In this 'Back to Basics' video, Orit Alul explores the fundamentals of data lake and lake house patterns. She discusses the exponential growth of data from various sources and the need for efficient, cost-effective analysis. Orit covers key concepts including data ingestion using Amazon Kinesis Data Firehose, data format optimization, partitioning, and compaction. She explains how to use AWS services like Glue, Athena, QuickSight, and Redshift to build a lake house architecture that combines the flexibility of a data lake with the performance of purpose-built analytics services. This comprehensive overview provides insights into managing and analyzing large-scale data effectively in the cloud.