Data Engineering Training
Build Scalable Data Solutions with Our Data Engineering Training Course! Learn essential skills in data pipeline development, ETL processes, data warehousing, and big data technologies. This practical, hands-on course covers tools like Python, SQL, Apache Spark, and cloud platforms to help you manage, process, and optimize large data systems. Discover how data engineering powers analytics, AI, and business intelligence, and build job-ready expertise for high-demand data roles.
7 | 8 monthsCourse duration
Classroom | OnlineMode of Delivery
09Capstone projects
Why should you do this course?
Learn and grow as a developer with our project based courses.
Analyze Massive Data Sets Efficiently
Learn how to build powerful web apps from scratch using MongoDB, Express, React, and Node. This course is highly practical and project-based.
Build Fast, Scalable Data Pipelines
Master both frontend and backend technologies and become a job-ready full stack developer, capable of handling entire application flows.
Optimize Data Workflows with Spark
MERN stack is one of the most popular stacks used by top companies. This course prepares you for full stack developer roles with strong job prospects.
Turn Complex Data into Insights
Gain hands-on experience with Git, GitHub, REST APIs, JWT, MVC architecture, and deployment strategies used in real-world teams.
Starting from₹ 70000₹ 50000
LIVE BATCHKey Highlights
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Live Projects
7 | 8 months duration
Certificate of Excellence/Completion
Placement assistance
Syllabus
Quickstart
Overview of the Journey
We offer live classes with expert instructors, weekly assignments to reinforce your learning, and fully practical training focused on real-world skills. You’ll work on hands-on projects throughout the course to build experience and confidence.
Python Programming Language
Python Basics
Python Basics form the backbone of any data engineering course, offering a strong foundation for building data pipelines and processing workflows. In this module, students will learn about essential Python data types such as integers, floats, strings, and booleans, along with advanced collections like lists, tuples, and dictionaries that are widely used in data processing tasks. The topic also covers conditional statements (if, elif, else) for decision-making and looping structures (for and while loops) to automate repetitive operations. Additionally, learners will master file handling in Python, an important skill for reading, writing, and manipulating data files, which is crucial for working with large datasets in real-world data engineering projects. By the end of this module, participants will have a clear understanding of Python’s core concepts, preparing them for advanced data engineering tools and frameworks.
Functions and Exception Handling
Functions and Exception Handling are essential for writing clean, reliable Python code in data engineering. This module covers how to create and use functions for reusable, organized code and introduces exception handling with try, except, else, and finally to manage errors during data processing. These skills help data engineers build efficient, fault-tolerant scripts for tasks like file operations, database access, and API integrations, ensuring smooth and error-free data workflows.
Data Analytics Using Pandas
Data Analytics using Pandas is a must-have skill for data engineers to efficiently handle and analyze structured data. In this module, learners will explore the powerful Pandas library in Python, focusing on creating, manipulating, and analyzing DataFrames and Series. The course covers essential operations like data cleaning, filtering, sorting, grouping, merging, and handling missing values, which are crucial for preparing data for further processing. With hands-on practice on real-world datasets, students will learn to perform quick and effective data analysis tasks, making it easier to build robust ETL pipelines and reporting solutions. Mastering Pandas is key to excelling in data engineering and analytics projects.
Data Visualization
Data Visualization is a crucial skill for data engineers to present insights clearly and effectively. This module introduces popular Python libraries like Matplotlib and Seaborn for creating informative charts and graphs. Learners will practice generating bar charts, line graphs, pie charts, histograms, and heatmaps to explore and visualize trends in large datasets. Data visualization helps in understanding data patterns, identifying outliers, and communicating results to stakeholders. By the end of this topic, students will be able to create compelling visuals that support data-driven decision-making in real-world data engineering projects
Object Oriented Programming
Object Oriented Programming (OOP) in Python is essential for building scalable, organized, and efficient data engineering solutions. This module covers core OOP concepts like classes, objects, inheritance, encapsulation, polymorphism, and constructors, enabling learners to structure code for complex data workflows. By applying OOP principles, data engineers can create reusable, modular, and maintainable scripts for handling large-scale data pipelines, APIs, and automation tasks. Mastering OOP not only improves code quality but also prepares learners for advanced tools and frameworks in data engineering
MYSQL Database
Introduction to Database
An Introduction to Database is the first step for anyone looking to manage, store, and retrieve data effectively in data engineering projects. This module covers the fundamentals of databases, types of databases (relational and non-relational), database management systems (DBMS), and key concepts like tables, records, and primary keys. Learners will understand how databases support efficient data storage, organization, and access in large-scale applications. This topic builds a strong foundation for working with popular databases like MySQL, PostgreSQL, and NoSQL systems, essential for modern data engineering workflows.
