The video tutorial explores the history and evolution of distributed computing, starting with Google's GFS and the open-source HDFS. It contrasts traditional data warehouses with HDFS and MapReduce, highlighting the advantages of horizontal scalability and cost-effectiveness. The concept of Data Lakes, initially synonymous with Hadoop, is introduced, detailing its maturation into a platform with key capabilities like data collection, storage, processing, and access. The tutorial also covers data processing frameworks like Apache Spark and orchestration tools such as Kubernetes, concluding with the importance of data consumption and additional capabilities like security and governance.

What does the term 'Data Lake' refer to in the context of modern data platforms?

What are the responsibilities of the processing layer in a Data Lake?

How does the consumption layer of a Data Lake complicate data access?