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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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

CIO

But 76% of respondents say theres a severe shortage of personnel skilled in AI at their organization, according to the August report. In a November report by HR consultancy Randstad, based on a survey of 12,000 people and 3 million job profiles, demand for AI skills has increased five-fold between 2023 and 2024.

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The future of data: A 5-pillar approach to modern data management

CIO

This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices.

Data 167
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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. 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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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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Are you ready for MLOps? 🫵

Xebia

Gartner reported that on average only 54% of AI models move from pilot to production: Many AI models developed never even reach production. The development- and operations world differ in various aspects: Development ML teams are focused on innovation and speed Dev ML teams have roles like Data Scientists, Data Engineers, Business owners.