Today’s globalized organizations demand a new standard for communicating and sharing information. That includes data-rich content that moves through environments, networks, and locales. From being stored, analyzed, and shared, to quickly and effectively moving between environments, to spinning up in clusters and informing endless applications—data is more critical than ever.
This blog post is part of a series on Cloudera’s Operational Database (OpDB) in CDP. Each post goes into more details about new features and capabilities. Start from the beginning of the series with, Operational Database in CDP. This blog post gives you an overview of the languages, frameworks, and applications supported by Cloudera’s OpDB.
Data accessibility and analysis is a crucial part of getting value from your data. While there are many methods to view data when it comes to BigQuery, one common way is to export query results as an email on a scheduled basis. This lets end users get an email with a link to the most recent query results, and is a good solution for anyone looking for daily statistics on business processes, monthly summaries of website metrics, or weekly business reviews.
Manufacturing has historically been laggards in its adoption of emerging technologies as business processes, from the production line to back-office operations, have inherently been tied to legacy applications. But that is rapidly changing. Faced with competitive pressures, and driven by technological enhancements across broad sectors of the economy, today’s manufacturing leaders are seeking new ways to improve productivity, reduce downtime, and streamline operations and the supply chain.
Apache YuniKorn (Incubating) is a standalone resource scheduler that aims to bring advanced scheduling capabilities for Big Data workloads onto containerized platforms. Please read YuniKorn: a universal resources scheduler to learn about the rationale and architecture. Since the time of our last post, we are delighted to update that YuniKorn was accepted by the Apache incubator in Jan 2020!
It was three years ago, just after the Gartner Magic Quadrant (MQ) came out, that everything in customer success changed at Yellowfin. We had made it into the MQ again but we weren’t positioned where we believed we should be. We were a product-driven company that had been first to market for many of the functionalities that have since become expected in what BI vendors offer today.
It seems everyone is talking about machine learning (ML) these days — and ML’s use in products and services we consume everyday continues to be increasingly ubiquitous. But for many enterprise organizations, the promise of embedding ML models across the business and scaling use cases remains elusive. So what about ML makes it difficult for enterprises to adopt at scale?
Too often, companies are finding out after the fact that customers have stopped using their product or service, without enough notice to have done anything about it. The term customer churn is used to describe the loss of existing customers. These are people or organizations that were using a company’s products and/or services and have decided not to use them anymore, in favor of a competitor. Tracking customer churn is a key business metric for most companies.
Our Head of Product Design and Creative Director, Tony Prysten, has worked in brand, design and advertising roles over the course of his career. Bringing his wealth of experience to Yellowfin, he now shapes the creative and UX experience of our product. Here he shares his thoughts on how design flexibility improves the dashboard experience.
We are now living in a truly hyper-connected environment where vast amounts of data are being transferred, gathered, and consumed daily. There are 3.9 billion internet users globally – a number that is still growing. Just think about the last time you were waiting for a Zoom conference call to start.
