Cloud Masters Episode #126
Amazon S3 Tables explained: Better storage for AWS Analytics workloads
Learn how Amazon S3 Tables enhances data lake performance with built-in Apache Iceberg support.
Cloud Masters Episode #126

With DoiT Spot Scaling, automate your AWS Spot Instances to save up to 90% on compute spend without compromising reliability.

Cloud Masters
Cloud Masters
Amazon S3 Tables explained: Better storage for AWS Analytics workloads
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Cloud Masters
Cloud Masters
Amazon S3 Tables explained: Better storage for AWS Analytics workloads
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Episode notes

Key Moments

00:00: Introduction
00:35: S3 Tables vs. S3 buckets
01:00: Benefits of Parquet file format
01:31: Parquet vs. Apache Iceberg
03:33: How we got to S3 Tables
06:09: Compaction and snapshot management explained
09:46: S3 Table namespaces
11:34: S3 Tables cost and performance benefits
13:27: EMR ETL performance with S3 Tables
15:47: Migration considerations from Glue catalog
19:28: S3 Tables security model and access controls
20:27: Getting started with S3 Tables
22:34: Future outlook

About the guests

Swapnil Bhoite
Swapnil is an AWS Big Data Engineer and Amazon Glue SME at DoiT, helping companies architect their data solutions on AWS. Prior to DoiT, he worked at AWS to assist and onboard enterprise customers with the AWS data services like EMR, Redshift, OpenSearch, debugging the issues in their solution and optimizing their end-to-end data solutions with speciality on ETL/ELT pipelines.
Swapnil is an AWS Big Data Engineer and Amazon Glue SME at DoiT, helping companies architect their data solutions on AWS. Prior to DoiT, he worked at AWS to assist and onboard enterprise customers with the AWS data services like EMR, Redshift, OpenSearch, debugging the issues in their solution and optimizing their end-to-end data solutions with speciality on ETL/ELT pipelines.

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