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Business Intelligence on the Cloud Data Platform: Approaches to Schemas

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.

Unstructured Data Now Generally Available in Snowflake, Processing with Snowpark in Public Preview

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.

New Pathways to New Insights

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.

What Does Embedded BI Really Mean? OEM Reporting Tools Defined

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.

Do You Have What it Takes to Manage the Flood of Data?

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.

Assessing the Validity and Relevance of Data To Discover True, Actionable Information and Insights

In a previous article, we talked about the lost art of questioning and its importance when working with data and information to find actionable insights. In this article, we will expand on this topic and explain how questioning differs depending on what stage in the process you are from transforming data and information into insights.

At Covanta, data health improves the business and the planet

At Talend, we tend to describe poorly organized, unhealthy data as “digital landfills.” But we don’t often talk about actual landfills. That’s right, the ones filled with trash. As anyone watching real estate prices will know, land is a finite resource. It’s crazy to think that we’re still dedicating land to storing our garbage, where it will sit releasing pollutants and greenhouse gases for decades to come.

How Mercado Libre Builds Upon a Continuous Intelligence Ecosystem with BigQuery and Looker

At Mercado Libre, we are obsessed with unlocking the power and potential of data. One of our key cultural principles is to have a Beta Mindset. This means that we operate in a “state of beta”, constantly asking new questions of our data, experimenting with technologies and iterating our business operations in service of creating the best experiences for our customers.

MLOps in BigQuery ML with Vertex AI Model Registry

Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders.