Scalegrid

JANUARY 12, 2024

This article analyzes cloud workloads, delving into their forms, functions, and how they influence the cost and efficiency of your cloud infrastructure. Storage is a critical aspect to consider when working with cloud workloads. This opens up possibilities not only difficult but almost impossible to attain conventionally!

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

SEPTEMBER 13, 2023

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 240

AWS Efficiency Azure Cloud 240

Scalegrid

MARCH 14, 2024

In practice, a hybrid cloud operates by melding resources and services from multiple computing environments, which necessitates effective coordination, orchestration, and integration to work efficiently. Tailoring resource allocation efficiently ensures faster application performance in alignment with organizational demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., When each of those use cases is powered by a dedicated back-end, investments in better performance, improved scalability and efficiency etc. Oh, and in additional to low latency, “ we require access to fresh data.” VLDB’19.

Analytics 75

Analytics Latency Cache Google 75

The Netflix TechBlog

OCTOBER 27, 2020

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The data warehouse is not designed to serve point requests from microservices with low latency. Moving data with Bulldozer at Netflix.

Latency 243

Latency Storage Big Data Tuning 243

The Netflix TechBlog

OCTOBER 19, 2020

If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage.

Infrastructure 233

Infrastructure Transportation Storage Open Source 233

Percona

JUNE 22, 2023

We will also discuss related configuration variables to consider that can impact these KPIs, helping you gain a comprehensive understanding of your MySQL server’s performance and efficiency. Query performance Query performance is a key performance indicator (KPI) in MySQL, as it measures the efficiency and speed of query execution.

Performance 104

Performance Monitoring Traffic Database 104

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . By transparently distributing stored objects across a cluster of servers (physical or virtual), it automatically scales performance for fast-growing workloads and maintains consistently low access latency. In-Memory Data Grids for Fast-Changing Data.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . By transparently distributing stored objects across a cluster of servers (physical or virtual), it automatically scales performance for fast-growing workloads and maintains consistently low access latency. In-Memory Data Grids for Fast-Changing Data.

Analytics 52

Analytics Airlines Cache Scalability 52

Dynatrace

DECEMBER 15, 2022

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. The primary goal of ITOps is to provide a high-performing, consistent IT environment.

Artificial Intelligence 195

Artificial Intelligence DevOps Hardware Virtualization 195

The Netflix TechBlog

SEPTEMBER 2, 2020

Edgar helps Netflix teams troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata. In one request hitting just ten services, there might be ten different analytics dashboards and ten different log stores. What is Edgar?

Latency 296

Latency Transportation Engineering Traffic 296

Dynatrace

APRIL 26, 2022

Digital experience monitoring enables companies to respond to issues more efficiently in real time, and, through enrichment with the right business data, understand how end-user experience of their digital products significantly affects business key performance indicators (KPIs). Endpoint monitoring (EM). Endpoints can be physical (i.e.,

Monitoring 196

Monitoring Social Media IoT Metrics 196

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. bits per unique value.

Analytics 191

Analytics Traffic Big Data Efficiency 191

Dotcom-Montior

OCTOBER 6, 2021

Site reliability engineers are focused with creating scalable and reliable software systems, so this also includes ensuring that development work is efficient and reliable, so when the finished product is ready for production, there are no surprises. that are required to keep the software deployments live are running efficiently.

Engineering 67

Engineering DevOps Monitoring Google 67

ACM Sigarch

DECEMBER 6, 2018

smart cameras & analytics) to interactive/immersive environments and autonomous driving (e.g. Each of these categories opens up challenging problems in AI/visual algorithms, high-density computing, bandwidth/latency, distributed systems. cameras) in many usages ranging from digital security/surveillance and automated retail (e.g.

Wireless 54

Wireless IoT Analytics Architecture 54

Abhishek Tiwari

JULY 12, 2018

It can be used to power new analytics, insight, and product features. It can be used to power new analytics, insight, and product features. These nodes and edges require a good amount of compute and storage which is typically distributed across a large number servers either running in the cloud or your own data center.

Latency 63

Latency Analytics Scalability Engineering 63

Abhishek Tiwari

DECEMBER 27, 2017

It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake. Delta implements the unified data management layer by extending the Amazon S3 object storage for ACID transactions and automatic data indexing.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

Testsigma

FEBRUARY 12, 2021

The usage by advanced techniques such as RPA, Artificial Intelligence, machine learning and process mining is a hyper-automated application that improves employees and automates operations in a way which is considerably more efficient than conventional automation. Gartner’s 2020 projections first included the trend of hyperautomation.

