In our previous posts, we talked about how data warehouses and data lakes can help us manage our data in a more secure, cost-effective, reliable, and scalable way, as well as how to use GCP to improve our data pipelines and their orchestration. But it doesn’t matter how rich or robust your data structure is if you cannot use it to leverage your business decision making.

Spring Cloud Stream is a framework that helps developers with data integration problems, especially in event oriented applications. The goal of the framework is to make it easy to apply modern microservices architecture patterns, decouple application responsibilities, and port business logic onto message brokers.

Over the last few years, machine learning has garnered a great deal of attention both in academic circles and in the marketplace. We now have a huge number of applicable use cases, thanks to the fact that we're living in an era where data is the new coin. 

In the last 2 or 3 years, you may have heard that "data is the new currency." Or perhaps you've read news about the increased usage of artificial intelligence to create self-driving cars, supplement customer support, and improve product recommendation systems based on the last purchased item or the customer's shopping profile.

Interested in developing cloud infrastructure and automating repetitive tasks? This blog is for you. Today, we're going to show you how to get Google Cloud Platform and Terraform to work together. Then we'll create a virtual machine in just a few simple steps.

In this blog, I am going to explain some of the best practices for building a machine learning system in Google Cloud Platform.  We'll start by showing how to understand and formulate the problem and end with tips for training and deploying the model. We'll also discuss required GCP tools, data processing in GCP, and data validation.