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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. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data 154
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What Should You Know About Graph Database’s Scalability?

DZone

Do Not Be Misled Designing and implementing a scalable graph database system has never been a trivial task. There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable.

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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., Finally, we show that Seer can identify application level design bugs, and provide insights on how to better architect microservices to achieve predictable performance. ASPLOS’19.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. For unclassified errors, the job may be retried multiple times with the default retry policy.

Tuning 210
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers.

Big Data 321
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What is ITOps? Why IT operations is more crucial than ever in a multicloud world

Dynatrace

ITOps refers to the process of acquiring, designing, deploying, configuring, and maintaining equipment and services that support an organization’s desired business outcomes. ITOps is an IT discipline involving actions and decisions made by the operations team responsible for an organization’s IT infrastructure. ITOps vs. AIOps.

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Data Engineers of Netflix?—?Interview with Samuel Setegne

The Netflix TechBlog

clinical data was often small enough to fit into memory on an average computer and only in rare cases would its computation require any technical ingenuity or massive computing power. There was not enough scope to explore the distributed and large-scale computing challenges that usually come with big data processing.