In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way.

Gift guides come in all shapes and sizes. There are shopper’s guides for sporting goods and wine, aimed at travelers and crafty types, and offering electronics or candy. Since there is no gift guide we’re aware of for data buyers, this is our chance to create the first such guide. Is your wife, best friend, or dad a nerd? No, not that kind of nerd, not an over-the-counter nerd, a data nerd! If so, this stuff will stuff their stocking but good. Remember Sears’ Wish Book?

Machine learning (ML) model serving refers to the series of steps that allow you to create a service out of a trained model that a system can then ping to receive a relevant prediction output for an end user. These steps typically involve required pre-processing of the input, a prediction request to the model, and relevant post-processing of the model output to apply business logic.

This week, ThoughtSpot gathered virtually with thousands of global customers, partners, and friends to share our vision for the future of analytics at Beyond 2021. A future where everyone in your business can create personalized insights and operationalize them to drive smarter business actions. And where innovative brands like Snowflake, Starbucks, Just Eat Takeaway, and Opendoor are already building their businesses on data with the Modern Analytics Cloud.