The world is undergoing a remarkable transformation fueled by data. Organizations have accumulated silos across their data infrastructure to support various workloads, languages, tools, and formats because of technology limitations. These silos can have major consequences in the form of greater operational burden, security vulnerabilities, increased total cost of ownership, incomplete insights, and reduced agility.

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.

Active listening is an admired and sought after skill in both the professional and personal sphere. After all, who doesn’t love to be heard? But what happens when we apply that mindset to the way our organizations solicit feedback and interact with our customers? We don’t have to make any assumptions to answer this question, because we have the data.

Amidst growing concerns around user privacy and regulatory laws, the cookieless paradigm has been gaining momentum over time in digital advertising. In addition, web browsers are increasingly blocking third-party cookies altogether in web sessions, necessitating the need for new authentication methods in web applications. Cookieless authentication is a secure way to verify user identities in web applications without relying on cookies.

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.

Available in beta, we have introduced new version control REST APIs for Git integration in the 9.3.0.cl release of ThoughtSpot Analytics Cloud. ThoughtSpot administrators can now link their instances to a GitHub repository and utilize continuous integration and deployment (CI/CD) best practices to effectively manage their organization's analytic content throughout its lifecycle.

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?

You may already be familiar with dbt, but just to be sure we're on the same page, let's revisit the concepts. The Data Build Tool (dbt) is a widely used framework provided by dbt Labs, specifically designed to bridge the gap between data analysts, data engineers, and software engineers, resulting in the creation of a new role in data and analytics: the analytics engineer.

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.