Extracting Data Using MySQL
Extracting data using MySQL is a critical skill for data engineers to efficiently retrieve and manipulate large datasets from relational databases. This module focuses on writing advanced SQL queries, including SELECT statements, filtering with WHERE clauses, sorting, joins, subqueries, and aggregate functions to extract meaningful insights from complex data. Learners will practice optimizing queries for better performance and integrating MySQL data extraction with ETL pipelines and data processing tasks. Mastery of data extraction techniques in MySQL enables data engineers to streamline workflows and support data-driven decision-making.
Functions Filtering & Subqueries
Functions, Filtering, and Subqueries are powerful SQL tools that enable data engineers to write precise and efficient queries for complex data extraction. This module covers the use of SQL functions like aggregate (SUM, COUNT, AVG) and scalar functions, advanced filtering techniques using WHERE, HAVING, and logical operators, and crafting subqueries to perform nested data retrievals. Mastering these concepts allows learners to handle intricate database queries, optimize data workflows, and prepare clean, actionable datasets for downstream data engineering tasks and analytics.
GroupBy & Joins
GroupBy and Joins are essential SQL techniques that empower data engineers to aggregate and combine data from multiple tables efficiently. This module teaches how to use GROUP BY to summarize data with aggregate functions like SUM, COUNT, and AVG, and explains different types of joins (INNER, LEFT, RIGHT, FULL) to merge related datasets seamlessly. Mastery of GroupBy and Joins is crucial for building complex queries, optimizing data pipelines, and enabling comprehensive data analysis in real-world engineering projects.
Windows Functions
Window Functions are advanced SQL tools that enable data engineers to perform complex calculations across sets of rows related to the current row without collapsing the result set. This module covers essential window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG(), and aggregate functions used with OVER() clauses. Learners will understand how to apply these functions for running totals, moving averages, and ranking data, which are critical for time-series analysis, data reporting, and building sophisticated data pipelines. Mastering window functions enhances your ability to write efficient, powerful queries in modern data engineering workflows.
Indexing and Partitioning
Indexing and Partitioning are key database optimization techniques that help data engineers improve query performance and manage large datasets efficiently. This module explains how indexes speed up data retrieval by creating pointers to rows in tables, while partitioning divides large tables into smaller, manageable pieces for faster query processing and maintenance. Understanding these concepts is crucial for designing scalable databases and optimizing ETL pipelines in big data environments, ensuring smooth and speedy access to critical information.
Normalization & Transactions
Normalization and Transactions are fundamental concepts in database management that ensure data integrity and efficiency. This module covers normalization techniques to organize data into related tables, minimizing redundancy and improving database structure. It also explains transactions, which group multiple SQL operations into a single unit, ensuring ACID properties (Atomicity, Consistency, Isolation, Durability) for reliable and error-free data processing. Mastering these topics helps data engineers build robust, scalable databases that support complex data workflows and maintain data accuracy.
Hadoop Distribution File System
HDFS
HDFS (Hadoop Distributed File System) is a foundational technology in big data engineering, designed to store and manage massive datasets across distributed clusters efficiently. This module introduces the architecture of HDFS, highlighting its fault tolerance, scalability, and high-throughput data access. Learners will explore how HDFS breaks data into blocks, replicates them across nodes, and supports parallel processing frameworks like Hadoop MapReduce and Spark. Understanding HDFS is essential for data engineers working with large-scale data storage and processing in modern big data ecosystems.
