The explosive rise of generative AI has prompted incredible excitement about its transformative potential, much like the advent of the Internet. But if like me, you’re old enough to remember what that looked like circa 1995, there was a lot we did not know at the time, creating uncertainty in both worlds of work and education on how to best leverage it, and whether providing unlimited access to employees or students was a good idea.
In my last article, I outlined how we in Snowflake Support use contextual data about where our customers get stuck to improve the overall product experience. Now I’ll take you through how your organization can also implement these important feedback loops from support to product enhancements, to your company’s—and your user’s—benefit. Customers don’t wake up in the morning and decide they’d love to spend time with a Support team.
Last week’s announcement of a data fabric approach from Microsoft was interesting to us on a number of levels, most notably for what it confirmed: Data Fabrics are now mainstream. If you’ve not heard why Data Fabrics are the next big thing, then here is a bit of history. Data Fabric is a concept that started to take hold over a decade ago with published research from Forrester.
In today's competitive software market, Independent Software Vendors (ISVs) constantly seek innovative ways to add value to their offerings, and make features more accessible. One such area that holds immense potential is in providing embedded self-service business intelligence (BI) capabilities to customers, for better access to their data.
In today's fast-paced and ever-evolving business landscape, organizations are constantly striving to gain a competitive edge, make well-informed decisions, and fuel their growth. As a result, advanced Business Intelligence (BI) tools have emerged, revolutionizing the way businesses harness the power of data to drive their success. These sophisticated BI tools offer a wide range of capabilities, empowering companies to collect, analyze, and visualize data from various sources.
Bulk data exports in the Google Search console let you export search data to BigQuery and run complex queries and create custom reports on it.
BigQuery object tables offer a structured record interface for unstructured data, so you can process and manage it securely and programmatically.
The BigQuery partitioning and clustering recommender analyzes workloads and tables and identifies potential cost-optimization opportunities.
