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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. Performance. Native frameworks.

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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. The pipelines can be stateful and the engine’s middleware should provide a persistent storage to enable state checkpointing. Interoperability with Hadoop.

Big Data 154
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What is container orchestration?

Dynatrace

By embracing public cloud and hybrid cloud computing environments, IT teams can further accelerate development and automate software deployment and management. A container is a small, self-contained, fully functional software package that can run an application or service, isolated from other applications running on the same host.

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Kubernetes in the wild report 2023

Dynatrace

The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace Software Intelligence Platform to keep their Kubernetes clusters secure, healthy, and high performing. Open-source software drives a vibrant Kubernetes ecosystem. Java, Go, and Node.js

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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130
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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 post The Need for Real-Time Device Tracking appeared first on ScaleOut Software.

IoT 78
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. Utilizing cloned real traffic, we can exercise the diversity of inputs from a wide range of devices and device application software versions in production.

Traffic 339