YARN
YARN (Yet Another Resource Negotiator) is a vital resource management layer in the Hadoop ecosystem, enabling efficient allocation and scheduling of cluster resources for big data applications. This module covers how YARN manages computing resources across distributed nodes, supports multiple data processing frameworks like MapReduce and Spark, and ensures optimal workload balancing and scalability. Understanding YARN empowers data engineers to build robust, high-performance data pipelines capable of handling large-scale data processing tasks in modern big data environments.
Map Reduce
MapReduce is a powerful programming model for processing and generating large datasets in a distributed computing environment. This module explains the core concepts of Map and Reduce functions, which break down complex data processing tasks into smaller, parallelizable operations across a Hadoop cluster. Learners will understand how MapReduce enables scalable, fault-tolerant data processing for tasks like aggregation, filtering, and sorting massive datasets. Mastering MapReduce is essential for data engineers working with big data frameworks to build efficient and scalable data pipelines.
Apache Pyspark
Spark core concepts
Spark Core is the foundation of Apache Spark, providing fast and general-purpose distributed computing for big data processing. This module introduces key concepts such as Resilient Distributed Datasets (RDDs), transformations, actions, and Spark’s in-memory computing capabilities that enable lightning-fast data processing. Learners will explore how Spark Core supports fault tolerance, scalability, and easy integration with various data sources, making it ideal for building real-time data pipelines and complex analytics workflows. Mastery of Spark Core is essential for modern data engineers working with large-scale data processing.
RDDs, DataFrames, and SparkSQL
RDDs, DataFrames, and SparkSQL are core components of Apache Spark that empower data engineers to process and analyze big data efficiently. This module covers Resilient Distributed Datasets (RDDs) for low-level distributed data processing, DataFrames for optimized, schema-based data manipulation, and SparkSQL for running SQL queries on large datasets with ease. Learners will gain hands-on experience transforming, filtering, and aggregating data using these tools, enabling them to build scalable, high-performance data pipelines and perform complex analytics seamlessly within the Spark ecosystem.
Parallel processing and distributed computing with Spark
Parallel processing and distributed computing with Apache Spark enable data engineers to handle massive datasets quickly and efficiently. This module explores how Spark divides tasks across multiple nodes in a cluster, leveraging in-memory computation and task parallelism to accelerate data processing workflows. Learners will understand Spark’s architecture for distributing workloads, fault tolerance mechanisms, and how to optimize jobs for better performance. Mastering these concepts is essential for building scalable, high-speed data engineering pipelines that can process big data in real time.
Spark for data transformation, aggregation, and analytics
Apache Spark is a powerful engine for data transformation, aggregation, and advanced analytics in modern data engineering. This module teaches learners how to leverage Spark’s APIs to perform efficient data cleaning, filtering, joining, and grouping operations at scale. Students will also explore Spark’s built-in functions for aggregations, window operations, and complex analytics to derive meaningful insights from large datasets. By mastering Spark for these tasks, data engineers can build fast, scalable pipelines that support real-time analytics and data-driven decision-making.
Powerful data processing with PySpark for scalable analytics
PySpark brings the power of Apache Spark to Python, enabling scalable and efficient data processing for modern data engineering workflows. This module covers how to use PySpark’s APIs to handle large datasets, perform complex transformations, and execute distributed analytics seamlessly. Learners will gain hands-on experience with RDDs, DataFrames, and SparkSQL in Python, building scalable pipelines that can process big data quickly and support real-time decision-making. Mastering PySpark is essential for data engineers aiming to combine Python’s simplicity with Spark’s performance for robust analytics.
Real World Big Data Pipelines
Design and implement a basic pipeline using Hadoop or Spark
Designing and implementing a basic data pipeline using Hadoop or Spark is a fundamental skill for data engineers to manage and process large-scale data efficiently. This module guides learners through the end-to-end process of building data pipelines, including data ingestion, processing, and storage using Hadoop’s HDFS and MapReduce or Spark’s in-memory computing and distributed processing capabilities. Students will gain practical experience in orchestrating workflows that handle batch or real-time data, enabling them to create scalable, fault-tolerant pipelines essential for modern big data applications.
