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...

Over the past decade, we've seen more and more companies moving to the cloud. This trend is complemented by the growing power of a large community of data consumers who collect, ingest, store, and analyze data to gain business insights that inform decision making. Of course, as cloud migration...

What is data orchestration? Why do we need it? And how should your company go about it? Let's explore the best tools and practices for data orchestration.

Ready to set up your Spring Boot Application with Docker? This guide covers everything from how Docker works to how you can install and use it with Spring Boot.

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. But there is another critical concept we need to understand in order to work with these architectures: Data Pipelines. 

Nearly every business is facing new challenges in how to store and effectively utilize data. Today we'll look at how data lakes solve these challenges and why GCP is the available, scalable, cost-efficient data lake hosting solution your business needs.

The concept of Data Warehouses (DWs) has been around since the late 80s, and it is still a critical component for any company that wants to adopt a data-driven culture. Here's why Google Cloud Platform's BigQuery might be the right choice to host your data warehouse.