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Video Highlights: Unleashing DataOps Keynote

In this keynote presentation from the DataOps Unleashed virtual conference, innovator Kunal Agarwal, CEO of Unravel Data, describes how companies large and small are using DataOps to make their technology stacks hum, get more done at a lower cost, and improve both customer experience and the bottom line.

Data Engineering Survey: 2021 Impact Report

This Data Engineering Survey: 2021 Impact Report summarizes key findings from the inaugural survey and provides a glimpse into the current and future state of data engineering and DataOps. The report highlights some of the major trends uncovered in this year’s survey including the adoption of cloud data platforms, what platforms are winning (and emerging), what data engineers find to be their biggest challenges, and how organizations are handling sensitive data.

Data Engineering Survey: 2021 Impact Report

This Data Engineering Survey: 2021 Impact Report summarizes key findings from the inaugural survey and provides a glimpse into the current and future state of data engineering and DataOps. The report highlights some of the major trends uncovered in this year’s survey including the adoption of cloud data platforms, what platforms are winning (and emerging), what data engineers find to be their biggest challenges, and how organizations are handling sensitive data.

DataOps Engineer Will Be the Sexiest Job in Analytics

In this contributed article, Christopher Bergh, a Founder and Head Chef at DataKitchen, discusses how DataOps, is transforming the roles on the data analytics team. DataOps is a better way to develop and deliver analytics. It applies Agile development, DevOps and lean manufacturing principles to data analytics producing a transformation in data-driven decision making.

Model Risk Management in the Age of AI

In this contributed article, Stu Bailey, Co-Founder and Chief AI Architect of ModelOp, discusses how financial services companies can easily validate multiple AI/ML models and reduce ML project costs by 30% through automation. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization. This drives business value by getting models into production faster and with greater visibility, accountability and control.