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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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160+ live online training courses opened for May and June

O'Reilly Media - Ideas

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. Real-time Data Foundations: Kafka , June 11.

Course 45
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Azure vs AWS: How to Choose the Cloud Service Provider?

Existek

Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. They focus much attention on advancing user experiences utilizing AI, robotics, machine learning, IoT, etc. . AWS Lambda Azure Functions from $0.20

Azure 52
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Predictive analytics helps Fresenius anticipate dialysis complications

CIO

German healthcare company Fresenius Medical Care, which specializes in providing kidney dialysis services, is using a combination of near real-time IoT data and clinical data to predict one of the most common complications of the procedure.

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170+ live online training courses opened for March and April

O'Reilly Media - Ideas

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. What You Need to Know About Data Science , April 1. IoT Fundamentals , April 4-5.

Course 10
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Topics to watch at the Strata Data Conference in New York 2019

O'Reilly Media - Ideas

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.