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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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Data Gravity in Cloud Networks: Distributed Gravity and Network Observability

Kentik

As datasets scale and networks become distributed to free their data, the data gravity story begins to morph into a data complexity story. The more distributed an enterprise’s data, the more heterogeneous its sources. Cloud networks give operators a much wider surface area to protect against cyberattacks.

Network 99
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Big Data in Healthcare: Sources and Real-World Applications

Altexsoft

In this article, we will explain the concept and usage of Big Data in the healthcare industry and talk about its sources, applications, and implementation challenges. What is Big Data and its sources in healthcare? So, what is Big Data, and what actually makes it Big? Let’s see where it can come from.

Big Data 116
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12 data science certifications that will pay off

CIO

Whether you’re looking to earn a certification from an accredited university, gain experience as a new grad, hone vendor-specific skills, or demonstrate your knowledge of data analytics, the following certifications (presented in alphabetical order) will work for you. Check out our list of top big data and data analytics certifications.)

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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Storage provisioning.

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

Altexsoft

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data 87
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Unearthing the Value of Network Traffic Data with Big Data Network Analytics

Kentik

Seeing Beneath the Surface with Post-Hadoop Big Data. Irwin’s story may be interesting, but what does it have to do with network traffic data? The answer is rooted in the experience of Kentik’s founders, who’ve spent decades building and operating some of the world’s biggest and most complex networks.