Optimizing & Simplifying Business Analytics | Part 1 | Snowflake Inc.

Jumpstarting the digitalization of business, Babu Kuttala, Chief Data and Analytics Officer at ABB, details how he came into his role, his influence in different markets, & how he grows and simplifies ABB's use of internal and external data. Rise of the Data Cloud is brought to you by Snowflake.

How a Discovery Data Warehouse, the next evolution of augmented analytics, accelerates treatments and delivers medicines safely to patients in need

I met Matthew in New York City about a year ago. We sat in a private conference room and he told me the story of his pharma startup. A small group of researchers set out to solve the black-box enigma of certain kinds of vicious cancers. There are so many cancers, so their vision was to focus on especially heinous ones. Fast forward to their recent FDA approval of their “Hail Mary” procedure and treatment methodology for stage-four patients of a particular cancer.

Demo: Cloudera DataFlow on Data Hub

Cloudera DataFlow for Data Hub makes hybrid use cases possible by extending on-premises flow management, streams messaging, and stream processing and analytics capabilities to the public cloud. Watch an integrated demo of Cloudera DataFlow on Data Hub to understand how easy it is to ingest, process, and analyze your streaming data across multiple public cloud clusters.

The dos and don'ts of picking new BI tools

Choosing a new business intelligence (BI) tool can be a confusing, time-consuming and, frankly, exhausting process. It’s also an anxiety-inducing one: Companies are willing to shell out millions on business intelligence software, yet picking the wrong tool can set their business back months. With so many BI tools on the market now, choosing the wrong one is far too easy. This is a big factor contributing to the fact that 87% of businesses haven’t reached business intelligence maturity yet.


Introducing Lightweight, Customizable ML Runtimes in Cloudera Machine Learning

With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.


8 key considerations for choosing an Embedded Analytics solution

Historically, analytics has not always been a priority feature for software vendors. Many applications typically are built with analytics bolted-on later, as standalone tools. But the changing needs of today’s business users has accelerated the importance of providing in-built ways to monitor and explore their data while they use your software.