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Curriculam Designed By Experts
Expertly designed curriculum for future-ready professionals.Industry Oriented Curriculam
An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company-
30+  Case Studies & Projects
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9+  Engaging Projects
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10+   Years Of Experience
- Overview of data engineering roles and responsibilities
- Evolution of big data and modern data platforms
- Understanding data pipelines and data lifecycle
- Difference between Data Engineer, Data Analyst, and Data Scientist
- Batch vs Real-time processing
- Introduction to structured, semi-structured, and unstructured data
- Popular data engineering tools and technologies
- Python basics: data types, functions, loops, modules
- Working with files, APIs, and data formats (CSV, JSON, XML)
- SQL fundamentals: joins, subqueries, window functions
- Data extraction, transformation, and loading using Python
- Pandas for data manipulation
- SQL performance optimization techniques
- Introduction to Data Warehousing concepts (OLAP, OLTP)
- Star and Snowflake schema design
- ETL vs ELT – definitions and use case
- Building ETL pipelines with tools
- Data loading strategies and scheduling jobs
- Data validation and reconciliation
- Working with dimensional modeling
- Introduction to big data and Hadoop ecosystem
- HDFS and MapReduce architectur
- Apache Spark core concepts (RDD, DataFrames, DAGs)
- Spark SQL and data manipulation
- Spark Streaming for real-time processing
- Overview of data ingestion architectures
- Batch vs streaming ingestion methods
- Introduction to Apache Kafka
- Kafka producers, consumers, and topics
- Integration of Kafka with Spark and Flink
- Ingesting data from various sources
- Overview of cloud computing for data engineering
- AWS/GCP/Azure data services comparison
- Using AWS S3, Glue, Redshift, and Lambda
- Google Cloud Storage, BigQuery, and Dataflow
- Cloud-based data pipelines and orchestration
- IAM, permissions, and security best practices
- Serverless architecture concepts
- Understanding orchestration and scheduling
- Apache Airflow fundamentals (DAGs, tasks, operators)
- Creating workflows for ETL pipelines
- Error handling and retry mechanisms
- Workflow monitoring and logging
- Data quality dimensions and validation rules
- Implementing data profiling and cleansing techniques
- Metadata management and lineage tracking
- Data cataloging tools (e.g., Apache Atlas, Amundsen)
- Introduction to data governance frameworks
- GDPR, HIPAA, and compliance considerations
- Dashboard integration or data export layer
- Documentation and presentation of the project
- Version control with Git and GitHub portfolio setup
- Career guidance, resume building, and mock interviews
Data Engineering Training Projects
Become a Data Engineering Expert With Practical and Engaging Projects.- Practice essential Tools
- Designed by Industry experts
- Get Real-world Experience
Data Cleaning and Transformation
Work on a dataset to identify and fix missing values, duplicates, and formatting issues using Python or SQL.
Simple ETL Pipeline
Create a basic ETL (Extract, Transform, Load) pipeline that extracts data from a CSV, transforms it, and loads it into a database.
Schema Design
Design and implement a relational database schema for a small business (e.g., online store). This builds foundational skills.
Building a Data Warehouse
Develop a small data warehouse integrating data from multiple sources for analytical queries.
Real-time Data Streaming
Set up a data pipeline using Apache Kafka to stream real-time data into a database or dashboard.
Data Pipeline Automation
Automate a multi-step data pipeline using Apache Airflow or similar orchestration tools.
Big Data Processing with Apache Spark
Implement a scalable data processing job to analyze large datasets using Spark’s distributed computing.
Cloud Data Engineering Solution
Design and deploy a cloud-based data pipeline using AWS/GCP/Azure services for ingestion, storage, and analytics.
Data Lake Implementation
Build a data lake architecture to store raw and processed data, enabling flexible data access for analytics and machine learning.
Key Features
Practical Training
Global Certifications
Flexible Timing
Trainer Support
Study Material
Placement Support
Mock Interviews
Resume Building
Batch Schedule
Weekdays Regular (Class 1Hr - 1:30Hrs) / Per Session
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13 - Oct - 2025 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
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15 - Oct - 2025 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)
Weekend Regular (Class 3Hrs) / Per Session
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18 - Oct - 2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session
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19 - Oct - 2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)
Enquiry Form
- Learning strategies that are appropriate and tailored to your company's requirements.
- Live projects guided by instructors are a characteristic of the virtual learning environment.
- The curriculum includes of full-day lectures, practical exercises, and case studies.
Data Engineering Training Overview
What goals are achieved in Data Engineering Course in Bangalore?
Data Engineering training in Bangalore aims to equip learners with the skills to design, build, and maintain scalable data pipelines and architectures. The course focuses on mastering data ingestion, processing, storage, and integration techniques, enabling professionals to handle large volumes of data efficiently. Trainees gain expertise in various tools and frameworks that support real-time and batch data workflows, empowering them to contribute effectively in data-driven organizations.
Future Works for the Data Engineering Training Institute in Bangalore
- Cloud Data Engineering: Training on cloud platforms like AWS, Azure, and Google Cloud to manage and deploy data infrastructure flexibly.
- Real-time Data Processing: Emphasis on stream processing frameworks such as Apache Kafka and Apache Flink for instant data insights.
- Data Security and Compliance: Focusing on securing data pipelines and ensuring compliance with regulations like GDPR and HIPAA.
