Analytics

In this video, we'll look at some techniques for evolving events by analyzing a specific use case in a banking fraud detection system.

Nick Jewell, Senior Principal Product Marketing Manager at Alation, demonstrates how to use the Alation Data Intelligence Platform to easily find, understand, and use trusted data.The demo focuses on how data engineers, data governors, and data users can leverage Alation and Snowflake Horizon to more efficiently ingest, monitor, and share governed data.

The latest update to the App Analyzer brings a new feature: session-level data. Now, the App Analyzer can answer vital questions, such as which users are accessing which applications, how long they stay in each app, what sheets they use, the duration on each sheet, and the frequency of navigation between sheets. It also tracks the number of concurrent users within an app or across all apps in the tenant.

Learn how Power ON can revolutionize the way you interact with and train AI models: https://bit.ly/49iwwnC

When developing or debugging a stream processing pipeline with Flink SQL, it’s common to inspect each processing step's output to ensure data is being transformed properly. However, comprehending the resulting data stream's structure, distribution, and characteristics entails executing multiple ad-hoc SQL queries, which can be time-consuming and tedious. Additionally, isolating specific subsets of the stream for analysis or debugging often involves even more queries, adding to the complexity and time required.

Data products are highly trusted, reusable, and consumable data assets. They’re curated collections of high-quality datasets and business-approved metadata, designed to solve domain-specific business outcomes. They foster effective communication and accountability. Our Quality and Profiling Overview provides detailed quality metrics at both the data product and dataset levels.