Build Data Ingestion and Transformation Workflows
- ✓Write Python scripts for ingestion, transformation and validation
- ✓Extract data from files, databases and APIs
- ✓Add validation, error handling and repeatable processing logic
Live Online Data Engineering Course in India
Data Does Not Move Itself. Learn to Build the Pipelines That Do.
Dashboards, machine learning and AI cannot work without trusted data arriving in the right place, in the right format and at the right time.
The NeoNex MindX Data Engineering course teaches you to design, build, orchestrate and monitor data pipelines across databases, warehouses, distributed systems, streaming platforms and cloud environments.
- ✓6–7 month live online learning path
- ✓Python and advanced SQL for data pipelines
- ✓ETL, ELT, warehousing and dimensional modelling
- ✓Apache Spark, Kafka and Airflow
- ✓Cloud platform and reliability foundations
- ✓3–6 month internship component with certification
Program Snapshot
Data Engineering Program at a Glance
| Program Duration | 6–7 months |
|---|---|
| Training Mode | Live online, interactive sessions |
| Recommended Foundation | Basic programming and database awareness |
| Core Technologies | Python, SQL, MongoDB, Spark, Kafka, Airflow and cloud platforms |
| Learning Access | LMS, recordings, notes and resources |
| Practical Learning | Assignments, technical projects and portfolio outputs |
| Internship | 3–6 month component with certification |
| Career Support | Resume, profile, interview and placement assistance |
| Batch Options | Weekday morning, weekday evening and weekend |
Course Readiness
Check Your Readiness for a Data Engineering Career Path
Share your current technical experience and target role. A course advisor will help you understand the prerequisites, curriculum, current fee, batch options and learning commitment.
Direct Answer
What Is a Data Engineering Course?
A data engineering course teaches you how to build systems that collect, transform, store and deliver data for analytics, reporting, machine learning and business operations.
The work includes databases, data models, ETL and ELT pipelines, distributed processing, streaming, orchestration, cloud services and data reliability.
This online Data Engineering course in India covers Python, advanced SQL, MongoDB, data warehousing, dimensional modelling, ETL and ELT, data quality, Apache Spark, Kafka, Airflow and cloud data platforms.
You learn not only how a pipeline runs, but also how it fails, recovers, scales and remains trustworthy.
The Engineering Gap
A Pipeline That Works Once Is Not a Reliable Data System.
Data engineering requires more than connecting a source to a destination. You need to design for data volume, changing schemas, failures, quality checks, scheduling and maintainability.
| What Usually Goes Wrong | How This Program Solves It |
|---|---|
| SQL is learned without data-system design | Move from individual queries into schema design, optimisation, transactions and reusable processing workflows |
| ETL is understood only as a diagram | Build ingestion, transformation, loading, validation and error-handling steps in practice |
| Big-data tools are learned without foundations | Understand partitioning, distributed processing and batch design before relying on Spark commands |
| Orchestration and recovery are ignored | Use Airflow concepts, dependencies, retries, alerts and backfills to manage multi-stage workflows |
| Cloud concepts remain disconnected buzzwords | Connect storage, compute, warehouses, access, cost and reliability inside one system design |
Learning Outcomes
What You Will Be Able to Do After the Program
Design Databases and Analytical Storage
- ✓Design relational schemas and optimise advanced SQL workflows
- ✓Model semi-structured data in MongoDB
- ✓Create dimensional models for analytics and reporting
Build and Operate Data Pipelines
- ✓Build batch ETL and ELT pipelines with data-quality checks
- ✓Document source-to-target mappings, dependencies and recovery steps
- ✓Manage incremental loads and pipeline failures
Work with Distributed and Streaming Systems
- ✓Process distributed datasets with Apache Spark foundations
- ✓Create a streaming-ingestion prototype with Apache Kafka
- ✓Orchestrate dependent workflows with Apache Airflow
Plan Cloud and Reliable Data Platforms
- ✓Plan data platforms using cloud storage, compute and warehouses
- ✓Document lineage, monitoring, ownership and failure-recovery practices
- ✓Consider scalability, access control, governance and cost
Who Should Join
Who Should Join This Data Engineering Course?
Software and Backend Developers
Move toward data platforms, pipelines, distributed processing and cloud systems.
SQL and Database Professionals
Progress from database work into ETL, warehousing, orchestration and data reliability.
Data Analysts and BI Professionals
Understand and build the engineering layer that supplies trusted data to reports and dashboards.
Data Science Professionals
Gain the pipeline and platform knowledge required to support model training and production data workflows.
