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Our Data Engineering Course in Pune covers Python, SQL, Data Warehousing, ETL Development, Apache Spark, Databricks, Apache Airflow, Kafka, Azure Data Factory, AWS Data Services, Snowflake, Data Lakehouse Architecture, Real-Time Data Pipelines, and Generative AI applications in data engineering. The course includes hands-on projects and industry use cases.

Yes. Our program is designed for both freshers and working professionals. Beginners start with Python, SQL, and database fundamentals before progressing to advanced cloud and big data technologies.

Absolutely. A Computer Science degree is not mandatory to become a Data Engineer. Employers focus more on practical skills, project experience, cloud knowledge, and certifications than academic background.

The course primarily covers Python and SQL, which are the most widely used programming languages in modern data engineering. You will also work with PySpark for large-scale data processing.

Yes. The curriculum includes Databricks, Azure Data Factory, Azure Synapse Analytics, AWS Data Services, cloud storage solutions, and modern lakehouse architectures used by leading organizations.

Yes. Our Data Engineering Course includes multiple end-to-end industry projects designed to simulate real-world data engineering environments. Students build scalable ETL and ELT pipelines, cloud-based data platforms, data lakes, real-time streaming solutions, and production-ready data architectures using modern industry tools. As part of the project work, learners gain hands-on experience with Agile and Waterfall project methodologies, enabling them to understand how data engineering teams collaborate and deliver solutions in enterprise environments. The curriculum also includes advanced implementation of Medallion Architecture using Delta Lake for batch processing and real-time data ingestion. Students work on projects involving Bronze, Silver, and Gold data layers, Event Hub integration, Stream Analytics, real-time data processing, Databricks, Azure Data Factory, and cloud-native data engineering workflows.

The duration typically ranges from 4 to 5 months depending on the learning path, batch schedule, and whether you choose weekday or weekend sessions.

Yes. We offer weekend batches and evening sessions specifically designed for working professionals. Session recordings are also provided for revision and self-paced learning.

Yes. We provide resume preparation, LinkedIn profile optimization, mock interviews, technical interview preparation, career mentoring, and placement assistance to help students secure Data Engineering roles.

Salaries vary based on experience and location. Freshers typically start with competitive packages, while experienced professionals transitioning into Data Engineering can achieve significant salary growth after acquiring in-demand cloud and big data skills.

Yes. The curriculum is aligned with industry certification requirements and helps learners prepare for Azure Data Engineer Associate, AWS Data Analytics, and other cloud data engineering certification exams.

The roadmap starts with programming fundamentals, moves to databases and SQL, then covers big data tools like Spark and Kafka, followed by cloud platforms, data pipeline orchestration with Airflow, and finally Gen AI integration.

You'll learn to integrate Large Language Models (LLMs), build RAG (Retrieval-Augmented Generation) pipelines, work with vector databases like Pinecone and ChromaDB, and deploy Gen AI-powered data applications.