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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. Incremental computations over sliding windows is a group of techniques that are widely used in digital signal processing, in both software and hardware. Apache Spark [10]. References.

Big Data 154
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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. Key challenges. Performance.

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What Should You Know About Graph Database’s Scalability?

DZone

There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable. Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios.

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Auto-Diagnosis and Remediation in Netflix Data Platform

The Netflix TechBlog

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. This has led to a dramatic reduction in the time it takes to detect issues in hardware or bugs in recently rolled out data platform software.

Big Data 237
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What is ITOps? Why IT operations is more crucial than ever in a multicloud world

Dynatrace

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. Although modern cloud systems simplify tasks, such as deploying apps and provisioning new hardware and servers, hybrid cloud and multicloud environments are often complex.

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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., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

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Kubernetes in the wild report 2023

Dynatrace

On-premises data centers invest in higher capacity servers since they provide more flexibility in the long run, while the procurement price of hardware is only one of many cost factors. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.