Mastercard is one of the world’s top payment processing platforms, with more than 700 million cards in use worldwide. In the US, nearly 40% of American adults hold a Mastercard-branded card. And the company is going from strength to strength; despite a dip in valuation of more than a third when the pandemic hit, the company has doubled in value three times in the last nine years, recently reaching a market capitalization of more than $350B dollars.

In a press release delivered today, Unravel Data announced its appearance on CRN’s Big Data 100 list for 2021. Unravel’s entry appears in the Data Management and Integration category. Also featured in this category are other rising stars such as Confluent, Fivetran, Immuta, and Okera, all of whom spoke at new industry conference DataOps Unleashed, held in March.

Cox Automotive is a large, global business. It’s part of Cox Enterprises, a media conglomerate with a strong position in the Fortune 500, and a leader in diversity. Cox also has a strong history of technological innovation, with its core cable television business serving as a leader in the growth and democratization of media over the last several decades.

Let’s start with a real-world example from one of my past machine learning (ML) projects: We were building a customer churn model. “We urgently need an additional feature related to sentiment analysis of the customer support calls.” Creating the data pipeline to extract this dataset took about 4 months! Preparing, building, and scaling the Spark MLlib code took about 1.5-2 months!

Adobe is a legendary Silicon Valley company. From the desktop publishing era of the 1980s, powered by the Adobe Postscript page description language, through the creation and marketing of Photoshop, Illustrator, and other creative power tools, the digital revolution is unthinkable without Adobe.

The exponential adoption of IT technologies over the past several decades has had a profound impact on organizations of all sizes. Whether it is a small, medium, or large enterprise, the need to create web applications while managing an extensive set of data effectively is high on every CIO’s priority list. As a result, there has been an ongoing effort to implement better approaches to software development, data analysis, and data management.

In this blog post, I’ll set up and run a couple of experiments to demonstrate the effects of different kinds of partition pruning in Spark.

Over the past several years, there has been an explosion of different terms related to the world of IT operations. Not long ago, it was standard practice to separate business functions from IT operations. But those days are a distant memory now, and for good reason.

Data is often compared to oil – it powers today’s organizations, just like the fossil fuel powered companies of the past. Just like oil, the data that companies collect needs to be refined, structured, and easily analyzed in order for it to really provide value in the form of gaining actionable insights. Every organization today is in the process of harnessing the power of their data using advanced analytics, which is likely running on a modern data stack.

In this episode of CDO Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Meenal Iyer, Sr. Director of Enterprise Analytics and Data at Tailored Brands. They discuss battlescars in two areas, data and metrics: Growing Data Literacy and Developing a Data-Driven Culture and Standardization of Business Metrics.