To understand the fundamental principles and responsibilities of a data engineer in managing large-scale data systems. 2. To design a scalable and efficient data pipeline that ingests, processes, and stores data from diverse sources. 3. To implement data extraction, transformation, and loading (ETL) processes using modern tools and frameworks such as Apache Spark, Kafka, and Hadoop. 4. To ensure data quality, integrity, and consistency throughout the pipeline by integrating validation and error-handling mechanisms. 5. To explore data storage solutions including relational databases, NoSQL databases, and data lakes to optimize query performance and storage efficiency. 6. To develop skills in automating workflows and monitoring pipeline operations to maintain high availability and reliability of data services. 7. To analyze and document best practices for scalable data pipeline development and deployment within cloud environments like AWS or Azure.
Conduct a comprehensive literature review on current data engineering tools, frameworks, and best practices in big data processing. 2. Design a detailed architecture diagram for a scalable data pipeline capable of handling real-time and batch data ingestion. 3. Implement ETL workflows to extract data from multiple sources, transform it using data cleansing and aggregation techniques, and load into chosen storage solutions. 4. Set up and configure necessary infrastructure components on local systems or cloud platforms to support pipeline operations. 5. Develop automation scripts to schedule and monitor the data pipelines, ensuring resilience and fault tolerance. 6. Test the pipeline performance under different data loads and document the findings with metrics such as throughput, latency, and resource utilization. 7. Prepare a final report detailing the design decisions, implementation challenges, testing results, and recommendations for future improvements.
