Understanding Redshift ML: 4 Important Aspects

Amit Kulkarni • Last Modified: December 29th, 2022

With the ever-increasing size and complexity of data, creating Machine Learning (ML) models has become a strenuous task for companies. However, Amazon Redshift ML tackles this problem by availing Structured Query Language (SQL) functions for creating and leveraging ML models in the Amazon Redshift Data Warehouse. Redshift ML, a feature of Redshift Data Warehouse, simplifies the process for obtaining ML-based insights to create reports, dashboards for making better business decisions.

This article gives an overview of Amazon Redshift ML processes. It explains the architecture and benefits possessed by Redshift ML. It also describes Data Modelling, query, and a method to integrate analytical tools using Redshift ML.

Table of Contents


  • Understanding of SQL
  • Understanding of Data Warehouse

What is Redshift ML

Redshift ML - logo
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In order to solve business problems, organizations use ML techniques like supervised, unsupervised, and reinforcement learning. But to implement these techniques requires an understanding of ever-evolving tools and technologies to gain ML-based insights. However, Amazon Redshift ML enables Data Analysts or decision-makers to seamlessly create, train and deploy ML models using familiar SQL commands. To create an ML model, users need to write a ‘create model’ command and pass the necessary subset of data available in Redshift.

As Amazon Redshift ML receives a ‘Create Model’ SQL command, it securely exports data from Amazon Redshift to Amazon Simple Storage Service (S3 bucket). Simultaneously, it also calls Amazon SageMaker Autopilot to prepare data (data preprocessing and feature engineering) and trains the ML model. Once the model is trained, Amazon SageMaker Neo is used to optimize the model for deployment and avail it as an SQL function in Redshift that can be used for garnering predictive insights into business-critical data.

In other words, Redshift ML communicates with various cloud-based services like S3 bucket, SageMaker, and Redshift under the hood to simplify model development with SQL queries.

Key Benefits of Redshift ML

Redshift ML handles interactions between Amazon Redshift, S3, and SageMaker while including all the steps involved during training and compilation of ML models. Below are a few benefits of Redshift ML:

1) Beginner Friendly

Redshift ML allows users (beginners) to become more productive using SQL — a basic programming language. It securely integrates with all necessary Amazon services like Redshift and SageMaker, making it easy to use predictions generated by ML models. It also eliminates the need to maintain a separate summary of models along with secured end-to-end encryption of training data.

2) SQL for ML

Redshift ML automatically handles the required steps, including preprocessing and optimization, to train and deploy a model using SQL functions. If a user queries the ‘CREATE MODEL SQL’ command specifying training data either as a table or SELECT statement, Redshift ML compiles and imports the trained model inside the Redshift Data Warehouse. This process creates a SQL inference function that can be immediately used for SQL queries.

3) Data Analytics

With Redshift ML, analysts can now embed predictions in dashboards with simple SQL queries in Redshift. This allows companies to ameliorate their existing descriptive analytics on dashboards with in-depth insights from ML models.

4) Custom Models

Redshift ML can be used to not only create models from the data present in the Redshift but also external data from S3 buckets. Bring your own model (BYOM) assists users in creating custom models and utilizing them within Redshift.

For further information on Redshift, check out the official website here.

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Understanding Architecture of Redshift ML

Since Machine Learning technology is continuously involving and making things complex, organizations have to rely on ML experts to obtain predictive insights. However, Redshift ML is available for general usage to democratize the use of ML in Reports and Dashboards. To understand the impact of Redshift ML, you need to understand the architecture of ML project workflow as explained below:

1) Traditional Method

Redshift ML - traditional method
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Traditionally, data analysts use SQL to query data from databases and analyze data using BI tools. But to gain in-depth and accurate insights, organizations utilize strenuous ML techniques, leading to the requirement of various professionals for different types of analysis.

