

Essentially, signing up for this course is a move towards a future where you call the shots. You will acquire the mastery of the most important skills like data integration, transformation, security, and optimization on the Microsoft Azure platform. The job assistance that we offer is like a weapon that will give you an advantage in the market and make you stand out from the crowd. 

 

Stop waiting for the right opportunity, create it with GoLogica expert training. Become the in-demand Azure Data Engineer the industry is looking for and unlock a phenomenal salary potential. Join us today to transform your career trajectory in Ahmedabad.

Salary Trends:

According to ZipRecruiter, The average salary of an Azure Data Engineer professional typically ranges from $119k to $124k PA. It depends on factors such as experience, location, and specific job responsibilities.

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Azure Data Engineer Curriculum

Azure Databricks Introduction
  • Databricks Architecture

  • Databricks Components overview

  • Benefits for data engineers and data scientists

Azure Databricks concepts
  • Workspace – Creation and managing workspace.

  • Notebook – creating notebooks, calling and managing different notebooks.

  • Library - installing libraries, managing libraries

Data Management
  • Databricks File System. - DBFS commands copy and manage files using DBFS.

  • Database - Creating database, tables and managing databases and tables.

  • Table - Creating Tables, dropping tables, loading data.

  • Metastore - managing metadata and delta tables creation, managing delta tables.

Computation Management
  • Cluster -- Creating Clusters , managing clusters

  • Pool - creating pools and using pools for Auto scaling.

  • Databricks Runtime - understanding and using Databricks runtimes based on requirement.

  • Jobs - creating jobs from notebooks and assigning types of clusters for jobs.

  • Workload - monitoring jobs and managing loads.

  • Execution Context – understanding context.

Databricks Advanced topics.
  • Databricks Workflows

  • Calling one notebook into another notebook.

  • Creating global variables (widgets) and using into Azure ADF pipeline.

  • How to implement parallelism in notebooks execution.

  • Mounting azure blob storage and data lake storage accounts.

  • Integrating source code (notebooks) with GitHub

  • Calling DataBricks notebooks into Azure Data factory.

  • Databricks Clusters logs monitoring flow.

Introduction to Spark
  • What is Spark and what is its purpose?

  • Components of the Spark unified stack

  • Resilient Distributed Dataset (RDD)

  • Downloading and installing Spark standalone

  • Scala and Python overview

  • Launching and using Spark’s Scala and Python shell

Resilient Distributed Dataset and Data Frames
  • Understand how to create parallelized collections and external datasets

  • Work with Resilient Distributed Dataset (RDD) operations

  • Utilize shared variables and key-value pairs

Spark application programming
  • Understand the purpose and usage of the Spark Context

  • Initialize Spark with the various programming languages

  • Describe and run some Spark examples

  • Pass functions to Spark

  • Create and run a Spark standalone application

  • Submit applications to the cluster

  • Introduction to Spark libraries

  • Understand and use the various Spark libraries

  • Spark configuration, monitoring and tuning

  • Understand components of the Spark cluster

  • Configure Spark to modify the Spark properties, environmental variables, or logging properties

  • Understand components of the Spark cluster Monitor Spark using the web UIs, metrics, and external instrumentation ,Understand performance tuning considerations

Introduction To Pyspark
  • What is Spark Session?

  • How to create spark session

  • What is Spark Context?

  • How to create Spark Context

  • What is SQL CONTEXT?

How to Use Jupiter Notebooks & Databricks notebooks for Python Development.
Install and configure PySpark in Local System for development.
Introduction to Big Data and Apache Spark
Apache Spark Framework & Execution Process.
Introduction To RDDs
  • Different Ways to Create RDD’s in Pyspark.

  • RDD Transformations

  • RDD Actions

  • RDD Cache & Persist

Introduction to Data Frame.
  • Different Ways to Create Data Frame’sin Pyspark.

  • Dataframe Transformations

  • Dataframe Actions

  • Dataframe Cache & Persist

Different types of Big Data File systems.
  • Difference between Row store format and column store format.

