We recently wrote about the interest we’re seeing in connected applications that are built on Snowflake. Connected applications separate code and data such that the app provider creates and maintains the application code, while their customers manage their own data and provide their data platform for processing the application’s data. Some of our partners choose the connected application model because it has benefits for both customers and application providers.

Following the recent GA of Snowpark for our customers on AWS, we’re happy to announce that Snowpark Scala stored procedures are now available in preview to all customers on all clouds. Snowpark provides a language-integrated way to build and run data pipelines using powerful abstractions like DataFrames. With Snowpark, you write a client-side program to describe the pipeline you want to run, and all of the heavy lifting is pushed right into Snowflake’s elastic compute engine.

This post is going to be a bit of a step back into the past. As Mork from Ork would say: “nanu nanu.”

Here’s one of the most memorable quotes I have heard from a customer here in Asia: “Every time they tell me it’s ‘not in the universe’, I feel like mine is collapsing.”

Digital technology promises transformative results. Yet, it’s not uncommon to encounter potholes and speed bumps along the way. One area that frequently trips up businesses is putting data into action. It can be extraordinarily difficult to take advantage of the right data at exactly the right time — in real time — to drive decision-making. For SAP customers wanting to maximize the value of their data, Google Cloud offers a number of capabilities.

The value of healthy data is obvious. But how do you build that practice in your own business? The difference between people who live a healthy lifestyle and those who don’t isn’t whether they know how to be healthier — it’s whether or not they prioritize diet, sleep, and exercise in their daily life. The same is true for your data: if you don’t have the infrastructure that supports your customer 360 initiatives , those initiatives become moot.

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” classification problem. Figure 01: Is this a chihuahua or a muffin? In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin.

During a Teradata migration to BigQuery, one complex and time consuming process is migrating Teradata users and their permissions to the respective ones in GCP. This mapping process requires admin and security teams to manually analyze, compare, and match hundreds to thousands of Teradata user permissions to BigQuery IAM permissions. We already described this manual process for some common data access patterns in our earlier blog post.

It’s official — Self-Service Data APIs are available as a new feature of Data Inventory! Highly skilled and less-technical users can now make their datasets available to applications and partners via APIs in just a few clicks. Self-Service Data APIs empower every permissioned employee to leverage data across the business to generate valuable insights or even extend data availability beyond the organization to trusted partners.

Piano is a leading provider of software and services that help organizations better understand their audiences across digital channels. With these insights in hand, Piano customers can intelligently serve audiences with personalized experiences that keep them engaged and drive revenue, all through a single unified platform.