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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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What is container orchestration?

Dynatrace

This orchestration includes provisioning, scheduling, networking, ensuring availability, and monitoring container lifecycles. The configuration file directs the container orchestration tool on how to retrieve container images, how to create a network between containers, and where to store log data or mount storage volumes.

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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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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. Computer operations manages the physical location of the servers — cooling, electricity, and backups — and monitors and responds to alerts. Functionality. ITOps vs. AIOps.

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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 best they can usually do in real-time using general purpose tools is to filter and look for patterns of interest.

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

The Netflix TechBlog

The second phase involves migrating the traffic over to the new systems in a manner that mitigates the risk of incidents while continually monitoring and confirming that we are meeting crucial metrics tracked at multiple levels. We use this additional logging to debug and identify the root cause of issues driving the mismatches.

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Why MySQL Could Be Slow With Large Tables

Percona

If CPU usage is not a bottleneck in your setup, you can leverage compression as it can improve performance which means that less data needs to be read from disk and written to memory, and indexes are compressed too. It can help us to save costs on storage and backup times. MyRocks is shipped in Percona Server for MySQL.