Data Engineering Course

About the Data Engineering Course

Data engineering plays an important role in helping organisations manage large amounts of information and make it ready for analysis. This course introduces you to the technologies and techniques used to move data between systems, maintain data quality, and build reliable data pipelines.

Through structured lessons and practical exercises, you will learn programming fundamentals, database concepts, ETL processes, cloud storage, and data processing tools. The Data Engineering online course is designed for learners who want to understand how modern data systems work and develop skills that can be applied to real-world scenarios.

Why Choose Our Data Engineering Training?

• Work through exercises that help you understand data engineering concepts.
• Learn Python and SQL for data manipulation and querying.
• Understand how data is extracted, transformed, and loaded between systems.
• Explore cloud-based storage, integration, and processing services.
• Gain exposure to Apache Spark, Databricks, and Microsoft Fabric.
• Apply your knowledge to practical data engineering tasks.
• Strengthen your technical understanding and prepare for data engineering interviews.

Syllabus of Data Engineering Course

Programming Foundations & Data Modeling

Module 1: Python for Data Engineering

• Python fundamentals, setup & environment, variables, data types, functions, modular code, loops, conditions and logic building.
• Data Structures — Lists, tuples, sets, dictionaries and iteration techniques.
• Pandas & NumPy — Series & DataFrames, data reading/writing, cleaning, filtering, sorting, aggregation and numerical operations.
• Scripting & Automation — Running scripts, best practices and building reusable components.

Module 2: Data Handling with Python

• Working with CSV, Excel and JSON, handling missing data and type conversions.
• Data Cleaning & Transformation — Filtering, grouping, merging, joining and reshaping data.
• Data Analysis Essentials — EDA, summary statistics, Matplotlib and Seaborn visualization.

Module 3: SQL Fundamentals

• SELECT, WHERE, JOINs, filtering and sorting.
• Query Writing & Optimization — Subqueries, indexing and execution plans.
• Database Design Fundamentals — Normalization, keys and relationships.
• Hands-on Practice — Real-world query-writing drills and exercises.

Cloud & Security

Module 4: Cloud & Microsoft Azure Foundations

• Cloud computing basics, public/private/hybrid cloud models and real-world use cases.
• Azure Fundamentals — Navigating Azure Portal and understanding key Azure services.
• Cloud Service Models — IaaS, PaaS and SaaS with real-world examples.
• Azure Environment Setup — Creating an account, first resource group and hands-on resource launch.

Module 5: Secure Identity & Access Management in Azure

• Identity & access management, authentication vs authorization.
• Microsoft Entra ID — Tenants, users, groups, roles and Role-Based Access Control (RBAC).
• SPN & Managed Identity — Service principals and when to use each identity approach.
• Security Best Practices — Secure access to storage, databases and Azure services.

Storage & Integration

Module 6: Azure Storage & Data Lake Fundamentals

• Storage account basics and architecture for modern data platforms.
• Blob Storage Essentials — Containers, folder structures, block blobs, append blobs and page blobs.
• Advanced Storage Concepts — Metadata storage, audit logs and configuration management.
• Queue Storage & Workflows — Table storage and queue storage for data engineering workflows.

Module 7: Azure Data Lake Storage Gen2

• ADLS Gen2 architecture and hierarchical namespace.
• Working with Data Lake — File systems, directories, files and configuration.
• Data Ingestion & Management — Ingesting and accessing data in ADLS Gen2.
• Data Lake Best Practices — Design concepts, performance optimization and cost optimization.

Module 8: REST APIs & Data Integration Fundamentals

• REST APIs in data engineering and REST vs SOAP.
• API Integration Concepts — Real-world data integration scenarios.
• Working with APIs — Testing tools and CRUD operations.
• API Data Handling — Data extraction and error management.

Module 9: Azure Data Factory

• ADF architecture and pipeline execution flow.
• Pipelines, Datasets & Integration Runtime — Linked services and dataset types.
• Data Integration & Orchestration — Connecting Storage, SQL and REST services, scheduling and triggers.
• Monitoring & Best Practices — Debugging, optimization and production-ready pipelines.

Processing (Azure Data Engineering)

Module 10: PySpark Foundations

• Introduction to Databricks & Spark, SparkSession, execution flow, transformations vs actions and lazy evaluation.
• Core Concepts & Data Structures — RDDs, DataFrames and Spark SQL.
• Data Ingestion in PySpark — CSV, JSON and Parquet with schema inference.
• Data Output in PySpark — DataFrame APIs, save modes and partitioning.

Module 11: Data Processing with PySpark

• Joins, aggregations, data cleaning and type conversions.
• Advanced Analytics with Spark SQL — Window functions and complex queries.
• Performance Optimization — Partitioning, shuffle, caching and persistence.
• Monitoring & Tuning — Spark UI, stages, tasks, executors and tuning best practices.

Module 12: Databricks Platform, Delta Lake & Governance

• Databricks architecture, workspace, notebooks and clusters.
• Data Connectivity & Storage — Connecting to ADLS Gen2, mounting and secure access.
• Delta Lake Essentials — Delta architecture, Delta tables and ACID transactions.
• Governance & Unity Catalog — Permissions, access control and data governance.

Module 13: Databricks Lakehouse Architecture & Pipelines

• Lakehouse and Medallion architecture with Bronze, Silver and Gold layers.
• Building Data Pipelines — Batch and real-time streaming pipelines.
• Advanced Pipeline Concepts — Incremental processing and Change Data Capture (CDC).
• Use Cases & Best Practices — Cost optimization and scalable pipeline design.

Microsoft Fabric

Module 14: Introduction to Microsoft Fabric

• What Microsoft Fabric is and how it differs from Azure Data Engineering.
• Microsoft Fabric Components — Unified platform for Data Engineering, Data Science and analytics.
• Real-time Analytics in Fabric — Benefits for modern data engineering.
• Microsoft Fabric Eventhouse — Real-time data ingestion and processing.

Module 15: Data Integration & Orchestration with Fabric

• Dataflows Gen2 and data integration.
• Pipelines for Data Engineering — Pipeline concepts and templates.
• Run & Monitor Pipelines — Pipeline execution and performance tracking.
• Real-time Data Processing — Ingest, transform, store, query and visualize data.

Module 16: Lakehouse, Delta Tables & Warehousing in Fabric

• Lakehouse and Medallion architecture using Bronze, Silver and Gold layers.
• Delta Tables in Fabric — Creating and handling Delta tables using Spark and structured streaming.
• Data Warehousing in Fabric — Fact tables, dimensions and loading data using T-SQL.
• Dataflows & Data Management — Loading, transforming, managing and protecting data.

Module 17: CI/CD, Deployment & Governance in Fabric

• Data loading and pipeline strategies for Fabric Warehouse.
• CI/CD in Microsoft Fabric — Git version control and deployment pipelines.
• Automation with APIs & KQL — Automating CI/CD tasks and using KQL.
• Advanced SQL & Analytics — Materialized views and stored functions.

Snowflake

Module 18: Snowflake Fundamentals & Architecture

• Snowflake basics, use cases and why Snowflake is used for modern data platforms.
• Setup & Environment — Creating a Snowflake account and navigating the Web UI.
• Architecture & Storage Layers — Cloud services, compute and storage layers with micro-partitioning.
• Data Types & Tables — VARIANT data type and permanent, temporary and transient tables.

Module 19: Data Loading & Transformation in Snowflake

• Time Travel, Fail-safe and Zero Copy Cloning.
• Stages & COPY Command — File formats and COPY command usage.
• Streams, Tasks & Automation — SQL-based ETL and scheduling tasks.
• Real-time ETL & Integrations — Data Lake integration and BI tool connections.

Module 20: Performance Optimization & Warehousing

• Virtual warehouse sizing, auto-suspend and auto-resume.
• Performance Tuning — Clustering keys and query profiling.
• Caching & Optimization — Compute resource management.
• Schema Design & Best Practices — Star vs snowflake schema and data modeling.

