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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

Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. 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.

Course 45
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New live online training courses

O'Reilly Media - Ideas

Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24.

Course 66
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219+ live online training courses opened for June and July

O'Reilly Media - Ideas

Understanding Data Science Algorithms in R: Scaling, Normalization and Clustering , August 14. Real-time Data Foundations: Spark , August 15. Visualization and Presentation of Data , August 15. Python Data Science Full Throttle with Paul Deitel: Introductory AI, Big Data and Cloud Case Studies , September 24.

Course 49
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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

You can use Amazon Inspector to automate vulnerability discovery and management for Amazon Elastic Compute Cloud (Amazon EC2) instances, containers, AWS Lambda functions, and identify the network reachability of your workloads. Ram Vittal is a Principal ML Solutions Architect at AWS.

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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. The next big step in advancing Azure was introducing the container strategy, as containers and microservices took the industry to a new level. Data Engineer $130 000.

Azure 52
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Predictive analytics helps Fresenius anticipate dialysis complications

CIO

Our primary challenge was in our ability to scale the real-time data engineering, inferences, and real-time monitoring to meet service-level agreements during peak loads (6K messages per second, 19MBps with 60K concurrent lambda invocations per second) and throughout the day (processing more than 500 million messages daily, 24/7).”