Everybody is talking about artificial intelligence (AI) and data, but how do you make it real for your business? That's where data for operations (DataOps) comes in. Data Is Everywhere Everything we ...

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the ...

NewVantage Partners 2018 Big Data Executive Survey, demonstrates that culture and organizational impediments are leading barriers to harnessing Big Data. Over half of executives surveyed reported that ...

Enterprises‌ ‌have‌ ‌struggled‌ ‌to‌ ‌collaborate‌ ‌well ‌around‌ ‌their‌ ‌data, which hinders their ability to adopt‌ ‌transformative‌ ‌applications‌ ‌like‌ ‌AI.‌ ‌ ‌The‌ ‌evolution‌ ‌of ...

Ashish Thusoo and Joydeep Sen Sarma know a thing or two about big data. They led the team that built Facebook's data infrastructure, and they are also the co-authors of the Apache Hive project and ...

The practice focuses on collaboration and automation to speed delivery of analytics—and accelerate innovation. Produced in association withHitachi Vantara Businesses today are facing a mammoth digital ...

DataOps is a viable approach that combines data engineering into operations processes. It aims to promote data management practices and procedures that improve the speed and accuracy of analytics.

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Systematic data management investment and effort is associated with outsized returns on data-driven initiatives, according to a newly released report on DataOps from BMC Software. Further, large ...

Digitalization requires data aggregation, standardization, and contextualization at scale. I believe the missing link to industrial transformation is a codeless solution that can aggregate, ...

Data teams within businesses and organizations strive to provide internal business users and external customers with analytical insights. But those efforts often fall short because the data and ...