Understand the fundamental principles and key responsibilities of a Data Engineer in designing, implementing, and maintaining data infrastructure.
Develop proficiency in building scalable, efficient, and reliable data pipelines that handle large volumes of structured and unstructured data.
Gain hands-on experience with ETL (Extract, Transform, Load) processes, data ingestion, cleansing, and transformation techniques.
Learn to utilize modern big data technologies such as Apache Spark, Kafka, Hadoop, and cloud-based data storage platforms.
Explore data modeling concepts and schema design optimized for analytical workflows.
Enhance knowledge of automation, orchestration tools like Apache Airflow, and monitoring strategies to ensure data pipeline reliability and fault tolerance.
Understand security best practices and compliance considerations in managing sensitive data within engineering workflows.
Research and analyze the role and responsibilities of a Data Engineer in contemporary data-driven organizations.
Design and implement an end-to-end scalable data pipeline that ingests data from different sources, performs necessary transformations, and loads it into a data warehouse or data lake.
Utilize tools such as Apache Spark for processing large datasets and frameworks like Apache Kafka for real-time data streaming.
Implement data validation, quality checks, and error handling mechanisms within the pipeline.
Deploy the pipeline on a cloud platform (e.g., AWS, Google Cloud, or Azure) to demonstrate scalability and robustness.
Document the pipeline architecture, technology stack, and the rationale behind design choices.
Present findings in a comprehensive report, including challenges faced, solutions implemented, and recommendations for future improvements.
