Analytics

Transform Self-Service Analytics With Vizlib and Qlik

In today’s digital age, data has evolved from being a mere byproduct of business processes to becoming the cornerstone of strategic decision-making. Yet, for many organizations, unlocking the full potential of their data remains a significant challenge. Traditional data analytics models often create bottlenecks, relying heavily on overextended IT departments to provide insights, which delays decision-making and limits agility.

Supercharge your data storytelling with VitaraCharts on ThoughtSpot

In today’s data-driven world, visualization goes beyond displaying numbers — it’s about empowering users to express meaningful stories from data. Visualization is key to quickly interpreting data, uncovering patterns and trends, and enabling faster, more trusted decisions. At ThoughtSpot, we have always championed making data accessible and actionable for everyone. With our AI-powered analytics, any user can self-serve key business insights within seconds.

Building Reliable AI models on Snowflake

Step into the future of AI with Snowflake and Hevo—where innovation meets reliability. Join us for an exclusive webinar to explore how Snowflake's cloud-native platform is empowering organizations to build reliable, scalable AI models. With cutting-edge advancements like Cortex AI and RAG, Snowflake sets the foundation for AI-driven transformations across industries. Discover how to harness these powerful capabilities to develop of Snowflake that delivers faster, more accurate results for your data-driven projects.

3 Databricks Mosaic AI Use Cases to Supercharge Your Log Analytics Program

Modern organizations generate large amounts of logs from multiple data sources, creating significant challenges when it comes to analyzing the data and extracting useful insights at scale. Data scientists can tackle these challenges with help from Mosaic AI, which helps Databricks users build and deploy artificial intelligence (AI) and machine learning (ML) solutions.

Deep Dive into Handling Consumer Fetch Requests: Kafka Producer and Consumer Internals, Part 4

Recap: This is the last part of our four chapters: It’s been a long time coming, but we’ve finally arrived at the fourth and final installment of our blog series. In this series, we’ve been peeling back the layers of Apache Kafka to get a deeper understanding of how best to interact with the cluster using producer and consumer clients. At a high level, a fetch request is comprised of two parts: Let’s dive in.

Everything you need to know about AI Agents in analytics

A few years ago, I received a call from the Tesla service team advising me not to come for my car service—a savings of $600 and my time. Imagine my surprise. In over two decades of car ownership, no auto service had ever called me and asked me not to show up. The Tesla representative explained that the data from my car prescribed an action, or in this case, an inaction.