DataOps
Gartner identified the top 10 data and analytics (D&A) trends for 2023 that can guide D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities ...
DataOps as a practice is maturing, according to a survey from Unravel Data. This year, more than 44% of respondents reported they are actively employing DataOps methodologies, compared to just less than a quarter (21%) of respondents in 2022 ...
Based on a survey of automation-focused IT professionals worldwide, the 2022 Global State of IT Automation report offers a unique perspective on the convergence of automation with cloud, DataOps, DevOps, and hybrid IT topics. Read on ...
Modern complex systems are easy to develop and deploy but extremely difficult to observe. Their IT Ops data gets very messy. If you have ever worked with modern Ops teams, you will know this. There are multiple issues with data, from collection to processing to storage to getting proper insights at the right time. I will try to group and simplify them as much as possible and suggest possible solutions to do it right ...
Robotic Data Automation (RDA) is a new paradigm to help automate data integration and data preparation activities involved in dealing with machine data for Analytics and AI/Machine Learning applications. RDA is not just a framework, but also includes a set of technologies and product capabilities that help implement the data automation ...
Industry experts offer predictions on how APM and related technologies will evolve and impact business in 2020. Part 3 covers more about AIOps, AI and Machine Learning (ML) ...
As the New Year approaches, it is time for APMdigest's 10th annual list of Application Performance Management (APM) predictions. Industry experts offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2020 ...
Data-driven applications are helping drive cloud growth, according to a survey by Unravel Data. The data also reveals that enterprises are most concerned with a lack of sufficient technical talent to properly manage these data systems as well as the perceived high cost of deploying a modern data infrastructure ...