Data storage, transformations, and querying
Data storage, transformations, and querying are core pillars of data engineering that enable effective management and analysis of large datasets. This module covers various storage solutions, including relational databases, data lakes, and distributed file systems, alongside techniques for transforming raw data into clean, usable formats. Learners will explore querying methods using SQL, SparkSQL, and other tools to extract valuable insights efficiently. Mastering these concepts equips data engineers to build robust pipelines that support scalable data processing and real-time analytics.
Data Streaming
Introduction to streaming data
Introduction to Streaming Data covers the fundamentals of handling continuous data flows generated by real-time applications and devices. This module explains what streaming data is, how it differs from batch data, and the challenges involved in processing high-velocity, high-volume data streams. Learners will gain insight into the importance of event-driven systems, data ingestion methods, and the role of streaming platforms like Apache Kafka in capturing live data. Understanding streaming data is essential for building real-time data pipelines and responsive analytics in today’s fast-paced data engineering landscape.
Apache Kafka: Basics
Apache Kafka is a leading distributed streaming platform widely used for building real-time data pipelines and streaming applications. This module introduces the basics of Kafka, including its core components like producers, consumers, topics, and brokers, and explains how Kafka enables high-throughput, fault-tolerant, and scalable data streaming. Learners will understand Kafka’s architecture and explore use cases such as log aggregation, event sourcing, and messaging systems. Mastering Apache Kafka basics is essential for data engineers working on real-time data processing and streaming solutions.
Stream processing with Spark Streaming
Stream Processing with Spark Streaming enables data engineers to process real-time data streams efficiently within the Apache Spark ecosystem. This module covers the fundamentals of Spark Streaming, including DStreams, window operations, and stateful processing, allowing learners to build scalable, fault-tolerant streaming applications. Students will explore how Spark Streaming integrates with sources like Kafka and HDFS to ingest and analyze live data, supporting use cases such as real-time analytics, monitoring, and alerting. Mastering Spark Streaming is key to developing robust, low-latency data pipelines for modern data engineering workflows
Amazon Web Services
AWS EMR and AWS S3
AWS EMR (Elastic MapReduce) and AWS S3 (Simple Storage Service) are powerful cloud services essential for scalable big data processing and storage. This module introduces AWS EMR as a managed Hadoop and Spark platform that enables data engineers to run large-scale distributed data processing jobs without managing infrastructure. Alongside, learners will explore AWS S3, a highly durable and scalable object storage service used for storing raw and processed data. Understanding how to integrate EMR with S3 allows data engineers to build flexible, cost-effective, and efficient data pipelines in the cloud, supporting modern data engineering and analytics needs.
EC2 and Elastic IP
AWS EC2 (Elastic Compute Cloud) provides scalable virtual servers that data engineers use to deploy and manage applications in the cloud with flexibility and control. This module covers launching, configuring, and securing EC2 instances to run data processing tasks and host big data tools. It also explains Elastic IPs, static IP addresses that enable consistent, reliable connectivity to EC2 instances, essential for stable access and integration in distributed data systems. Mastering EC2 and Elastic IP helps data engineers build resilient, scalable cloud infrastructures for efficient data workflows.
AWS Storage and Networking
AWS Storage and Networking services form the backbone of scalable and secure cloud-based data engineering solutions. This module covers key storage options like Amazon S3, EBS (Elastic Block Store), and Glacier for versatile data retention and backup, alongside networking services such as VPC (Virtual Private Cloud), Load Balancers, and Route 53 for secure, high-performance connectivity. Learners will understand how to architect efficient data pipelines that leverage AWS’s robust storage and networking capabilities to ensure data availability, security, and seamless communication across distributed systems.
