To understand the architectural principles and best practices involved in building scalable and efficient data engineering pipelines that process large volumes of data. 2. To explore the use of modern data storage solutions including distributed file systems and cloud data warehouses. 3. To develop proficiency in extracting, transforming, and loading (ETL) processes to ensure data quality, integrity, and usability. 4. To analyze methods for data ingestion from multiple sources including streaming and batch data. 5. To implement monitoring and optimization techniques aimed at improving pipeline performance and resource management. 6. To gain practical experience with relevant tools and technologies such as Apache Spark, Kafka, Airflow, and cloud platforms like AWS or Azure within the data engineering domain. 7. To develop an understanding of data governance, security, and compliance standards relevant to data engineering projects. 8. To apply these concepts in a comprehensive project that simulates real-world data engineering challenges.
Conduct a thorough literature review on current data engineering pipeline architectures and technologies focusing on scalability and efficiency. 2. Design a data ingestion framework that can handle both streaming and batch data from varied sources, ensuring robustness and fault tolerance. 3. Develop an ETL pipeline implementing data cleaning, transformation, and enrichment processes using tools such as Apache Spark or similar. 4. Integrate cloud-based data storage solutions to facilitate scalable and reliable data access and processing. 5. Implement workflow orchestration using Apache Airflow or equivalent to automate and monitor pipeline tasks. 6. Perform performance benchmarking of the pipeline, identify bottlenecks, and optimize resource utilization accordingly. 7. Address data security and compliance by incorporating access controls and encryption where necessary. 8. Prepare comprehensive documentation and present a final report detailing the design decisions, implementation challenges, and project outcomes.
