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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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How FiveStars re-engineered its data engineering stack

CIO

It shows in his reluctance to run his own servers but it’s perhaps most obvious in his attitude to data engineering, where he’s nearing the end of a five-year journey to automate or outsource much of the mundane maintenance work and focus internal resources on data analysis. It’s not a good use of our time either.”

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Sigmoid raises $12 million to scale its data engineering and analytics platform

TechCrunch

A leading Fortune 500 FMCG company received an 11% improvement in its return on marketing investments, Anand said of the customers’ performance. Sigmoid raises $12 million to scale its data engineering and analytics platform by Jagmeet Singh originally published on TechCrunch.

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NJ Transit creates ‘data engine’ to fuel transformation

CIO

Data engine on wheels’. To mine more data out of a dated infrastructure, Fazal first had to modernize NJ Transit’s stack from the ground up to be geared for business benefit. Today, NJ Transit is a “data engine on wheels,” says the CIDO. “We have shown out value,” Fazal says of the transformation.

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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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Delivering Modern Enterprise Data Engineering with Cloudera Data Engineering on Azure

Cloudera

After the launch of CDP Data Engineering (CDE) on AWS a few months ago, we are thrilled to announce that CDE, the only cloud-native service purpose built for enterprise data engineers, is now available on Microsoft Azure. . Prerequisites for deploying CDP Data Engineering on Azure can be found here.

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Here’s where MLOps is accelerating enterprise AI adoption

TechCrunch

In addition to requiring a large amount of labeled historic data to train these models, multiple teams need to coordinate to continuously monitor the models for performance degradation. Data engineers play with tools like ETL/ELT, data warehouses and data lakes, and are well versed in handling static and streaming data sets.