With a whole new Guided Natural Language Query (NLQ) capability and several new enhancements across every part of the platform, Yellowfin 9.7 is a must-have upgrade.

It takes vision, purpose, and skill to unlock the power of data. It also takes the right strategy. For ExxonMobil, Ares Trading (Merck), and the University of California San Diego (UCSD), the right strategy is taking full advantage of the cloud. All three organizations have partnered with Cloudera, leveraging a hybrid or cloud-based architecture to improve the lives of the people who depend on their organizations’ data.

Machine learning (ML) models have become key drivers in helping organizations reveal patterns and make predictions that drive value across the business. While extremely valuable, building and deploying these models remains in the hands of only a small subset of expert data scientists and engineers with deep programming and ML framework expertise.

At Snowflake, putting the customer first is an essential company value. But “customer-centric” is more than just a buzzword: We use a data-driven, outside-in lens on everything we do, at all levels of the company. In particular, here’s how Snowflake Support is listening to you—our customers—and continuously improving the Snowflake customer experience at every touchpoint.

The business world is rapidly pivoting all the time. Strategic shifts, reprioritization and being first all require being smart while moving fast. The value of agility has never stood out more due to the need to react to new realties in everything from public health, remote and in-office business policies and workflows, to broader economic concerns like supply chain as we move into recovery and revitalization.

In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. We need to get to the root of the problem. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI.

For Cloudera ensuring data security is critical because we have large customers in highly regulated industries like financial services and healthcare, where security is paramount. Also, for other industries like retail, telecom or public sector that deal with large amounts of customer data and operate multi-tenant environments, sometimes with end users who are outside of their company, securing all the data may be a very time intensive process.

Financial products are no longer characterized by the steps of filling out a form, waiting for a credit decision and, if successful, watching the monthly payments leaving your account.

There is an explosion of data from a myriad of sources and an insatiable demand to consume it. Traditional manual ETL methods are too brittle to keep up. Leaving many a BI team struggling to provide meaningful business insights quickly.

As customers grow their analytical workloads and footprint on BigQuery, their monitoring and management requirements evolve - they want to be able to manage their environments at scale, take action in context. They also desire capacity management capabilities to optimize their BigQuery environments. With our BigQuery Administrator Hub capabilities, customers can now better manage BigQuery at scale.