Engineering and Computer Science Graduates
Build a focused portfolio for entry-level data engineering and ETL roles.
Cloud and DevOps Learners
Add databases, data movement, processing and governance to existing infrastructure skills.
Recommended preparation: Basic programming and database awareness are helpful. Python and
SQL foundations are included, but regular hands-on practice is essential for this technical path.
Learning Journey
How You Progress from Scripts to a Complete Data Platform
Build Scripting and SQL Depth
Automate data tasks and write efficient, reliable database workflows.
Model Data for Use
Design operational and analytical structures with clear keys, grain and relationships.
Build Tested Pipelines
Create ETL and ELT flows with validation, incremental loads and error handling.
Process Data at Scale
Apply Spark and distributed-computing concepts to larger batch workloads.
Add Streaming and Orchestration
Use Kafka and Airflow foundations to manage real-time and scheduled workflows.
Design for Cloud and Reliability
Document monitoring, lineage, access, cost, recovery and governance in the capstone.
Prepare Your Portfolio and Interviews
Organise code, architecture diagrams, runbooks and project documentation, then practise explaining your design choices.
Curriculum
Data Engineering Course Curriculum
The curriculum moves from scripting and databases into warehousing, ETL and ELT, distributed processing, streaming, orchestration, cloud platforms and data operations.
Programming, Databases and Data Models
Build Python automation, advanced SQL, NoSQL and dimensional-modelling foundations.
Pipeline Development and Integration
Learn how to extract, transform, validate, load and connect data across multiple sources and destinations.
Distributed Processing, Streaming and Orchestration
Progress into high-volume batch processing, event streaming and scheduled multi-stage workflows.
Cloud Platforms, Reliability and Capstone Work
Design scalable cloud data platforms, establish operational practices and complete an end-to-end portfolio project.
01Python for Data EngineeringAutomate ingestion and transformation tasks.
Topics covered
- Python scripting, functions, modules and exception handling
- File handling, CSV and JSON processing
- Task automation
- API data extraction
- Data-validation scripts
What you will build: An automated data-ingestion script.
02Advanced SQL and Relational Database EngineeringDesign and query relational systems efficiently.
Topics covered
- Complex joins, subqueries and common table expressions
- Window functions, ranking and analytical queries
- Stored procedures, functions, transactions and triggers
- Indexes, execution awareness and query optimisation
- Schema design, constraints and normalisation
- Data integrity and reliable processing workflows
What you will build: An optimised SQL data-processing workflow.
03MongoDB and NoSQL Data SystemsWork with document databases and scalable semi-structured data.
Topics covered
- Collections, documents and CRUD operations
- NoSQL data modelling
- Aggregation pipelines
- Indexing and performance optimisation
- Embedded and referenced relationships
- Replication and sharding awareness
- Distributed-data concepts
What you will build: A MongoDB data model and aggregation pipeline.
04Data Warehousing and Dimensional ModellingDesign analytical storage for reporting and downstream use.
Topics covered
- Data-warehouse architecture
- OLTP, OLAP and data marts
- Fact and dimension tables
- Grain, keys and slowly changing dimensions awareness
- Star and snowflake schemas
- Source-to-target mapping
- Warehouse implementation
What you will build: A dimensional model and source-to-target specification.
05ETL, ELT, Data Quality and TransformationBuild repeatable and reliable movement of data.
Topics covered
- Extraction, transformation and loading patterns
- ETL and ELT workflows
- Cleaning, validation and deduplication
- Standardisation and schema checks
- Batch design and incremental loads
- Error-handling concepts
- Pipeline testing and documentation
What you will build: An end-to-end ETL workflow with data-quality checks.
06Data Pipeline Architecture and IntegrationConnect sources, storage and consumers into an operable platform.
Topics covered
- Pipeline stages, dependencies and integration patterns
- API, file, database and SaaS source integration
- Data synchronisation and scheduling
- Failure-recovery concepts
- Metadata and data lineage
- Observability awareness
- Pipeline documentation and runbooks
What you will build: A pipeline architecture diagram and operational runbook.
07Big Data and Distributed ProcessingProcess high-volume data across distributed systems.
Topics covered
- Hadoop ecosystem overview
- Distributed-computing concepts
- Apache Spark foundations
- Spark DataFrame processing
- Partitioning
- Transformations and actions
- Performance considerations
- Batch-processing design
What you will build: A Spark-based data-transformation project.
08Streaming Data and Apache KafkaHandle events and near-real-time data flows.
