Remove Big Data Remove Data Remove Latency Remove Scalability
article thumbnail

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
article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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

The Netflix TechBlog

It can happen on an edge API system servicing customer devices, between the edge and mid-tier services, or from mid-tiers to data stores. The first phase involves validating functional correctness, scalability, and performance concerns and ensuring the new systems’ resilience before the migration.

Traffic 339
article thumbnail

QCon London: Lessons Learned From Building LinkedIn’s AI/ML Data Platform

InfoQ

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz

article thumbnail

The Need for Real-Time Device Tracking

ScaleOut Software

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. Unlike manual or automatic log queries, in-memory computing can continuously run analytics code on all incoming data and instantly find issues.

IoT 78
article thumbnail

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
article thumbnail

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. This enables customers to serve content to their end users with low latency, giving them the best application experience.

AWS 146