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The key to operational AI: Modern data architecture

CIO

The team should be structured similarly to traditional IT or data engineering teams. However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

Maintaining legacy systems can consume a substantial share of IT budgets up to 70% according to some analyses diverting resources that could otherwise be invested in innovation and digital transformation. This is where Delta Lakehouse architecture truly shines. This unified view makes it easier to manage and access your data.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows. The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both.

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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

Hes seeing the need for professionals who can not only navigate the technology itself, but also manage increasing complexities around its surrounding architectures, data sets, infrastructure, applications, and overall security. Weve been innovating with AI, ML, and LLMs for years, he says. But not every company can say the same.

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Fundamentals of Data Engineering

Xebia

The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.

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Ready to transform how your IT organization drives business outcomes with AIOps?

CIO

Today, IT encompasses site reliability engineering (SRE), platform engineering, DevOps, and automation teams, and the need to manage services across multi-cloud and hybrid-cloud environments in addition to legacy systems. Experience and deliberate cross-functional learning opportunities are needed for people to acquire these skills.

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Scala returning to its origins: A tale of 4 chapters

Xebia

This appeal attracted many talented engineers and bright students, leading to innovations like Twitter, Akka, Spark, Flink, and Play, among others. For example, events such as Twitters rebranding to X, and PySparks rise in the data engineering realm over Spark have all contributed to this decline.