Topics covered
- Batch and stream processing
- Kafka topics, producers and consumers
- Partitions and offsets
- Streaming-pipeline patterns
- Delivery-guarantee awareness
- Near-real-time use cases
- Streaming monitoring considerations
What you will build: A streaming-ingestion prototype.
09Workflow Orchestration with Apache AirflowSchedule and manage dependent data jobs.
Topics covered
- Directed acyclic graph concepts
- Tasks, dependencies and scheduling
- Retries and alerts
- Backfills
- Parameterised workflows
- Connecting ETL jobs
- Monitoring workflow runs
What you will build: An Airflow DAG for a multi-stage data pipeline.
10Cloud Data Platforms and WarehousesBuild cloud-scale data infrastructure.
Topics covered
- AWS or Azure data foundations based on the approved stack
- Cloud storage and compute
- Managed databases
- Cloud pipeline services
- Snowflake concepts
- BigQuery concepts
- Scalability and cost awareness
- Access-control considerations
What you will build: A cloud data-platform architecture.
11Data Reliability, Governance and OperationsKeep pipelines trustworthy and maintainable.
Topics covered
- Data-quality rules and reconciliation
- Pipeline checks
- Monitoring, logging and alerting
- Incident-response foundations
- Schema-change handling
- Documentation and ownership
- Governance and privacy
- Responsible data-access awareness
What you will build: A data-quality and operational-monitoring plan.
12Data Engineering Capstone and Career ReadinessDemonstrate a complete data-platform workflow.
Topics covered
- ETL pipeline and data-warehouse implementation
- Cloud warehouse or Kafka and Spark pipeline extension
- Architecture and implementation
- Code, documentation and testing
- Technical presentation
- GitHub portfolio preparation
- Resume and LinkedIn preparation
- Mock interviews and role-specific preparation
What you will build: An end-to-end data pipeline and warehouse portfolio project.
Tools and Technologies
Tools Covered in the Data Engineering Program
Every technology is taught inside a defined data-engineering workflow rather than as an isolated software feature.
PythonSQLMongoDBRelational DatabasesData WarehousingETL and ELT PatternsApache SparkPySparkApache KafkaApache AirflowSnowflake ConceptsBigQuery ConceptsAWS or Azure FoundationsGitGitHubDocker FoundationsData-Quality and Monitoring Practices
Projects and Portfolio
Build Data Engineering Projects That Demonstrate System Thinking
Your portfolio should show how data moves, how quality is checked and how the pipeline behaves when something changes or fails.
Automated API-to-Database Ingestion
Extract data from an API, validate records, handle errors and load the results into a designed database.
Analytics Data Warehouse
Create fact and dimension tables, source-to-target mappings and an ETL process for reporting.
Spark Transformation Pipeline
Process and transform a larger dataset using distributed DataFrame operations and partition-aware design.
Kafka Streaming Prototype
Publish and consume event data while documenting topics, partitions, offsets and monitoring needs.
Airflow-Orchestrated Workflow
Schedule dependent tasks, add retries and alerts, and document the pipeline runbook.
Cloud Data Platform Capstone
Combine ingestion, transformation, storage, orchestration, quality checks and serving in one architecture.
Why NeoNex MindX
Why Learn Data Engineering with NeoNex MindX?
Pipeline-First Curriculum
Follow the complete flow from source ingestion and transformation to storage, orchestration, serving and monitoring.
Modern Tools Built on Strong Foundations
Learn SQL and data modelling deeply before progressing to Spark, Kafka, Airflow and cloud platforms.
Architecture and Implementation
Create code, diagrams, source-to-target mappings, runbooks and quality checks.
Failure-Aware Engineering
Understand retries, schema changes, validation, observability and recovery instead of learning only happy-path demonstrations.
Live Technical Mentoring
Review queries, pipeline logic, data models and capstone architecture during guided sessions.
Career-Ready Proof
Build an end-to-end data-platform project and prepare to explain your design choices in interviews.
Trainer-Led Learning
Meet the Trainers Supporting Your Data Engineering Journey
Learn pipeline architecture, data systems, projects and professional readiness with trainers covering data engineering, analytics, AI, development, QA, DevOps and communication skills. Trainer allocation may vary by module and batch.
Nikhil
Developer & QA
LinkedIn Profile →Sanjana
DevOps
LinkedIn Profile →
Lakshmi
Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →
Neeraj
Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Yashas
Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Raveendran
Data Analytics & Data Engineering
LinkedIn Profile →Venkata Nagarjuna
Developer & QA
LinkedIn Profile →
Megha
Communication Skills, Soft Skills & Profile Building
LinkedIn Profile →
Internship and Career Support
Build Technical Depth, Portfolio Evidence and Interview Readiness
Internship Component
The program includes a 3–6 month internship component with certification, subject to current program requirements and completion of the required learning and project milestones.
