In this course, you will learn about the raw ingredients and processes that are used to physically store data on disk and in memory. You’ll explore different storage systems, including object, block, and file storage, as well as databases, that are built on top of these raw ingredients. You’ll also get a chance to use the Cypher language 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 will 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.
Data Storage and Queries
This course is part of DeepLearning.AI Data Engineering Professional Certificate
Instructor: Joe Reis
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What you'll learn
Design storage architectures for various use cases, and select appropriate technologies to implement these architectures
Practice common query patters and identify ways to improve query performance and enhance the value of your data systems
Skills you'll gain
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September 2024
3 assignments
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There are 3 modules in this course
What's included
16 videos11 readings1 assignment2 app items
What's included
16 videos2 readings1 assignment2 app items
What's included
15 videos4 readings1 assignment1 programming assignment2 app items
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Reviewed on Nov 18, 2024
Insightful overview of a simple yet complicated concept such as storage and queries.
Reviewed on Nov 19, 2024
Clearly structured and all concepts are well explained by the instructor.
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