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CoursesHigh Demanding
Data Engineering Course in Hyderabad
High Demanding

Data Engineering Course in Hyderabad

Build practical skills with a Data Engineering Course in Hyderabad covering Python, SQL, ETL/ELT, Apache Spark, Kafka, Airflow, dbt, cloud data platforms, data warehousing, data lakes, and real-time pipelines. This Data Engineering Training in Hyderabad focuses on hands-on projects, modern data engineering tools, and production-ready workflows.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

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About Data Engineering Course in Hyderabad

The Data Engineering Course in Hyderabad is designed to build practical skills for developing scalable, production-ready data pipelines. The program covers Python, advanced SQL, data modeling, ETL and ELT, Apache Spark, PySpark, Apache Kafka, Airflow, dbt, data warehouses, data lakes, lakehouse architecture, cloud data platforms, data quality, governance, monitoring, and real-time processing.

This Data Engineering Training in Hyderabad includes hands-on learning across AWS, Azure, and Google Cloud concepts, with practical work on ingestion, transformation, orchestration, batch and streaming pipelines, and cloud data platforms. The Data Engineering Course Hyderabad also introduces Docker, Git, CI/CD, pipeline testing, and performance optimization.

Learners exploring Data Engineering Courses in Hyderabad can build skills through projects and a capstone. The program supports learners seeking a Data Engineer Course in Hyderabad, Data Engineer Training in Hyderabad, Data Engineering Classes in Hyderabad, Data Engineering Institute in Hyderabad, or an Online Data Engineering Course.

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Training Plan

01
About trainer

About trainer

Working professional who is carrying more than 10 years of industry experience.

02
Decks & Updated Content

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

03
e-Book

e-Book

E-book provided by TechPratham. All rights reserved.

04
Assignments & MCQs

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

05
Video Recording

Video Recording

Daily Session would be recorded and shared to the candidate.

06
Projects

Projects

Live projects will be provided for hands-on practice.

07
Resume Building

Resume Building

Expert-guided resume building with industry-focused content support.

08
Interview Preparation

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Data Engineering Course Curriculum

Module 1: Data Engineering Fundamentals

Understand the data engineering lifecycle, modern architectures, data sources, pipeline components, and the role of a data engineer.

Introduction to Data Engineering
Data Engineer Roles and Responsibilities
Data Engineering Lifecycle
Structured, Semi-Structured and Unstructured Data
Batch vs Real-Time Processing
ETL vs ELT
Modern Data Stack
Data Pipeline Architecture
Data Lake, Warehouse and Lakehouse
Data Engineering Best Practices

Module 2: Python and SQL for Data Engineering

Build strong programming and database skills required for developing and managing data pipelines.

Python Fundamentals
Data Structures and Functions
File Handling
CSV, JSON and API Data
Pandas for Data Processing
Error Handling and Logging
Advanced SQL
Joins and Subqueries
CTEs
Window Functions
Query Optimization
SQL for ETL Pipelines

Module 3: Data Modeling and Database Systems

Learn how to design efficient data models and database structures for analytics and enterprise applications.

Relational Databases
OLTP vs OLAP
Database Normalization
Data Modeling Fundamentals
Fact and Dimension Tables
Star Schema
Snowflake Schema
Slowly Changing Dimensions
Primary and Foreign Keys
Indexing
Partitioning
Data Warehouse Modeling

Module 4: ETL, ELT and Data Pipeline Development

Design automated pipelines that ingest, transform, validate, and deliver data for analytics and business use.

ETL Architecture
ELT Architecture
Data Extraction
Data Transformation
Data Loading
Batch Data Pipelines
Incremental Data Processing
Full vs Incremental Loads
Data Validation
Pipeline Error Handling
Pipeline Testing
Data Pipeline Documentation

Module 5: Apache Spark and PySpark

Process large datasets using distributed computing with Apache Spark and PySpark.

Apache Spark Architecture
Driver and Executors
Spark Jobs and Stages
RDD Fundamentals
DataFrames
Spark SQL
PySpark
Transformations and Actions
Joins and Aggregations
Partitioning
Caching
Spark Performance Optimization
Introduction to Structured Streaming

Module 6: Apache Kafka and Real-Time Data

Learn event-driven data ingestion and real-time streaming using Apache Kafka.

Kafka Architecture
Topics and Partitions
Producers and Consumers
Consumer Groups
Offsets
Replication
Kafka Connect
Schema Registry Concepts
Event-Driven Architecture
Real-Time Data Pipelines
Streaming Data Processing
Fault Tolerance
Monitoring Kafka Pipelines

Module 7: Apache Airflow and Workflow Orchestration

Build, schedule, monitor, and manage reliable data workflows with Apache Airflow.

Airflow Architecture
DAGs
Tasks and Operators
Sensors
Scheduling
Dependencies
Task Retries
Failure Handling
Backfilling
Pipeline Monitoring
Workflow Automation
Airflow with Cloud and Databases

Module 8: Data Warehousing, Data Lakes and Lakehouse

Understand modern analytical storage architectures and how data moves from raw sources to analytics-ready systems.

