Percona
NOVEMBER 9, 2023
Kubernetes manages and orchestrates these containers, handling tasks such as deployment, scaling, load balancing, and networking. Your workloads, encapsulated in containers, can be deployed freely across different clouds or your own hardware. have adopted Kubernetes.
Scalegrid
MAY 13, 2020
Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. Greenplum interconnect is the networking layer of the architecture, and manages communication between the Greenplum segments and master host network infrastructure.
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Percona
AUGUST 30, 2023
In general terms, here are potential trouble spots: Hardware failure: Manufacturing defects, wear and tear, physical damage, and other factors can cause hardware to fail. heat) can damage hardware components and prompt data loss. Human mistakes: Incorrect configuration is an all-too-common cause of hardware and software failure.
Percona
JUNE 22, 2023
Defining high availability In general terms, high availability refers to the continuous operation of a system with little to no interruption to end users in the event of hardware or software failures, power outages, or other disruptions. Without enough infrastructure (physical or virtualized servers, networking, etc.),
All Things Distributed
SEPTEMBER 6, 2013
When Tom Tom launched the LBS platform they wanted the ability to reach millions of developers all around the world without having them invest a lot of capital upfront in hardware and building expensive data centers so turned to the cloud.
ScaleOut Software
JULY 27, 2018
A bigger problem with query is that when it matches many results, a large amount of data may need to be returned over the network to the requesting client for processing, as illustrated below. This can quickly saturate the network (and bog down the client). We have seen this computing model’s utility in countless applications.
ScaleOut Software
JULY 27, 2018
A bigger problem with query is that when it matches many results, a large amount of data may need to be returned over the network to the requesting client for processing, as illustrated below. This can quickly saturate the network (and bog down the client). We have seen this computing model’s utility in countless applications.
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