Data Engineer (Apache Spark)

About This Course

Data Engineers (Apache Spark) design, build, and optimize scalable data pipelines that process large volumes of structured and unstructured data for analytics, reporting, and machine learning applications. They collaborate with data scientists, analysts, and software engineers to develop efficient data processing solutions using distributed computing technologies. This course prepares learners for a career as a Data Engineer (Apache Spark) through hands-on projects, industry workflows, collaborative learning, and real-world case studies aligned with current hiring expectations in India. Learners gain expertise in Python, SQL, Apache Spark, PySpark, Hadoop ecosystem fundamentals, ETL development, data warehousing, distributed data processing, cloud data platforms, and data pipeline orchestration. The curriculum includes practical experience with industry-standard big data tools and cloud technologies. Through live projects and capstone assignments, participants develop programming, analytical, and data engineering skills, preparing them for Data Engineer, Big Data Engineer, Spark Developer, and Data Platform Engineer roles across diverse industries.

Key Responsibilities

• Design and develop scalable data pipelines using Apache Spark for batch and real-time processing
• Build, optimize, and maintain ETL workflows for large-scale data ingestion and transformation
• Integrate data from multiple sources into data lakes and warehouses
• Monitor data quality, performance, and reliability of distributed data systems
• Collaborate with data scientists, analysts, and engineering teams to enable data-driven decision-making and machine learning initiatives

Growth Path

Tools Used

Perfect For

Computer Science and Engineering Graduates | Software Developers and Backend Engineers | Data Analytics and Big Data Professionals | Cloud and Database Professionals | Machine Learning Aspirants | Individuals Interested in Building Scalable Data Infrastructure

Fee Structure