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

ScaleOut Software

MAY 4, 2014

Usually this means moving to a server with more CPU cores, greater memory capacity, and higher-end networking and storage options. Now CPU, memory, and storage resources can grow without predefined limits. At some point, scaling up becomes costly, and workloads grow beyond what even a high end server can handle.

Network 40

Network Servers Analytics Storage 40

Highly Scalable

AUGUST 20, 2013

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. The pipelines can be stateful and the engine’s middleware should provide a persistent storage to enable state checkpointing. Interoperability with Hadoop. Pipelining.

Big Data 154

Big Data Processing Lambda Database 154

Abhishek Tiwari

NOVEMBER 3, 2018

Most of the CMS vendors dodge questions of evolution by talking about incremental innovation primarily focused on customer experience (CX) such as analytics and personalisation. Alternatively, you can upload output directory to cloud object/blob storage such as Amazon S3 or Azure Blob Storage and serve your site from there.

Systems 63

Systems Cache Website Network 63

ScaleOut Software

MAY 15, 2014

For example, the IMDG must be able to efficiently create millions of objects in each server to make use of its huge storage capacity. Likewise, object access paths must be heavily multi-threaded and avoid lock contention to minimize access latency and maximize throughput.

Cache 40

Cache Servers Network Design 40

Smashing Magazine

JANUARY 11, 2021

This guide has been kindly supported by our friends at LogRocket , a service that combines frontend performance monitoring , session replay, and product analytics to help you build better customer experiences. Study common complaints coming into customer service and sales team, study analytics for high bounce rates and conversion drops.

Performance 145

Performance Cache Media Metrics 145

Smashing Magazine

JANUARY 6, 2020

Study common complaints coming into customer service and sales team, study analytics for high bounce rates and conversion drops. Run performance experiments and measure outcomes — both on mobile and on desktop (for example, with Google Analytics ). Yet often, analytics alone doesn’t provide a complete picture.

Performance 145

Performance Cache Network Metrics 145

Smashing Magazine

JANUARY 7, 2019

To get accurate results and goals though, first study your analytics to see what your users are on. Estimated Input Latency tells us if we are hitting that threshold, and ideally, it should be below 50ms. In exchange, your team gains maintainability and developer efficiency, of course.

Performance 103

Performance Cache Metrics Network 103

All Things Distributed

JUNE 21, 2018

Use cases such as gaming, ad tech, and IoT lend themselves particularly well to the key-value data model where the access patterns require low-latency Gets/Puts for known key values. The purpose of DynamoDB is to provide consistent single-digit millisecond latency for any scale of workloads.

Database 167

Database AWS Games Latency 167

Alex Russell

JULY 14, 2021

Here, native apps are doing work related to their core function; storage and tracking of user data are squarely within the four corners of the app's natural responsibilities. iOS's security track record, patch velocity, and update latency for its required-use engine is not best-in-class. How can that be?

Google 121

Google Mobile Engineering Internet 121

All Things Distributed

OCTOBER 12, 2016

Three years ago, as part of our AWS Fast Data journey we introduced Amazon ElastiCache for Redis , a fully managed in-memory data store that operates at sub-millisecond latency. ElastiCache for Redis Multi-AZ capability is built to handle any failover case for Redis Cluster with robustness and efficiency. Building upon Redis.

Scalability 130

Scalability Analytics Cache AWS 130

All Things Distributed

JANUARY 18, 2012

Amazon DynamoDB offers low, predictable latencies at any scale. In response, we began to develop a collection of storage and database technologies to address the demanding scalability and reliability requirements of the Amazon.com ecommerce platform. s read latency, particularly as dataset sizes grow. The growth of Amazonâ??s

Scalability 167

Scalability Database Ecommerce Latency 167

All Things Distributed

NOVEMBER 15, 2010

For example, the most fundamental abstraction trade-off has always been latency versus throughput. Modern CPUs strongly favor lower latency of operations with clock cycles in the nanoseconds and we have built general purpose software architectures that can exploit these low latencies very well.Â

AWS 136

AWS Latency Programming Architecture 136