While some analysts are familiar with SQL for simple analytics, others work with SageMaker or are required to learn a programming language (Python or R) to build, train, and deploy ML models for unearthing insights into data. Even if a model is deployed, predictions of a model would require test data, which would require moving data back and forth between Redshift and SageMaker. This process involves series of manual and complicated steps as mentioned below:

  • Export training data from Amazon Redshift to Amazon Simple Storage Service (Amazon S3 bucket).
  • Train the model in Amazon SageMaker using data from an S3 bucket.
  • Export test data from Amazon Redshift to Amazon S3 for making predictions.
  • Run the trained model with predicted inputs using Amazon SageMaker for evaluating accuracy.
  • Import predicted columns back to Amazon Redshift.

2) The Alternative Method

Redshift ML - alternative method a
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The above traditional iterative process is time-consuming and prone to errors. Also, automating data movement can consume long hours of custom coding, which would later need to be maintained.

Amazon Redshift ML solves the core complexity of the ML domain, as it leverages the ability of database programming language to not only train and build ML models but also unify the same query language for analyzing data.

Redshift ML - alternative method b
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From the above image, you can see that Redshift ML handles users’ requirements by acting as a mediator for various Amazon services in the following ways:- 

  • Simple SQL commands to create and train and deploy ML models that eliminate dependencies for other programming languages.
  • Automates preprocessing of data to create, train, and deploy models.
  • Enables ML experts to select algorithms, hyperparameters, and preprocessors of their choice.
  • Users have to pay only for training, as prediction is included with the costs of Amazon Redshift clusters.

Understanding Redshift Data Modelling

As Redshift is a Data Warehouse, Data Modelling plays an essential role in designing the schemas to provide detailed and summarized information about the different features. Prior planning of a database with certain key tables can heavily influence overall query performance because they significantly optimize storage requirements and minimize the memory allocated to process queries.

1) What is Data Modelling

The main goal for Data Modelling is to make relationships among data through a well-designed schema and effectively structure schemas to not only decrease the cost of implementing a warehouse but also improve efficiency.

2) Data Modelling for Redshift ML models

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To begin Data Modelling in Redshift ML, a user requires permission to access resources allocated to manage interactions with S3 and Sagemaker. Create AWS identification and access management (IAM) role and use RedshiftML as the role name. Using the Amazon Redshift console, create a cluster and associate a RedshiftML IAM role with loading a dataset in an S3 bucket.

Understanding Redshift Query

With added functionalities to manage large datasets for high-performance analysis and reporting, Redshift is built considering the industry-standard SQL to interact with data and objects in the system. As model training and predictions can be executed using SQL queries, let’s understand the Redshift Query method:

1) Query Editor

Redshift ML - query editor
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The query editor is the simplest way to run queries on databases hosted by an Amazon Redshift cluster using the Redshift console. To access the query editor, a user needs permission to use the Redshift console. To enable access, follow the below steps :

  • Attach the AmazonRedshiftQueryEditor and AmazonRedshiftReadOnlyAccess AWS-managed policies for IAM permissions to the IAM user.
  • Select ‘Run’ to process the queries written in the query editor and choose ‘Execution’ to view run details.
  • Select desired data and export it to download the query results as a file.

2) Scheduling a Query

Using the Redshift console or AWS command-line interface (AWS CLI), users can schedule SQL queries and run routine maintenance. Scheduling of queries is carried out because the results of SQL statements may take almost 24 hours. As a result, most SQL queries are scheduled to run during non-business hours.

In order to create a schedule for SQL statements, choose ‘Schedule’ in the query editor. Assuming the necessary permission is provided, write a single SQL statement and name of the schedule mentioning the scheduled frequency.

Understanding Redshift Data Analytics

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Data stored in Redshift can help companies derive valuable insights by visualizing data. To facilitate analytics, Amazon offers Redshift Data API for enterprises to get insights using high-end Data Visualization tools like Power BI and Tableau. It assists analysts in making better conclusions of ever-increasing data by visualizing real-time data on a dashboard interface.


As organizations decide their new venture by forecasting data stored in Data Warehouses, AWS gives an end-to-end ML solution using Redshift ML. Amazon Redshift ML plays a vital role in simplifying the process of ML projects, which has become essential to make reliable business decisions. In case you want to export data into your desired Redshift Data Warehouse, then Hevo Data is the right choice for you! 

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