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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

Delta Lake: Fueling insurance AI Centralizing data and creating a Delta Lakehouse architecture significantly enhances AI model training and performance, yielding more accurate insights and predictive capabilities. Modern AI models, particularly large language models, frequently require real-time data processing capabilities.

Insurance 164
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What is a data engineer? An analytics role in high demand

CIO

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

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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. The authors state that the target audience is technical people and, second, business people who work with technical people. Nevertheless, I strongly agree.

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When is data too clean to be useful for enterprise AI?

CIO

You can’t treat data cleaning as a one-size-fits-all way to get data that’ll be suitable for every purpose, and the traditional ‘single version of the truth’ that’s been a goal of business intelligence is effectively a biased data set. There’s no such thing as ‘clean data,’” says Carlsson.

Data 211
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Who is Business Intelligence Developer: Role Description, Responsibilities, and Skills

Altexsoft

But, as a business, you might be interested in extracting value of this information instead of just collecting it. Business intelligence (BI) is a set of technologies and practices to transform business information into actionable reports and visualizations. Who is a business intelligence developer?

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Data Scientist vs Data Engineer: Differences and Why You Need Both

Altexsoft

Engineers are not only the ones bearing helmets and operating on construction sites. Scientists don’t always wear lab coats or handle test tubes. Explaining the difference, especially when they both work with something intangible such as data , is difficult. Data science vs data engineering.

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How companies around the world apply machine learning

O'Reilly Media - Data

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.