Database sharding

Data partitioning, or sharding, involves dividing a large database into smaller pieces. This helps improve how the database is managed, its performance, availability, and how the application distributes its workload. The idea behind sharding is that once a database reaches a certain size, it’s more efficient and cost-effective to add more machines horizontally (meaning, adding more machines) than to invest in bigger, more powerful servers vertically. In nutshell, it is the process of splitting up a DB/table across multiple machines.

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Latency metrics

Latency measures are crucial for checking how well your apps and services perform. Latency means the total time it takes for a piece of data to go from where it starts to where it ends up, usually on a network. When we talk about latency, we’re mostly talking about how fast things move in a network. It’s one of the main things we look at to see if a service is good or not. We usually measure it in milliseconds. The lower the latency, the better the user’s experience.

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Kubernetes Resource Quota and LimitRange

Kubernetes allows you to manage your application in numerous ways. Consider that your users spread across multiple teams, or projects, at that time we can start thinking about using namespaces within Kubernetes cluster. In Kubernetes, namespaces provide a mechanism for isolating groups of resources within a single cluster. Namespaces are way to divide cluster resources in groups for multiple users (via resource-quota). Each namespace will have one or multiple containers running inside it.

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Business Continuity and Disaster Recovery (BCDR)

What is Business continuity and disaster recovery?

BCDR represents a set of approaches or processes that helps a company to recover from a disaster so company can resume its routine business operations. Disasters includes hardware failure, natural calamities (earthquake or flood), network failure, employee negligence, outages or disruption due to power failure, cyberattacks etc.

A BCDR plan ensures that businesses operate as close to normal as possible after an unexpected interruption, with minimal loss of data.

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