In this course, you'll learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different data systems, including object stores, block storage, file systems, and databases, that are built on top of these raw ingredients. You’ll also get a chance to query a Neo4j graph database and perform vector similarity search, a key feature behind generative AI and large language models. You will explore the evolution of data storage abstractions, from data warehouses to data lakes and data lakehouses, while comparing the advantages and drawbacks of each architectural paradigm.
In this course, you'll learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different data systems, including object stores, block storage, file systems, and databases, that are built on top of these raw ingredients. You’ll also get a chance to query a Neo4j graph database and perform vector similarity search, a key feature behind generative AI and large language models. You will explore the evolution of data storage abstractions, from data warehouses to data lakes and data lakehouses, while comparing the advantages and drawbacks of each architectural paradigm.
With hands-on practice, you'll design a simple data lake using Amazon Glue, and build a data lakehouse using AWS LakeFormation and Apache Iceberg. In the last week of this course, you’ll see how queries work behind the scenes, practice writing more advanced SQL queries, compare the query performance in row vs column-oriented storage, and perform streaming queries using Apache Flink.
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