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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes.

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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Enhanced data security, better data integrity, and efficient access to information. This article cuts through the complexity to showcase the tangible benefits of DBMS, equipping you with the knowledge to make informed decisions about your data management strategies. What are the key advantages of DBMS?

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

Dynatrace

Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. Why is IT operations important?

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Protecting critical infrastructure and services: Ensure efficient, accurate information delivery this election year

Dynatrace

To address this, state and local governments are adopting multicloud environments to achieve the necessary speed, scale, and agility to keep up with faster digital transformation. In contrast, observability enables teams to understand a system’s internal state by analyzing the data it generates, including logs, metrics, and traces.

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Snap: a microkernel approach to host networking

The Morning Paper

Snap: a microkernel approach to host networking Marty et al., This paper describes the networking stack, Snap , that has been running in production at Google for the last three years+. It implements reliability, congestion control, optional ordering, flow control, and execution of remote data access operations. SOSP’19.

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Beyond data and model parallelism for deep neural networks

The Morning Paper

Beyond data and model parallelism for deep neural networks Jia et al., Traditional approaches to training exploit either data parallelism (dividing up the training samples), model parallelism (dividing up the model parameters), or expert-designed hybrids for particular situations. SysML’2019. Expanding the search space.

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What is a message queue? How an observability platform eases message queue monitoring

Dynatrace

In a distributed processing environment, message queuing is similar, although the speed and volume of messages are much greater. Additionally, a message queue can smooth out spiky workloads by enabling the producers and consumers to work at a consistent pace without losing data. Queued messages are typically small and specific.