- Machine Learning Pipelines: Integrating ML model deployment with data engineering workflows for enhanced predictive analytics.
- Automation and Orchestration: Using tools like Apache Airflow for automating complex data workflows and scheduling tasks efficiently.
What new >Data Engineering Certification in Bangalore Frameworks are there?
Recent Data Engineering Course in Bangalore have incorporated frameworks that reflect industry advancements and emerging technologies. Modern curricula focus on cloud-native architectures with tools such as AWS Glue and Google Dataflow, supporting serverless data processing. There’s also a strong emphasis on containerization using Docker and orchestration with Kubernetes for scalable deployments. Courses increasingly integrate DevOps principles for continuous integration and delivery of data pipelines, making the frameworks robust and adaptable.
Trends and Data Engineering Placement in Bangalore
- Increased Demand for Cloud Skills: Employers prioritize candidates skilled in cloud data platforms for flexible and scalable infrastructure.
- Focus on Big Data Ecosystem: Knowledge of Hadoop, Spark, and Kafka is critical for handling large datasets effectively.
- DataOps Adoption: Training includes best practices in DataOps to improve collaboration and pipeline reliability.
- Growing Startup Ecosystem: Bangalore’s startups are creating new opportunities for data engineers with innovative projects.
- Emphasis on Hands-on Projects: Placement programs focus on real-world assignments that prepare candidates for job requirements.
Data Engineering Certification in Bangalore Uses
Data Engineering Placement in Bangalore serves as the foundation for managing complex data systems that power analytics, business intelligence, and machine learning initiatives. It enables organizations to efficiently collect, process, and store vast amounts of data from diverse sources. Data Engineering Training Institute in Bangalore empowers professionals to build reliable data pipelines that support timely decision-making and strategic planning. Moreover, data engineers trained in modern tools contribute to automation, scalability, and security of data infrastructure, making them vital assets in data-centric businesses.
Career Opportunities After Data Engineering
Supply Chain Analyst
Analyzes supply chain data to identify inefficiencies and improve processes. Develops reports and forecasts demand to optimize inventory and reduce costs.
Big Data Engineer
Develops and manages big data solutions using technologies like Hadoop and Spark to process large volumes of data efficiently. Optimizes performance and ensures reliability.
ETL Developer
Creates Extract, Transform, Load (ETL) processes to move data between systems and transform it into usable formats. Ensures data accuracy, consistency, and integrity platforms.
Data Architect
Designs data models and database structures that support business needs and data strategy across the organization. Establishes data standards and governance to align with business goals.
Cloud Data Engineer
Builds and manages data infrastructure on cloud platforms like AWS, Azure, or Google Cloud, focusing on scalable, secure data storage and processing. Ensures cost-efficiency and scalability in data solutions.
Data Engineer
Designs, builds, and maintains scalable data pipelines and architectures. Ensures data is accessible, clean, and ready for analysis and reporting. Plays a key role in enabling decision making teams.
Skill to Master
SQL and NoSQL databases
Data warehousing concepts
ETL and ELT processes
Python and/or Scala programming
Apache Hadoop ecosystem
Apache Spark
Data modeling and schema design
Cloud platforms
Real-time data processing
Workflow orchestration
Data pipeline automation
Data governance and security
Tools to Master
Apache Hadoop
Apache Spark
Apache Kafka
Apache Airflow
Talend
Informatica
Google Cloud Dataflow
Microsoft Azure Data Factory
Snowflake
PostgreSQL
MongoDB
Learn from certified professionals who are currently working.
Training by
Sowmiya, having 8+ yrs of experience
Specialized in: Big data architecture and cloud-based data pipelines .
Note: Sowmiya has Emphasis on hands-on projects using Apache Spark and real-time streaming with Kafka
We are proud to have participated in more than 40,000 career transfers globally.
Data Engineering Certification
Yes, there are multiple Data Engineering certification Course offered by various organizations and tech companies. These certifications focus on different platforms, tools, and skill levels. Examples include Google Cloud Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate, and AWS Certified Data Analytics – Specialty.
Definitely! Pursuing multiple Data Engineering certification Training can broaden your expertise across different tools, platforms, and methodologies. It allows you to adapt to diverse job requirements and increases your versatility in the job market
Yes, many Data Engineering certification Training exams are now offered online, enabling candidates to take them remotely. These exams are typically proctored through secure online systems to maintain integrity.
While having real-world experience can significantly improve your chances of securing a Data Engineering certification Course, it is not always mandatory. Many entry-level positions and internship opportunities are available for fresh graduates or certified professionals without extensive work experience.
Frequently Asked Questions
- Visit the official website of the training provider and look for options like Book a Demo or Schedule a Free Session to register directly.
- Contact the support or admissions team via phone, email, or chat to request access to an upcoming demo session.
- Instructors typically have expertise in big data technologies, cloud platforms, and data pipeline design, gained through real-world implementation in diverse industries.
- They often hold professional certifications from providers like Google Cloud, AWS, or Microsoft, reinforcing their technical credibility and teaching quality
- Yes, Most quality training programs offer placement assistance, including job referrals, mock interviews, and help with portfolio building.
- Yes, certifications from providers like Google Cloud Professional Data Engineer, AWS Data Analytics, or Microsoft Azure Data Engineer are well-recognized in the industry.
- Yes, training includes hands-on labs with tools like Apache Spark, Hadoop, and cloud services, plus projects based on real-world data engineering challenges.