Module 21: Security, Governance & Data Sharing

• Authentication, RBAC and sensitive data access.
• Security & Monitoring — Encryption at rest/in transit and auditing.
• Data Sharing & Masking — Secure data sharing features.
• Governance Best Practices — Compliance and operational best practices.

Orchestration & Streaming

Module 22: Apache Airflow

• Airflow fundamentals and components overview.
• Installation & Setup — Installing Airflow and working with the Web UI.
• DAGs, Operators & Tasks — Building workflows and managing dependencies.
• Scheduling & Jobs — Creating, triggering and monitoring jobs.

Module 23: Apache Kafka

• Kafka fundamentals, its need in data engineering and core concepts.
• Kafka Architecture — Kafka use cases and cluster components.
• Kafka Cluster Management — Brokers, topics, partitions, replication, producers and consumers.
• Reliability & Best Practices — High availability and fault-tolerant Kafka architectures.

Capstone Project

• Industry-Grade Capstone Project — Apply the concepts learned throughout the curriculum to build an end-to-end data engineering solution.
• End-to-End Data Engineering — Work with data ingestion, storage, processing, transformation and analytics components.
• Cloud Data Engineering — Implement a production-oriented solution using relevant Azure and modern data engineering technologies.
• Portfolio Development — Build a project that demonstrates practical, industry-ready Data Engineer skills.
• Real-World Scenario — Solve a business-focused data engineering problem using appropriate architecture and engineering practices.

Power BI & Business Intelligence

Module 26: Power BI & Business Intelligence

• Connecting Power BI to Azure, Snowflake and Databricks data sources.
• Data Modeling & DAX Basics — Relationships, calculated columns and measures.
• Building Dashboards & Reports — Visuals, interactivity and drill-throughs.
• Row-Level Security & Publishing — Securing, publishing and sharing Power BI reports.

DevOps

Module 28: DevOps

• CI/CD concepts and automation using build and release pipelines.
• Docker & Kubernetes — Containerization fundamentals and introduction to orchestration.
• Infrastructure as Code — Automated environment provisioning and infrastructure management.
• Real-world DevOps Projects — Applied hands-on practice with DevOps tools and workflows.

Upcoming Batch Schedule for Data Engineering Training

Besant Technologies provides flexible timings to all our students. Here are the Data Engineering Training Classes Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

• 26-10-2026 Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fees
• 22-10-2026 Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per Session Get Fees
• 24-10-2026 Sat (Sat - Sun)Weekend Batch 11:00 AM (IST) (Class 3Hrs) / Per Session Get Fees

Can’t find a batch you were looking for?

Request a Batch

Trainer Profile

Learn from trainers who guide you through data engineering concepts with practical examples and technical exercises. The training focuses on helping learners understand the tools, work through challenging topics, and apply their knowledge to data engineering tasks.

Whether you are beginning your career or upgrading your existing technical skills, trainer guidance and project-based practice can help you build a stronger foundation and prepare for technical interviews.

Data Engineering Certification

Besant Technologies Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our certification at Besant Technologies is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.

A recognised certification can help demonstrate your knowledge of specific data engineering technologies. Learners interested in the Microsoft data platform can explore the Microsoft Certified: Fabric Data Engineer Associate (DP-700) certification, which focuses on data engineering solutions using Microsoft Fabric.

The course can help you develop relevant technical knowledge as you prepare for certification. Certification requirements and exam details should be checked through the official Microsoft website.

Key Features of Data Engineering Training

• Python and SQL fundamentals
• Data cleaning and transformation
• ETL and data pipeline concepts
• Cloud storage and data integration
• Azure data engineering fundamentals
• Apache Spark and PySpark practice
• Databricks and Lakehouse concepts
• Microsoft Fabric and Snowflake
• Apache Airflow and Kafka fundamentals
• Hands-on exercises and capstone project
• Certification guidance

Training Courses Reviews

Frequently Asked Questions