  • Avro File

  • Parquet file

  • ORC File

Reading and Writing Different Types of Files using Dataframe.
  • CSV files

  • Json files

  • Xml files

  • Excel files

  • Complex Json files

  • Avro files

  • Parquet files

  • Orc files

Need for Spark SQL
  • What is Spark SQL

  • SQL Table Creation

  • SQL Join Types

  • SQL Nested Queries

  • SQL DML Operations

  • SQL Merge Scripts

  • SQL SCD Type 2 implementation

User-Defined Functions
Create Private Link to different services
Limitation
What is Azure Data Factory
How to create Data Factory + All Components
Creating Pipeline + Copy Activity
General Activities
Iteration and Conditions
Data Flow Resource + Data Flow Activity
Synapse Activities
Azure Monitor and Manage

Delta Lake usage in Databricks.
  • Delta Lake Architecture

  • Delta Lake Storage Understanding

  • Delta lake table creation and API options

  • Delta Lake DML Operations usage.

  • Delta Lake partitions

  • Delta Lake Schema Enforcement

  • Delta Lake Schema Evolution

  • Delta Lake Versions

  • Delta Lake Time Travel

  • Delta Lake Vaccum

  • Delta Lake Merge (SCD Type 1 and SCD Type2)

Overview of the Microsoft Azure Platform
  • Introduction to Azure

  • Basics of Cloud computing

  • Azure Infrastructure

  • Walkthrough of Azure Portal

  • Overview of Azure Services

Azure Data Architecture
  • Traditional RDBMS workloads.

  • Data Warehousing Approach

  • Big data architectures.

  • Transferring data to and from Azure

Azure Storage options
  • Blob Storage

  • ADLS Gen1 & Gen2

  • RDBMS

  • Hadoop

  • NoSQL

  • Disk

Blob Storage
  • Azure Blob Resources

  • Types of Blobs in Azure

  • Azure storage account data objects

  • Azure storage account types and Options

  • Replications in distribution

  • Secure access to an application's data

  • Azure Import/Export service

  • Storage Explorer

  • Practical section on Blob Storage

Azure Data Factory
  • Azure Data Factory Architecture

  • Creating ADF Resource and Use in azure cloud

  • Pipeline Creation and Usage Options

  • Copy Data Tool in ADF Portal, Use

  • Linked Service Creation in ADF

  • Dataset Creation, Connection Reuse

  • Staging Dataset with Azure Storage

  • ADF Pipeline Deployments

  • Pipeline Orchestration using Triggers

  • ADF Transformations and other tools integration.

  • Processing different type’s files using ADF.

  • Integration Runtime

  • Monitoring ADF Jobs

  • Manage IR’s and Linked Services.

Azure SQL Database Service
  • Introduction to Azure SQL Database

  • Relational Data Services in the Cloud

  • Azure SQL Database Service Tiers

  • Database Throughput Units (DTU)

  • Scalable performance and pools

  • Creating and Managing SQL Databases

  • Azure SQL Database Tools

  • Migrating data to Azure SQL Database

Azure Data Lake Gen1 & Gen2
  • Explore the Azure Data Lake enterprise-class security features.

  • Understand storage account keys.

  • Understand shared access signatures.

  • Understand transport-level encryption with HTTPS.

  • Understand Advanced Threat Protection.

  • Control network access.

  • Differences between Gen1 & Gen2

Azure Synapse SQL DW (Dedicated SQLPOOL)
  • Azure Synapse DW (Dedicated SQL POOL)?

  • Synapse DW Architecture.

