To design and implement a scalable enterprise data lake that ingests structured and unstructured data using AWS S3 and Apache Spark. The system enables large-scale data storage, transformation, and analytics while ensuring governance, security, and optimized query performance.
Study data lake architecture and medallion (bronze-silver-gold) layers.
Configure AWS S3 buckets for raw and processed data.
Ingest structured and semi-structured datasets into S3.
Implement Spark jobs for transformation and cleansing.
Partition and optimize datasets for efficient querying.
Apply schema enforcement and data validation checks.
Implement metadata cataloging using AWS Glue Data Catalog.
Integrate Athena or Redshift Spectrum for querying data lake.
Apply IAM roles for secure data access.
Optimize Spark job performance and resource allocation.
Monitor data ingestion workflows.
Implement data lifecycle management and retention policies.
Benchmark performance with increasing data volumes.
Document architecture diagrams and governance framework.
