Analytics

Cross-cloud analytics are critical to breaking down data silos. Moving data with usability, security, low-latency, and audibility makes it a reality.

When doing long-term planning for an organization, it is really helpful to start with a statement of what the architecture is and what that architecture will encompass. The architectural definition of the technology that will be used will serve as a long-term guide for making technical decisions. In addition, a properly built architecture serves as an instrument of focus, direction, and prioritization for the organization.

The data lakehouse is a promising new technology that combines aspects of data warehouses and data lakes.

When you’re racing around a circuit at over 200mph, there isn’t much room for error. There's a team of thousands backing you, millions of dollars on the line and a grid of other teams vying for the podium. In motorsport, every millisecond counts – and that’s where data can make the difference. This is what I spoke to Joe DosSantos about in the latest episode of Data Brilliant.

Zeotap is a next-generation Customer Data Platform hosted in Google Cloud aimed at building customized Audience segments and activating at scale.

Use new BigQuery geospatial functions ST_IsClosed and ST_IsRing

Learn how Fivetran solution architects are supporting customers with implementing robust and scalable data platforms.

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. For analytic applications to properly leverage a hybrid, multi-cloud ecosystem to support modern data architectures, data observability has become even more important. I spoke to Mark Ramsey of Ramsey International (RI) to dive deeper into that last subject.

When setting up a modern data stack, data warehouse modeling is often the very first step. It is important to create an architecture that supports the data models that you wish to build. I often see people going straight to writing complex transformations before thinking about how they want to organize the databases, schemas, and tables within their warehouse. To succeed, it is key to design your data warehouse with your models in mind before starting the modeling process.