Career and Placement Assistance
- ✓Resume and LinkedIn profile development
- ✓Job-portal profile setup and optimisation
- ✓GitHub, architecture and project-portfolio review
- ✓Mock interviews and technical-assessment practice
- ✓SQL, pipeline and system-design preparation
- ✓Communication and interview-readiness support
- ✓Relevant job-opening and referral support where applicable
Important: Data engineering roles can require strong SQL, coding and systems knowledge. Role eligibility depends on your background, technical depth and project quality. NeoNex MindX provides placement assistance, not a guaranteed job.
Discuss Your Technical Career PathCareer Paths
Career Paths Supported by Data Engineering Skills
The program supports preparation for roles such as Data Engineer, ETL Developer, Data Pipeline Engineer, Cloud Data Engineer, Big Data Engineer, Analytics Engineer, Database Developer and Data Platform Associate.
Job titles and entry requirements vary by employer, industry, prior experience and portfolio quality.
Course Comparison
Data Engineering vs Data Science: Which Path Fits You?
| Comparison Point | Data Engineering | Data Science |
|---|---|---|
| Primary Focus | Moving, transforming and serving reliable data | Analysing data and building predictive models |
| Core Tools | SQL, Python, warehouses, Spark, Kafka, Airflow and cloud platforms | Python, SQL, statistics, machine learning and deployment tools |
| Typical Output | Pipeline, warehouse, streaming system or data platform | Model, experiment, prediction application or analytical report |
| Main Technical Emphasis | Databases, backend systems, scalability and reliability | Statistics, experimentation, modelling and evaluation |
| Choose This When | You enjoy automation, data systems and scalable infrastructure | You enjoy analysis, experimentation and predictive modelling |
Batch Schedule
Flexible Live Batch Timings
Batch availability and start dates may vary. Confirm the current schedule during counselling.
Check the Next Available BatchWeekday Morning
Monday to Friday
7:00 AM–9:30 AMWeekday Evening
Monday to Friday
8:00 PM–10:30 PMWeekend Batch
Saturday and Sunday
9:00 AM–1:30 PMFrequently Asked Questions
Data Engineering Course FAQs
Data engineering is the discipline of building systems that collect, transform, store and deliver data. Data engineers create the pipelines, databases and platforms used by analysts, data scientists, applications and business teams.
Beginners with a technical interest can join because Python and SQL foundations are included. However, data engineering is a hands-on technical path, so learners should be ready to practise coding, databases and system concepts regularly.
The curriculum covers Python, advanced SQL, MongoDB, data warehousing, ETL and ELT, Apache Spark and PySpark, Apache Kafka, Apache Airflow, cloud data-platform concepts, Snowflake and BigQuery awareness, Git and reliability practices.
Yes. The program includes cloud storage, compute, managed databases, pipeline services, cloud warehouses, scalability, access control and cost awareness. The exact cloud stack may be aligned to the current batch.
ETL extracts data, transforms it before loading and then stores the prepared result. ELT loads data into a target platform first and performs transformations there. The program covers both patterns and when each is appropriate.
Yes. Learners study distributed-computing foundations, Spark DataFrames, transformations, actions, partitioning and performance considerations through a practical batch-processing project.
Yes. Kafka is introduced for streaming ingestion, producers, consumers, topics, partitions and offsets. Airflow is used to understand DAGs, scheduling, dependencies, retries, alerts and multi-stage workflow orchestration.
Project themes can include API ingestion, an analytics warehouse, ETL with quality checks, a Spark transformation pipeline, Kafka streaming, an Airflow DAG and a cloud data-platform capstone.
Data Engineering builds and maintains the systems that prepare reliable data. Data Analytics uses that prepared data to answer questions, build dashboards and support decisions. The roles work closely but require different core skills.
The program includes a 3–6 month internship component with certification, subject to current program requirements and completion of the required learning and project milestones.
Support can include resume and LinkedIn preparation, GitHub and architecture portfolio review, mock interviews, SQL and system-design practice, and relevant opening or referral support where applicable. Employment is not guaranteed.
Call 084318 03839 or submit the enquiry form. A NeoNex MindX advisor will share the current fee, EMI options, prerequisites, schedule, upcoming batch date and full syllabus.
Start Your Next Step
Build the Data Layer That Analytics and AI Depend On.
Get the complete Data Engineering syllabus and speak with an advisor about prerequisites, batch timings and your technical career path.