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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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What is a data architect? Skills, salaries, and how to become a data framework master

CIO

Analytics/data science architect: These data architects design and implement data architecture supporting advanced analytics and data science applications, including machine learning and artificial intelligence. In some ways, the data architect is an advanced data engineer.

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DBeaver takes $6M seed investment to build on growing popularity

TechCrunch

Krupenya says this capability puts data administration in reach of not just the most technical data engineers, but also people in other lines of business roles, who normally might not have access to tools like this. “So So actually anyone who needs to work with data can use DBeaver,” she told TechCrunch.

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Data Summit 2023 Event Recap

Datavail

Attendees were able to explore solutions and strategies to help them unlock the power of their data and turn it into actionable insights. The event tackles topics on artificial intelligence, machine learning, data science, data management, predictive analytics, and business analytics.

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How to Hire Women in Tech: The Ultimate Guide

Mobilunity

Dr. Fei-Fei Li: A pioneer in artificial intelligence, Dr. Li is the co-director of Stanford University’s Human-Centered AI Institute and the Stanford Vision and Learning Lab. She previously served as the director of the Stanford Artificial Intelligence Lab.

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Data Architect: Role Description, Skills, Certifications and When to Hire

Altexsoft

The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority.

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Mind the Gap: 3 Ways to Fill Your Data Management Talent Shortage

TIBCO - Connected Intelligence

Further, these challenges are growing exponentially as massive data trends, such as the ten I identified in a recent blog , combine to make data management more complex and difficult than ever. In fact, data engineering staffing savings of 40 percent are typical. Embed intelligence.