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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications.

Big Data 154
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Kubernetes in the wild report 2023

Dynatrace

While Kubernetes is still a relatively young technology, a large majority of global enterprises use it to run business-critical applications in production. Findings provide insights into Kubernetes practitioners’ infrastructure preferences and how they use advanced Kubernetes platform technologies. Java, Go, and Node.js

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Data Engineers of Netflix?—?Interview with Kevin Wylie

The Netflix TechBlog

I was later hired into my first purely data gig where I was able to deepen my knowledge of big data. After that, I joined MySpace back at its peak as a data engineer and got my first taste of data warehousing at internet-scale. Both were appliances located in our own data center. What drew you to Netflix?

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No need to compromise visibility in public clouds with the new Azure services supported by Dynatrace

Dynatrace

In addition to providing visibility for core Azure services like virtual machines, load balancers, databases, and application services, we’re happy to announce support for the following 10 new Azure services, with many more to come soon: Virtual Machines (classic ones). Effortlessly optimize Azure database performance.

Azure 148
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Driving down the cost of Big-Data analytics - All Things Distributed

All Things Distributed

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. However, this cannot be done without efficient, scalable data analytics.

Big Data 111
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The Need for Real-Time Device Tracking

ScaleOut Software

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. The technology that can do this is called in-memory computing. The heavy lifting is deferred to the back office.

IoT 78
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Why MySQL Could Be Slow With Large Tables

Percona

While the technologies have evolved and matured enough, there are still some people thinking that MySQL is only for small projects or that it can’t perform well with large tables. With disks being faster nowadays and CPU and memory resources being cheaper, we could easily say MySQL can handle TBs of data with good performance.