You're reading from Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781804619285

Length 290 pages

Edition 1st Edition

Languages

Python

Tools

Amazon Redshift

Concepts

Machine Learning

Authors (4):

Phil Bates

Sumeet Joshi

Debu Panda

Bhanu Pittampally

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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 FREE CHAPTER

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

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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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You're reading from Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Table of Contents (19) Chapters

Data loading from Amazon S3 using the COPY command

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Authors (4)

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