  • Creating Internal table with default distribution

  • Creating external table in synapse dw

  • Loading data from databricks to azure synapse dw

  • Loading data from adls gen2 to azure synapse dw

  • What is dedicated sql pool

  • data warehouse unit overview

  • Distributed table with example

  • Hash distribution with example

  • Round robin distribution with example

  • Replicate distribution with example

  • What are the types of indexes with examples

  • Clustered Index with example

  • Non-Clustered index with example

  • Clustered Column Store Index with example

  • Heap Index with example

Introduction to Spark SQL.
  • Spark SQL Create database

  • Drop databases

  • Create internal table

  • Create external table

  • Create partitioned table

  • Create partitioned with bucketing table

  • SPARK DML insert, update, delete and merge operations

  • SPARK SQL DRL Select queries with different clauses

  • Spark SQL MERGE With SCD Type 1 and SCD Type 2

  • Spark SQL WHERE Clause, Group By Clause and Having Clauses

  • Spark SQL Order by, Sort By clauses

  • Spark SQL join types, Window , Pivot , Limit and Like

  • Spark SQL Grouping Sets, Rollup and Cube

  • Spark SQL Cultured By and Distributed By

  • Spark SQL Case, With and Take sample

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Learning Options

Azure Data Engineer Self-Paced Learning

Self-Paced Learning

  • 24/7 access to premium quality self-paced high-end learning videos providing enhanced training.
  • Explore the digital learning experience with LMS access.
  • Get access to study materials develop by professionals with years of expertise.

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Led by Industry Experts for Azure Data Engineer

Led by Industry Experts

  • Experienced practitioners providing case studies and best practices to sessions.
  • Regular/Weekend batches meeting the requirements of the students.
  • 24/7 online support and guidance by top industry experts and mentors to solve conceptual doubts.

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Azure Data Engineer Corporate Solutions

Corporate Solutions

  • Access world-class learning experiences developed on industry-designed projects, mentoring, etc.
  • 24/7 online support and guidance by top industry experts and mentors.
  • Top-notch online training by industry experts and self-paced learning with effective guidance.

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Azure Data Engineer Certification

The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.

At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in Azure Data Engineer technology.

You will get certified in Azure Data Engineer by clearing the online examination with a minimum score of 70%.

Azure Data Engineer course certificate

Get Certification

Azure Data Engineer Objectives

Azure Data Factory is a cloud-based total information integration carrier that enables the creation, scheduling, and orchestration of records pipelines to transport and rework records throughout diverse records shops. It supports ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes for large-scale records workflows in each cloud and on-premises environments. Azure Data Factory is widely used for automating information movement and getting ready information for analytics.

To optimize information pipelines, you could use strategies along with partitioning data, optimizing information glide sports, leveraging PolyBase for big data hundreds, and using parallel reproduction to enhance overall performance.

Azure Data Lake Storage Gen2 is an upgraded version of Gen1, presenting hierarchical namespace aid, better security features, and integration with Azure Blob Garage, making it greater scalable and efficient for large data analytics.

Data may be secured using function-based get entry to manage (RBAC), POSIX-compliant gets entry to permissions, statistics encryption at relaxation and in transit, and setting up virtual network provider endpoints.

Azure Data Factory supports a whole lot of transformation sports, together with information glide alterations, mapping facts flows, SQL-based variations, and external activities like Azure Databricks.s

Real-time statistics processing can be carried out with the usage of Azure Stream Analytics or Azure Databricks alongside Event Hubs or IoT Hub for streaming information ingestion and processing.

Best practices include structuring records in folders for logical corporations, the use of partitioning strategies, handling admission to manage with Azure AD, and optimizing garage charges with the aid of deciding on suitable file formats.

Data partitioning in Azure Synapse Analytics entails dividing large datasets into smaller, greater attainable components, which improves question overall performance and enables parallel processing.

Monitoring can be completed with the use of Azure Monitor, the ADF tracking dashboard, hobby runs, pipeline runs, and integration with Log Analytics for designated logging and diagnostics.

Azure Databricks is a fast, clean, and collaborative Apache Spark-based analytics carrier. It is used for huge statistics analytics, information engineering, gadget gaining knowledge of, and strolling information pipelines.

Azure Data Lake integrates with offerings like Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Power BI, and others, providing a continuing ecosystem for data garage, processing, and analytics.

Delta Lake is an open-supply garage layer that brings ACID transactions to huge information workloads. It enhances information reliability and helps schema enforcement and time tour skills.

Azure Data Catalog is a completely controlled cloud service that lets fact specialists discover, classify, and understand their information sources, making it less difficult to discover and use records for analytics and reporting.

Common ETL patterns encompass batch processing, real-time processing, incremental facts loading, and information differences in the usage of offerings like Azure Data Factory, Azure Synapse, and Azure Databricks.

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Azure Data Engineer rated (5.0 / 5) based on 1 reviews.
Ganesh

I completed my Azure Data Engineer training with GoLogica. This course provides professionals with crucial experience in data storage, conversion, and protection on Microsoft Azure, as well as practical training for application development.