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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. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs. Pipelining.

Big Data 154
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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. Key challenges.

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What is a Distributed Storage System

Scalegrid

Durability Availability Fault tolerance These combined outcomes help minimize latency experienced by clients spread across different geographical regions. Handling Large Volumes of Data Distributed storage systems employ the technique of data sharding or partitioning to handle immense quantities of information.

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. A network administrator sets up a network, manages virtual private networks (VPNs), creates and authorizes user profiles, allows secure access, and identifies and solves network issues.

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Redis vs Memcached in 2024

Scalegrid

Advanced Redis Features Showdown Big data center concept, cloud database, server power station of the future. Data transfer technology. Cube or box Block chain of abstract financial data. Redis requires significantly less memory during write operations to store the same number of records as Memcached.

Cache 130
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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., on end-to-end latency) and less than 0.15% on throughput. This tracing system is similar to Dapper and Zipkin and records per-microservice latencies and number of outstanding requests. ASPLOS’19.

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Mastering Hybrid Cloud Strategy

Scalegrid

When delving into the networking aspect of a hybrid cloud deployment, complexities arise due to the requirement of linking or expanding existing on-premises network architectures into the cloud sphere. We will examine each of these elements in more detail.

Strategy 130