![]() Remember to clone this GitHub repo to a local working folder because you need to reference these artifacts as you walk through the deployment steps in this section. You can find the template and relevant code in the GitHub repo. For the tasks that could not be automated, the post provides detailed instructions along with actual code examples. To make the deployment quick and easy, this post automates much of the deployment steps through the use of an AWS CloudFormation template. Install the Power BI mobile app so you can consume the visuals from your phone.Install and configure the Power BI Desktop.Configure the data warehouse by creating and loading data into Amazon Redshift tables.SSH into an Amazon EC2 Linux instance to generate a sample dataset.Run an AWS CloudFormation template to provision the initial development environment.To create this environment, execute the following high-level tasks: After completing all the deployment steps in this section, you have an AWS infrastructure with all the integration hooks between AWS and Power BI fully configured. This section contains instructions to create and configure that environment from scratch. Power BI tenant – You can test all the described Power BI functionalities with minimum to no cost with the following:Ĭreating and configuring your development environmentīefore you can create Power BI visualizations in AWS, you need to load a fully working development environment with sample data. ![]() For more information, see Amazon EC2 Key Pairs. Create a key pair for the selected Region.If you are creating your account for the first time, choose the us-east-1 region.AWS account – You need an account to follow the instructions and test it with minimal cost.To complete the steps in this post, you need the following prerequisites: IAM user and roles with permissions to access Amazon S3 and Amazon Redshift.An Amazon Redshift cluster deployed in a private subnet.Windows Server EC2 instance to act as an on-premises data gateway that handles the communication between Power BI and Amazon Redshift.Windows Server EC2 instance to host Power BI Desktop.Linux EC2 instance provisioned in a public subnet to generate sample data.Networking infrastructure that includes VPC, public and private subnets, security groups, internet gateway, NAT Gateway, and route tables.When deployed, the solution contains the following components: All components inside the AWS Cloud boundary are deployed automatically using an AWS CloudFormation template to allow you to reproduce this solution quickly using your AWS account. The following diagram shows the solution architecture deployed to AWS. For example, how do you connect Power BI to AWS services using ODBC/JDBC drivers? How do you connect to AWS services that are deployed behind a private network? What credentials do you use to connect to AWS services? This post addresses and answers these questions in the subsequent sections. The post also demonstrates how to configure integration for the most common deployment scenarios. You can automatically provision a new Amazon Redshift data warehouse in under an hour without much technical depth required by using the AWS CloudFormation template and code examples provided. ![]() This post provides code artifacts to help you create a big data environment on AWS from scratch. For a more integrated experience, AWS offers Amazon QuickSight – a fully managed BI service with secure private VPC connectivity, native ML-insights and pay-per-session pricing to deliver insights to everyone in the organization. ![]() This post demonstrates how to integrate Power BI with Amazon Redshift to deliver powerful visualization and insights. With Power BI, you can perform ad-hoc query analysis, visualize data, and create-user friendly dashboards. Microsoft Power BI is a business analytics service that delivers insights to enable fast, informed decisions. July 2022: This post was reviewed for accuracy.Īmazon Redshift is a fast, fully managed, cloud-native data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. ![]()
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