Explore the process of constructing a cloud data lake using Databricks and AWS in this informative 29-minute video. Learn about the advantages of data lakes for data science and analytics, focusing on Amazon S3's secure and scalable object storage. Discover how Delta Lake addresses reliability and performance challenges in data lakes, adding database-like features such as transactions. Gain insights into best practices for cloud data lake implementation, including integrations with AWS services like Glue and Redshift. Understand the importance of operationalizing data lakes and how Databricks provides a unified data analytics platform to accelerate innovation. Through presentations, benchmarks, and code examples, acquire valuable knowledge about building efficient and effective cloud data lakes for your organization.
Building a Cloud Data Lake with Databricks and AWS - Best Practices and Implementation
Databricks via YouTube
Overview
Syllabus
Intro
What is a data lake?
A data lake architecture enables data science
Data lakes and analytics from AWS
Amazon Simple Storage Service (S3) Secure, highly scalable, durable object storage with millisecond latency for data access
Most ways to transfer data into the data lake Open and comprehensive
Most comprehensive and open
Cloud data lakes are great for data storage Data Lake is a file system that supports
Organizations want to operationalize To operationalize data lakes, you need features you expect on a database • Transactions
A new standard for building data lakes
Data reliability challenges with data lakes
Performance challenges with data lakes
Delta Lake: Adds Reliability & Performance
The A DELTA LAKE
Integration with Glue
Integration with Redshift
Cloud native enterprise solution
Best practices for building a cloud data lake
Databricks & AWS data lake implementation
Taught by
Databricks
