Chapter 1: Introduction to Amazon Forecast and Redshift ML

Amazon Forecast seamlessly integrates with Amazon Redshift ML, offering users the ability to perform machine learning-based time-series forecasting through familiar SQL commands. This integration simplifies the forecasting process by eliminating the need for unfamiliar tools or complex data pipelines, enabling users to leverage existing knowledge for predictive analytics.

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Section 1.1: Creating Predictors with SQL

With the latest enhancements, users can create and train forecasting models, known as predictors, directly from time-series data stored in Amazon Redshift using SQL statements. These models can forecast various metrics, including revenue and inventory, and can be seamlessly integrated into queries and reports. Additionally, users can specify probability metrics to refine their forecasts.

Chapter 2: The Broader Impact of Zero ETL Integration

The recent Zero ETL integration is available across multiple regions, including the US, parts of Asia like Korea and Singapore, as well as various locations in Europe. AWS has emerged as a pioneer in this approach, paralleling advancements made by Google and more recently, Snowflake, which has introduced similar functionalities in their Data Warehouse/Lakehouse solutions.

The first video titled "Connect Your Amazon Redshift Data for Analytics and ML - AWS Virtual Workshop" provides insights into effectively connecting Redshift with Amazon Forecast for enhanced analytics and machine learning capabilities.

The second video, "AWS Tutorials - Using Machine Learning with Amazon Redshift," elaborates on the practical applications of machine learning in conjunction with Amazon Redshift, illustrating how to maximize the benefits of this integration.