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Serverless Machine Learning with Amazon Redshift ML

You're reading from Serverless Machine Learning with Amazon Redshift ML

Product type Book

Published in Aug 2023

Publisher Packt

ISBN-13 9781804619285

Pages 290 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (4):

Debu Panda

Phil Bates

Bhanu Pittampally

Sumeet Joshi

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning

2. Chapter 1: Introduction to Amazon Redshift Serverless

3. Chapter 2: Data Loading and Analytics on Redshift Serverless

4. Chapter 3: Applying Machine Learning in Your Data Warehouse

5. Part 2:Getting Started with Redshift ML

6. Chapter 4: Leveraging Amazon Redshift ML

7. Chapter 5: Building Your First Machine Learning Model

8. Chapter 6: Building Classification Models

9. Chapter 7: Building Regression Models

10. Chapter 8: Building Unsupervised Models with K-Means Clustering

11. Part 3:Deploying Models with Redshift ML

12. Chapter 9: Deep Learning with Redshift ML

13. Chapter 10: Creating a Custom ML Model with XGBoost

14. Chapter 11: Bringing Your Own Models for Database Inference

15. Chapter 12: Time-Series Forecasting in Your Data Warehouse

16. Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models

17. Index

Why subscribe?

18. Other Books You May Enjoy

Data loading from Amazon S3 using the COPY command

Data warehouses are typically designed to ingest and store huge volumes of data, and one of the key aspects of any analytical process is to ingest such huge volumes in the most efficient way. Loading such huge data can take a long time as well as consume a lot of compute resources. As pointed out earlier, there are several ways to load data in your Redshift Serverless data warehouse, and one of the fastest and most scalable methods is the COPY command.

The COPY command loads your data in parallel from files, taking advantage of Redshift’s massively parallel processing (MPP) architecture. It can load data from Amazon S3, Amazon EMR, Amazon DynamoDB, or text files on remote hosts (SSH). It is the most efficient way to load a table in your Redshift data warehouse. With proper IAM policies, you can securely control who can access and load data in your database.

In the earlier section, we saw how Query Editor v2 generates the...

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Authors (4)

Debu Panda

Debu Panda, a Senior Manager, Product Management at AWS, is an industry leader in analytics, application platform, and database technologies, and has more than 25 years of experience in the IT world. Debu has published numerous articles on analytics, enterprise Java, and databases and has presented at multiple conferences such as re:Invent, Oracle Open World, and Java One. He is lead author of the EJB 3 in Action (Manning Publications 2007, 2014) and Middleware Management (Packt, 2009).

See other products by Debu Panda

Phil Bates

Phil Bates is a Senior Analytics Specialist Solutions Architect at AWS. He has more than 25 years of experience implementing large-scale data warehouse solutions. He is passionate about helping customers through their cloud journey and leveraging the power of ML within their data warehouse.

See other products by Phil Bates

Bhanu Pittampally

Bhanu Pittampally is Analytics Specialist Solutions Architect at Amazon Web Services. His background is in data and analytics and is in the field for over 16 years. He currently lives in Frisco, TX with his wife Kavitha and daughters Vibha and Medha.

See other products by Bhanu Pittampally

Sumeet Joshi

Sumeet Joshi is an Analytics Specialist Solutions Architect based out of New York. He specializes in building large-scale data warehousing solutions. He has over 17 years of experience in the data warehousing and analytical space.

See other products by Sumeet Joshi

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