Data Lake Architecture & Management is a comprehensive course designed to help professionals build, manage, and optimize modern data lakes capable of storing structured, semi-structured, and unstructured data at scale. The course begins with a clear explanation of what a data lake is, how it differs from traditional data warehouses, and why it is a foundational component of modern analytics platforms. Learners explore the key characteristics of a data lake: schema-on-read design, separation of compute and storage, scalability, and support for multiple data formats and sources. The course introduces popular storage platforms such as Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage, and explains how they serve as the foundation for building elastic, cloud-native data lakes. Learners explore data ingestion pipelines using tools like Apache NiFi, AWS Glue, Azure Data Factory, and Kafka to ingest data from transactional systems, APIs, IoT devices, logs, and external feeds. Batch and streaming ingestion strategies are compared to support both historical and real-time analytics use cases. Data is categorized into bronze (raw), silver (cleaned), and gold (curated) layers following the medallion architecture pattern. Students learn how to structure data in formats like Parquet, ORC, Avro, JSON, and CSV to optimize for storage efficiency and query performance. They explore metadata management using AWS Glue Data Catalog, Apache Hive Metastore, and Databricks Unity Catalog to enable data discovery and governance. Best practices in partitioning, compression, and file size optimization are also covered to improve data lake performance. Learners are introduced to lakehouse architecture, which blends the flexibility of data lakes with the reliability and performance of data warehouses. Technologies like Delta Lake, Apache Hudi, and Apache Iceberg are explored for features such as ACID transactions, schema evolution, time travel, and versioning in data lakes. Students learn to use Spark, Presto, Trino, and Athena to query data directly from the lake, enabling scalable and cost-efficient analytics without ETL duplication. The course emphasizes access control, encryption, and data masking techniques for securing sensitive data. Role-based policies and fine-grained access control mechanisms are implemented using services like AWS Lake Formation, Azure Purview, and Ranger. Data governance is addressed through data classification, lineage tracking, and compliance with regulatory frameworks like GDPR, HIPAA, and CCPA. The course includes monitoring and logging practices using cloud-native tools like CloudWatch, Azure Monitor, and GCP Operations Suite, as well as cost optimization techniques to control storage and query expenses. Learners practice building dashboards to monitor data ingestion, processing latency, and consumption patterns. By the end of the course, participants will have implemented a fully functional data lake that supports diverse analytics, machine learning, and business intelligence workloads. They will be equipped to handle large-scale data from ingestion to exploration, with strong security and governance in place. This course is ideal for data engineers, architects, and analytics professionals responsible for building robust, scalable, and secure data platforms in today’s data-driven enterprises.