Some 300 million years ago, Earth had one continent called Pangea. Over millions of years, that vast single land mass broke up and drifted in different directions, creating the seven continents that exist today. Since the planet changed so dramatically over millennia, it raises an obvious question: How will it change in the future? The same forces, plate tectonics and continental drift, that broke up Pangea hundreds of millions of years ago still exert themselves.

Companies nowadays are well aware of the importance of embedded analytics when it comes to being data-driven. Today, building your own analytics infrastructure into your software applications for your customers is not the only option anymore. There is a growing market of embedded analytics tools that offer purchasable solutions for data analysis.

IT leaders pick different clouds for many reasons, but the rest of the company shouldn’t be left to navigate the complexity of those decisions. For data analysts, that complexity is most immediately felt when navigating between data silos. Google Cloud has invested deeply in helping customers break down these barriers inherent in a disparate data stack. Back in October 2021, we launched BigQuery Omni to help data analysts access and query data across the barriers of multi cloud environments.

Protecting sensitive data and preventing unintended data exposure is critical for businesses. However, many organizations lack the tools to stay on top of where sensitive data resides across their enterprise. It’s particularly concerning when sensitive data shows up in unexpected places – for example, in logs that services generate, when customers inadvertently send it in a customer support chat, or when managing unstructured analytical workloads.

The cloud data platform combines data warehouse and data lake capabilities to support the exploding world of analytics. Like a data warehouse, the cloud data platform structures, transforms, and queries data. Like a data lake, it classifies multi-structured data objects in an elastic object store. The cloud data platform provides an ideal launchpad for modern business intelligence (BI) projects that need fast, flexible access to lots of varied data. As you might expect, this is a tall order to fill.

We’re excited to announce the general availability of the unstructured data management functionality in Snowflake. We launched public preview of this functionality in September 2021, and since then we have seen adoption by customers across industries for a variety of use cases. These use cases include storing and securing call center recordings, securely sharing PDF documents in Snowflake Data Marketplace, storing medical images and extracting data from them, and many more.

The term “AI-first” has received its share of attention lately, especially in the boardroom where strategies to gain a competitive advantage are always welcome. But before a company embarks on an AI-first strategy, it pays to understand what it is and how it will transform the organization.

To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.

More people are looking for more efficient BI products to integrate into their applications in 2022, and want to know exactly what embedded BI solutions mean for their users. This article will define embedded BI, explain its growth in popularity among software users, and why we suggest Yellowfin as your embedded BI solution for better analytics.

In 2010, Eric Schmidt, then CEO of Google, made the startling claim that every two days we humans generate as much information as we did from the dawn of civilization to today, or about five exabytes of data. At the time, we had TB disk drives and could only imagine an exabyte, which is one million terabytes. The next increments from TB is the peta byte and then the zettabyte, which is 1,000 exabytes. By the end of 2010, the world had crossed the zettabyte threshold.