Architecture Big Data Event Latency 
Uber Engineering
OCTOBER 17, 2018
To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.
Highly Scalable
AUGUST 20, 2013
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.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
The Netflix TechBlog
MAY 4, 2023
When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. It provides a good read on the availability and latency ranges under different production conditions.
Abhishek Tiwari
DECEMBER 27, 2017
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.
Dynatrace
DECEMBER 15, 2022
This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. Performance. What does IT operations do?
The Morning Paper
MAY 14, 2019
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.
ScaleOut Software
JULY 19, 2021
Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives. This data is also periodically uploaded to a data lake for offline batch analysis that calculates key statistics and looks for big trends that can help optimize operations.
Expert insights. Personalized for you.
109 
Let's personalize your content