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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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10 most in-demand enterprise IT skills

CIO

The software and services an organization chooses to fuel the enterprise can make or break its overall success. And part of that success comes from investing in talented IT pros who have the skills necessary to work with your organizations preferred technology platforms, from the database to the cloud.

UI/UX 203
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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

According to a survey conducted by FTI Consulting on behalf of UST, a digital transformation consultancy, 99% of senior IT decision makers say their companies are deploying AI, with more than half using and integrating it throughout their organizations, and 93% say that AI will be essential to success in the next five years.

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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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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 used by data scientists, data-centric applications, and other data consumers. The data engineer role.

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The top 15 big data and data analytics certifications

CIO

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 190
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Handling real-time data operations in the enterprise

O'Reilly Media - Data

Getting DataOps right is crucial to your late-stage big data projects. Data science is the sexy thing companies want. The data engineering and operations teams don't get much love. The organizations don’t realize that data science stands on the shoulders of DataOps and data engineering giants.