AWS Glue & Redshift
AWS Glue and Redshift are powerful cloud services that simplify data integration and analytics for data engineers. This module introduces AWS Glue, a fully managed ETL (Extract, Transform, Load) service that automates data preparation and cataloging, making it easier to move data between storage and analytics platforms. It also covers Amazon Redshift, a fast, scalable data warehouse designed for complex queries and large-scale data analysis. Learners will gain hands-on experience integrating Glue and Redshift to build efficient, end-to-end data pipelines that support real-time analytics and business intelligence in the cloud.
Linux Operating System
Introduction to Linux
Introduction to Linux provides data engineers with essential skills to navigate and manage Linux-based systems commonly used in data infrastructure. This module covers basic Linux commands, directory structures, file permissions, process management, and shell scripting fundamentals. Gaining proficiency in Linux enables learners to efficiently handle server environments, automate tasks, and support big data tools and applications critical to modern data engineering workflows.
Process Management
Process Management in Linux is vital for data engineers to monitor, control, and optimize running applications and services on data servers. This module covers key commands like ps, top, kill, nice, and systemctl to view active processes, manage system resources, and troubleshoot performance issues. Understanding process management enables learners to ensure smooth operation of data pipelines, maintain server health, and automate task scheduling in complex data engineering environments.
System configuration and advanced
System Configuration and Advanced Linux Concepts are essential for data engineers to fine-tune server environments and manage large-scale data systems effectively. This module covers advanced topics like user and group management, environment variables, disk partitioning, network configurations, firewall settings, and service management. Learners will also explore cron jobs, system logs, and performance tuning techniques to optimize server reliability and security. Mastering these skills ensures data engineers can deploy and manage robust, high-performance infrastructure for big data workflows.
Linux Commands
Linux Commands are the foundation for managing and automating tasks in server environments widely used in data engineering. This module introduces essential commands for file management, directory navigation, permissions handling, process monitoring, network configuration, and system operations. Learners will gain practical experience with commands like ls, cp, mv, rm, ps, top, grep, chmod, and tar, enabling efficient server management and automation of daily operations in big data projects. Proficiency in Linux commands is crucial for data engineers working across cloud, on-premises, and distributed systems.
ETL and Data Warehousing
ETL Pipelines
ETL Pipelines are the backbone of data engineering, enabling seamless data movement from multiple sources to storage and analytics platforms. This module focuses on designing and building efficient Extract, Transform, Load (ETL) workflows using tools like Apache Spark, AWS Glue, and SQL-based solutions. Learners will gain hands-on experience in data extraction, cleansing, transformation, and loading processes, ensuring data quality, consistency, and availability for reporting and analytics. Mastering ETL pipelines is essential for data engineers handling large-scale, real-time, and batch data processing projects
Data Warehousing
Data Warehousing is a crucial aspect of data engineering that focuses on storing, organizing, and managing large volumes of structured data for business intelligence and analytics. This module introduces the core concepts of data warehouses, including schema design, star and snowflake models, partitioning, indexing, and data aggregation techniques. Learners will explore popular data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake, gaining skills to build scalable, high-performance storage systems that support complex queries and data-driven decision-making.
Advance Data Operations
Advance Data Engineering
Advanced Data Engineering focuses on building scalable, high-performance data systems capable of handling complex, large-scale data workflows. This module covers topics like distributed data processing, real-time data pipelines, cloud data infrastructure, data orchestration, and optimization techniques. Learners will explore tools such as Apache Spark, Kafka, Airflow, and cloud services like AWS and Azure to design and manage end-to-end data solutions. Mastering advanced data engineering concepts prepares professionals to tackle enterprise-level data challenges, ensuring data reliability, scalability, and security in dynamic, big data environments.
DevOps for Data Engineering
DevOps for Data Engineering bridges the gap between data pipelines and infrastructure management, ensuring seamless deployment, monitoring, and automation of data workflows. This module introduces essential DevOps practices like CI/CD (Continuous Integration and Continuous Deployment), containerization with Docker, orchestration with Kubernetes, and infrastructure-as-code tools like Terraform. Learners will understand how to automate data pipeline deployments, manage scalable environments, and monitor system health effectively. Mastering DevOps tools and strategies empowers data engineers to build reliable, agile, and scalable data platforms in both on-premises and cloud environments.