Data Warehouse Architecture
Data Lake Architecture
Lakehouse Architecture
Cloud Object Storage
Parquet and Columnar Formats
Partitioning
Data Layers
Bronze, Silver and Gold Architecture
Delta Lake Concepts
Data Warehouse Loading
Storage Optimization
Data Lifecycle Management

Module 9: Cloud Data Engineering

Explore data engineering services and architecture patterns across AWS, Microsoft Azure, and Google Cloud.

Cloud Data Engineering Fundamentals
AWS Data Engineering Services
Amazon S3
AWS Glue
Amazon Redshift
Amazon EMR
Azure Data Engineering Services
Azure Data Factory
Azure Data Lake
Azure Synapse
Google Cloud Data Engineering
Google Cloud Storage
BigQuery
Dataflow
Cloud Data Architecture

Module 10: dbt, Data Quality and Testing

Learn modern SQL transformation workflows, data testing, validation, and quality management.

dbt Fundamentals
dbt Models
Sources and Seeds
Incremental Models
Data Transformation
dbt Testing
Data Quality Rules
Data Validation
Duplicate Detection
Schema Validation
Data Profiling
Pipeline Testing
Data Quality Monitoring

Module 11: Data Governance, Security and Production Engineering

Prepare pipelines for production through governance, security, monitoring, version control, and deployment practices.

Data Governance Fundamentals
Data Security
Access Control
Encryption Concepts
Data Lineage
Metadata Management
Data Catalog Concepts
Pipeline Observability
Logging and Monitoring
Git and Version Control
Docker for Data Engineering
CI/CD Fundamentals
Cost Optimization
Production Troubleshooting

Module 12: End-to-End Data Engineering Capstone

Apply the complete data engineering workflow to design and build a production-style data platform.

Business Problem Definition
Source Data Analysis
Data Ingestion
Data Modeling
ETL/ELT Development
Spark Processing
Kafka Streaming
Airflow Orchestration
Cloud Storage
Data Warehouse
dbt Transformations
Data Quality Checks
Monitoring
Documentation
Git-Based Project Management
Final Presentation

High Demanding Courses

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Additional Program Highlights

Learning Materials

Comprehensive study materials and resources

HD
Resume Writing

Professional resume building session

HD
Interview Preparation

Master your interview skills

HD
Live Project Demo

Real-world project demonstrations

HD

Upcoming Batches

Can't find a batch you were looking for?

Who Should Take This Course?

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Career Opportunities After Learning Data Engineering

Data Engineer

Junior Data Engineer

Cloud Data Engineer

Key Projects

Data Engineering Course in Hyderabad

Walmart

Walmart – Retail Data Pipeline

Scenario: Build a scalable pipeline to process customer, product and sales data for analytics.

Live Work:

  • Build batch ingestion workflows
  • Transform sales and customer data
  • Create analytics-ready datasets
Outcome: Production-style retail data pipeline
WNS

WNS – Customer Analytics Platform

Scenario: Develop a cloud-based pipeline that integrates customer and operational datasets.

Live Work:

  • Ingest multi-source customer data
  • Build warehouse data models
  • Automate pipeline workflows
Outcome: Automated customer data platform
Accenture

Accenture – Real-Time Event Pipeline

Scenario: Build a streaming pipeline to process high-volume business events in near real time.

Live Work:

  • Ingest events with Kafka
  • Process streams with Spark
  • Monitor pipeline performance
Outcome: Scalable streaming pipeline
Deloitte

Deloitte – Cloud Data Platform

Scenario: Build an end-to-end cloud platform combining ingestion, transformation, warehousing and reporting.

Live Work:

  • Design cloud data architecture
  • Build ETL and warehouse workflows
  • Add quality and monitoring checks
Outcome: End-to-end cloud data platform
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Our Success Mantra

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Commitment

  • Ensuring quality training every day

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Fulfillment

  • Meeting learning goals with confidence

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Accomplishment

  • Students achieving industry-ready expertise

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Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What is Data Engineering?

What will I learn in this Data Engineering course?

Is this course suitable for beginners?

Do I need to know Python for Data Engineering?

Will I learn Apache Spark?

Will Kafka be covered?

What is the difference between ETL and ELT?

What is Apache Spark?

What is PySpark?

What is Apache Kafka?

What is a Kafka consumer group?

What is Apache Airflow used for?

Data Engineering Course Certificate

The TechPratham Data Engineering Course Certificate recognizes successful completion of the Data Engineering learning program and practical coursework.


The program covers:

  • Python and SQL
  • Data Modeling
  • ETL and ELT
  • Apache Spark and PySpark
  • Apache Kafka
  • Apache Airflow
  • Data Warehousing
  • Data Lakes and Lakehouse
  • Cloud Data Engineering
  • dbt
  • Data Quality
  • Data Governance
  • Production Data Engineering
  • End-to-End Capstone


Certificate Completion

Learners receive the TechPratham Data Engineering Course Certificate after completing the required course components and practical learning activities.

Important: This is a TechPratham course completion certificate. It is not an official AWS, Microsoft, Google Cloud, Databricks, Snowflake, or other third-party vendor certification unless the learner separately earns that certification from the respective provider.

Industry-Recognized Certification

Certificate
Data Engineering Course Certificate

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