Analytics

CityFibre is one of the U.K’.s biggest fibre networks, connecting millions to high-speed broadband. Piyush Shandilya, Data Architect at CityFibre explains how the company uses Snowflake to process and analyze large, integrated data sets at speed, powering future growth and delivering next-gen connectivity. CityFibre is the U.K.’s third largest gigabit network and is predicted to connect 8 million people by 2025.

There will always be a place for spreadsheets in your business. But viewing your company’s performance data on charts can help you analyze your data faster, compare historic performance, and uncover insights you might’ve missed otherwise. The right visualization also helps your team understand performance better and can illustrate the progress they’re making toward a shared goal. The key is choosing the right visualization for each metric or KPI you want to track.

Continued global digitalization is creating huge quantities of data for modern organizations. To have any hope of generating value from growing data sets, enterprise organizations must turn to the latest technology. You’ve heard of data warehouses, and probable data lakes, but now, the data lakehouse is emerging as the new corporate buzzword. But what is a data lakehouse and how can you make the most of it to transform your Oracle Cloud data for advanced reporting and analytics?

Migrating to and from Amazon Web Services (AWS) is a common but tricky endeavor. Many moving parts and technical and security aspects need to be considered. AWS provides several of its own tools to simplify the migration process, but there are also several third-party products that can support the AWS migration process. This guide will walk you through some of the best options on the market, their pros and cons, and what features may be useful for your particular migration project.

Fivetran, LTIMindtree and Snowflake unveil DecisionsCX, empowering enterprises with Customer 360 for hyper-personalization.

The enterprise app market has been growing faster than ever before, due to the recent spike in demand for AI / ML workloads. These new types of apps operate over large sets of data, have increasingly higher compute demands, require strict data privacy protections, provide very sophisticated web experiences, and need to be secure at all stages of their life cycles. While such apps are being created at a very fast pace, there are two main challenges.