Data Security
Data Security is a critical aspect of data engineering, ensuring the protection of sensitive information across storage, processing, and transmission. This module covers key concepts like data encryption, access control, network security, secure authentication, and compliance standards (GDPR, HIPAA, etc.). Learners will explore tools and practices to safeguard data in cloud and on-premises environments, implement role-based access, and secure data pipelines against unauthorized access and breaches. Mastering data security is essential for data engineers to maintain data integrity, confidentiality, and regulatory compliance in modern data ecosystems.
DSA System Design
DSA
Data Structures and Algorithms (DSA) form the foundation for writing efficient, optimized, and scalable code in data engineering projects. This module covers essential data structures like arrays, linked lists, stacks, queues, trees, hash tables, and graphs, along with algorithms for sorting, searching, and traversal. Learners will understand how to apply these concepts to solve real-world data problems, optimize data processing tasks, and improve the performance of ETL pipelines and distributed systems. Mastering DSA equips data engineers with the problem-solving skills required for designing high-performance, reliable data solutions.
System Design
System Design is a vital skill for data engineers to architect scalable, reliable, and efficient data systems that handle massive volumes of data. This module introduces principles of distributed system architecture, data modeling, API design, database selection, data partitioning, caching strategies, and load balancing. Learners will explore how to design robust data pipelines, storage solutions, and real-time analytics platforms that can scale with business needs. Mastering system design empowers data engineers to build end-to-end data infrastructures capable of supporting enterprise-level data processing and analytics.
Data Security
Data Security is essential in data engineering to protect sensitive information throughout its lifecycle—from ingestion to storage and processing. This module covers critical topics like encryption methods, access controls, data masking, secure authentication, and compliance with regulations such as GDPR and HIPAA. Learners will gain practical knowledge on implementing security best practices in cloud and on-premises environments to safeguard data pipelines against breaches and unauthorized access. Mastering data security ensures data integrity, confidentiality, and compliance in today’s data-driven organizations.
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Learn and grow as a developer with our project based courses.
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Industry-vetted curriculum
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Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Project based learning
Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.
Superb placements
Result oriented courses across all genres, students as well as Working professionals.
Certificate of completion
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See what students have to say
Joining DATADRIX means you'll create an amazing network, make new connections, and leverage diverse opportunities.
I joined Datadrix to learn Python and Data Engineering. Thanks to Om Arora for simplifying coding concepts and providing practical projects to work on.
Datadrix Institute helped me build a solid base in Python and Data Science. Special thanks to Nitin Shrivastav for his clear and practical teaching.
Thanks to Datadrix’s Data Analytics program, I cracked my interview confidently. Nitin Shrivastav’s sessions were insightful and very practical.
Loved learning Python and Data Science here. Datadrix has the best trainers and projects. Special thanks to Om Arora for his real-world examples.
Finally cracked my second job in data science after Datadrix’s training. Nitin Shrivastav’s SQL and Power BI sessions boosted my confidence.
Datadrix Institute made learning Web Development super fun! Om Arora’s support and practical project work made the course so much more valuable.
The Data Analytics course by Datadrix Institute was worth it. Nitin Shrivastav’s explanations on tools like Excel and Power BI made it easy.
Datadrix's Data Science program gave me clarity on statistics and ML. Om Arora explained tough topics in a very simple and relatable way.
Big thanks to Datadrix for helping me master Python programming. Nitin Shrivastav’s approach to teaching made coding fun and easy to follow.
Datadrix's Data Science program gave me clarity on statistics and ML. Nitin sir explained tough topics in a very simple and relatable way.
The Data Analytics course at Datadrix helped me land my job as a data analyst. Nitin Shrivastav’s clear and patient teaching style stood out.
The Python programming training was perfect for beginners. Thanks to Nitin Shrivastav for always clearing doubts patiently and giving real projects.
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