Containers have emerged as the modern approach to package code in any language to ensure portability and consistency across environments, especially for sophisticated AI/ML models and full-stack data-intensive apps. These types of modern data products frequently deal with massive amounts of proprietary data.
Financial dashboards bring performance into focus by collecting the most important metrics and indicators in one location. But when organizations build their financial dashboards from the ground up, challenges often arise. A primary hurdle is making the right design choices to create dashboards that drive successful business decisions. To overcome this hurdle, it helps to incorporate ideas that have already been implemented, evaluated, and improved on by others.
At Snowflake Summit 2022, we introduced a new way of building apps with the Snowflake Native App Framework. Today, we are excited to bring the power of the Snowflake Native App Framework to developers around the world with the public preview in AWS. Developers can now start building and testing Snowflake Native Apps in their accounts in AWS. Distribution and monetization capabilities will be available in public preview on AWS later this year.
At ThoughtSpot, we know how important it is for businesses of every size and industry to empower every knowledge worker with personalized, actionable data-driven insights. These insights are your secret sauce to making better business decisions, growing faster, and delivering customer experiences that keep people coming back for more. But how do you scale self-service analytics to business users without completely overwhelming your data teams?
Over time, using the wrong tool for the job can wreak havoc on environmental health. Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster.
With any new, transformative technology comes a fear of the unknown, as I explored in my first blog of a new series on generative AI. Companies are pulled between acting quickly to avoid being left behind and proceeding with caution to avoid major missteps.
Over the past few years, software vendors moved their applications to the cloud. Microsoft committed heavily to the cloud across all its product lines, including the Dynamics family of ERP and CRM solutions. That will mean an eventual migration to the cloud for customers of Microsoft Dynamics AX, Microsoft Dynamics CRM, and the legacy Microsoft GP, SL, and NAV products.
In spite of diligent digital transformation efforts, most financial services institutions still support a loose patchwork of siloed systems and repositories. These dis-integrated resources are “data platforms” in name only: in addition to their high maintenance costs, their lack of interoperability with other critical systems makes it difficult to respond to business change.
BI and analytics are both umbrella terms referring to a type of data insight software. Many providers use them interchangeably, but some use them in conjunction, claiming to offer both business intelligence and business analytics. This of course makes us wonder: what’s the difference?
