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

Dynatrace

OCTOBER 25, 2022

Organizations adopt DevOps, where developers and operations work together in a continuous loop, so they can develop software and resolve issues efficiently before they affect users. He meant that more and more developers are now becoming responsible for operations, and operations are becoming ingrained in developers’ job descriptions.

DevOps 213

DevOps Big Data Cloud Innovation 213

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Like the development and design phases, these applications generate massive data volumes that offer relevant and actionable insights.

Artificial Intelligence 282

Artificial Intelligence Cloud Innovation Strategy 282

Dynatrace

DECEMBER 15, 2022

ITOps refers to the process of acquiring, designing, deploying, configuring, and maintaining equipment and services that support an organization’s desired business outcomes. Adding application security to development and operations workflows increases efficiency. CloudOps teams are one step further in the digital supply chain.

Artificial Intelligence 198

Artificial Intelligence DevOps Hardware Virtualization 198

The Netflix TechBlog

MARCH 2, 2021

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207

Analytics C++ Innovation Engineering 207

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. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191

Analytics Traffic Big Data Efficiency 191

Dynatrace

JUNE 1, 2020

Go faster, deliver consistently better results, with less team friction that you ever thought possible, as Dynatrace combines a unified data platform with advanced analytics to provide a single source of truth for your Biz, Dev and Ops teams. User Experience and Business Analytics ery user journey and maximize business KPIs.

Artificial Intelligence 181

Artificial Intelligence Social Media Monitoring IoT 181

Dynatrace

JUNE 1, 2020

Go faster, deliver consistently better results, with less team friction that you ever thought possible, as Dynatrace combines a unified data platform with advanced analytics to provide a single source of truth for your Biz, Dev and Ops teams. User Experience and Business Analytics ery user journey and maximize business KPIs.

Monitoring 132

Monitoring Performance Social Media Artificial Intelligence 132

Dynatrace

OCTOBER 14, 2021

Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” A comprehensive, modern approach to AIOps is a unified platform that encompasses observability, AI, and analytics.

Artificial Intelligence 208

Artificial Intelligence DevOps Innovation Metrics 208

Testsigma

FEBRUARY 12, 2021

The implementation of emerging technologies has helped improve the process of software development, testing, design and deployment. 38% of organisations were expected to introduce machine-learning initiatives in 2019, according to the Capgemini World Efficiency survey. Many changes are rendered through automated testing. from $12.6

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

The Morning Paper

SEPTEMBER 19, 2019

Could it be Analyzing efficient stream processing on modern hardware ? Hyper Dimension Shuffle describes how Microsoft improved the cost of data shuffling, one of the most costly operations, in their petabyte-scale internal big data analytics platform, SCOPE. for machine generated emails sent to humans).

Blockchain 46

Blockchain Hardware Google Analytics 46

All Things Distributed

DECEMBER 20, 2017

The founders had noticed that in many companies, product designers worked in a very detached manner from the rest of production. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster. Value creation through data.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

Percona

JANUARY 14, 2019

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node).

Database 40

Database Analytics Blockchain Healthcare 40

All Things Distributed

JUNE 29, 2011

AWS also applies the same customer oriented pricing strategy: as the AWS platform grows, our scale enables us to operate more efficiently, and we choose to pass the benefits back to customers in the form of cost savings. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications. Expanding the Cloud â??

AWS 104

AWS Retail Innovation Strategy 104

KeyCDN

OCTOBER 2, 2018

Codrops Codrops features blogs with topics ranging from UI design and page animations to image formatting and general JavaScript practices. A List Apart A List Apart focuses on UX and branding from business and design-oriented perspectives. Topics include web design, security, web-based tools and workflows and more.

Development 75

Development Website Design Code 75

Highly Scalable

MARCH 10, 2015

Although there are many books on data mining in general and its applications to marketing and customer relationship management in particular [BE11, AS14, PR13 etc.], The rest of the article is organized as follows: We first introduce a simple framework that ties together a retailer’s actions, profits and data. Propensity to churn.

Retail 152

Retail C++ Analytics Metrics 152

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. DBA needs to add all the negative and boundary value conditions as well in test data for testing.

Testing 60

Testing Storage Database Processing 60

The Netflix TechBlog

NOVEMBER 20, 2023

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

The Netflix TechBlog

APRIL 29, 2019

Various software systems are needed to design, build, and operate this CDN infrastructure, and a significant number of them are written in Python. Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud.

Open Source 279

Open Source Network Infrastructure Big Data 279

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.

Latency 243

Latency Storage Big Data Tuning 243

All Things Distributed

DECEMBER 13, 2009

The broad Amazon EC2 customer base brings such diversity in workload and utilization patterns that it allows us to operate Amazon EC2 with extreme efficiency. A highly efficient purchasing model such as Spot Instances is another way in which Amazon EC2 customers benefit from the unique economies of scale found in AWS Infrastructure Services.

Cloud 63

Cloud AWS Storage Innovation 63

All Things Distributed

DECEMBER 13, 2016

With the launch of the AWS Europe (London) Region, AWS can enable many more UK enterprise, public sector and startup customers to reduce IT costs, address data locality needs, and embark on rapid transformations in critical new areas, such as big data analysis and Internet of Things. Fraud.net is a good example of this.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

Smashing Magazine

JULY 29, 2019

I hope to raise the profile of difficulties faced by real people, which are avoidable if we design and develop in a way that is sympathetic to their needs. Notice the async keyword on the Google analytics script? Google analytics has ‘low’ priority. Read “ Efficiently load JavaScript with defer and async ” for more information.

Cache 96

Cache Google Mobile Network 96

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. 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.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

All Things Distributed

NOVEMBER 15, 2010

These trade-offs have even impacted the way the lowest level building blocks in our computer architectures have been designed. Graphics processing is one such area with huge computational requirements, but where each of the tasks is relatively small and often a set of operations are performed on data in the form of a pipeline.

AWS 136

AWS Latency Programming Architecture 136