The COVID-19 global pandemic has changed the ways we work, including how we use data, forever. Amidst the crisis, the analytics industry is experiencing its own paradigm shift.

During the process of turning data into insights, the most compelling data often comes with an added responsibility—the need to protect the people whose lives are caught up in that data. Plenty of data sets include sensitive information, and it’s the duty of every organization, down to each individual, to ensure that sensitive information is handled appropriately.

COVID-19 vaccines were developed in record time. One of the main reasons for the accelerated development was the quick exchange of data between academia, healthcare institutions, government agencies, and nonprofit entities. “COVID research is a great example of where sharing data and having large quantities of data to analyze would be beneficial to us all,” said Renee Dvir, solutions engineering manager at Cloudera.

According to Harvard Business Review, South Korea is one of the leading countries in the world for technology innovation, and it’s among the top producers of new data. Technology is so ingrained in the national identity that it launched a “Digital New Deal” to lay the foundation for a digital economy that will facilitate growth and innovation, according to PR Newswire.

Enabling customers and users to quickly find the value within a product is critical for many organizations and at the heart of being a product manager. The approach to driving user growth involves a growth mindset, combining qualitative and quantitative research methods, and driving impactful solutions.

There is an urgent need for banks to be nimble and adaptable in the thick of a multitude of industry challenges, ranging from the maze of regulatory compliance, sophisticated criminal activities, rising customer expectations and competition from traditional banks and new digital entrants. As banks find their bearings in this landscape, what appear to be insurmountable odds are in fact opportunities for growth and competitive differentiation.

In the first part of this series , I outlined the prerequisites for a modern Enterprise Data Platform to enable complex data product strategies that address the needs of multiple target segments and deliver strong profit margins as the data product portfolio expands in scope and complexity: With this article, I will dive into the specific capabilities of the Cloudera Data Platform (CDP) that has helped organizations to meet the aforementioned prerequisite capabilities and fulfill a successful data prod

Over the last year, perhaps unsurprisingly, increasing numbers of companies have made the jump to the cloud. It’s become a necessary move for so many businesses. But, as I discussed with Joe DosSantos on the latest episode of Data Brilliant – the rewards are abundant, but the journey is not always straight forward.

In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.

So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.