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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. Performance.

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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.

Big Data 154
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 339
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Expanding the Cloud – An AWS Region is coming to Hong Kong

All Things Distributed

The new region will give Hong Kong-based businesses, government organizations, non-profits, and global companies with customers in Hong Kong, the ability to leverage AWS technologies from data centers in Hong Kong. The new AWS Asia Pacific (Hong Kong) Region will have three Availability Zones and be ready for customers for use in 2018.

AWS 146
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Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. even lowered the latency by introducing a multi-headed device that collapses switches and memory controllers.

Latency 52
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Expanding the Cloud ? introducing the Asia Pacific (Sydney) Region.

All Things Distributed

Werner Vogels weblog on building scalable and robust distributed systems. This new Asia Pacific (Sydney) Region has been highly requested by companies worldwide, and it provides low latency access to AWS services for those who target customers in Australia and New Zealand. All Things Distributed. Expanding the Cloud â?? Comments ().

Cloud 117
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What is a Distributed Storage System

Scalegrid

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. Variations within these storage systems are called distributed file systems.

Storage 130