Dynatrace

AUGUST 10, 2021

Because here is a group of people who thrive on discovering new things, transforming workplaces, and innovating in the true sense of the word, every single day. Breakout Sessions on Scaling DevOps and SRE, Simplifying Kubernetes, Accelerating Cloud Native Innovation, and Delivering Perfect Experiences with Full Stack Observability.

DevOps 246

DevOps Innovation Big Data Cloud 246

Dynatrace

OCTOBER 4, 2022

Log management and analytics is an essential part of any organization’s infrastructure, and it’s no secret the industry has suffered from a shortage of innovation for several years. Several pain points have made it difficult for organizations to manage their data efficiently and create actual value.

Analytics 240

Analytics Artificial Intelligence Storage Serverless 240

Dynatrace

OCTOBER 17, 2022

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. CloudOps includes processes such as incident management and event management. The four stages of data processing.

DevOps 202

DevOps Artificial Intelligence Healthcare Innovation 202

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.” But what is AIOps, exactly? And how can it support your organization? What is AIOps? Two approaches to AIOps.

Artificial Intelligence 218

Artificial Intelligence DevOps Innovation Metrics 218

The Netflix TechBlog

MARCH 25, 2019

Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

The Netflix TechBlog

FEBRUARY 16, 2021

Membership Engineering at Netflix is responsible for the plan and pricing configurations for every market worldwide. However, with our rapid product innovation speed, the whole approach experienced significant challenges: Business Complexity: The existing SKU management solution was designed years ago when the engagement rules were simple?

Mobile 209

Mobile Engineering Infrastructure Scalability 209

Dynatrace

APRIL 25, 2023

Having access to large data sets can be helpful, but only if organizations are able to leverage insights from the information. These analytics can help teams understand the stories hidden within the data and share valuable insights. and only they have access.”

Analytics 206

Analytics Big Data Media Operating System 206

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

Dynatrace

JUNE 29, 2022

More than 90% of enterprises now rely on a hybrid cloud infrastructure to deliver innovative digital services and capture new markets. A hybrid cloud, however, combines public infrastructure and services with on-premises resources or a private data center to create a flexible, interconnected IT environment. Dynatrace news.

Infrastructure 187

Infrastructure Cloud Azure AWS 187

The Netflix TechBlog

SEPTEMBER 10, 2019

Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute. The two main challenges with this approach are establishing an easy contribution framework and handling Netflix’s scale of data.

Metrics 215

Metrics Architecture Infrastructure Innovation 215

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 137

Cloud Big Data AWS Analytics 137

All Things Distributed

NOVEMBER 15, 2016

Previously, I wrote about Amazon QuickSight , a new service targeted at business users that aims to simplify the process of deriving insights from a wide variety of data sources quickly, easily, and at a low cost. Put simply, data is not always readily available and accessible to organizational end users. Powered by Innovation.

Analytics 152

Analytics Availability Media Social Media 152

SQL Server According to Bob

FEBRUARY 15, 2018

Microsoft engineering is actually sending quite a few folks over the Atlantic to come talk about SQL Server 2017, SQL Server on Linux, GDPR, Performance, Security, Azure Data Lake, Azure SQL Database, Azure SQL Data Warehouse, and Azure CosmosDB. Best practices on Building a Big Data Analytics Solution – Michael Rys.

Engineering 40

Engineering Azure Best Practices Servers 40

All Things Distributed

JUNE 26, 2016

Market innovators and change agents need a comprehensive infrastructure platform that can reliably scale on-demand. Seamless ingestion of large volumes of sensed data. AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift.

AWS 90

AWS Cloud Healthcare Blockchain 90

All Things Distributed

DECEMBER 8, 2016

Canada has set forth a bold innovation agenda grounded in entrepreneurship, scientific research, growing small and medium-sized businesses with a focus on environmentally friendly technologies, and the transition to a digital economy. in the coming year. The Canadian opportunity. Rapid time to market.

AWS 155

AWS Cloud Lambda Innovation 155

ScaleOut Software

NOVEMBER 24, 2020

Competitive pressures should spark innovation in this area, and real-time digital twins can help. The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. Message-processing code can incorporate a rules engine or machine learning to amplify their capabilities.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Testsigma

FEBRUARY 12, 2021

The implementation of emerging technologies has helped improve the process of software development, testing, design and deployment. With all of these processes in place, cost optimization is also a high concern for organizations worldwide. This obligated QA engineers, in particular, to pay more attention to the user interface.

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

ACM Sigarch

APRIL 8, 2019

big-data processing, machine learning, quantum computing, and so on). ML and deep learning innovations are constantly in the news. Lena Olson is a Software Engineer at Google. . Disclaimer: Newsha is a Research Scientist at Baidu and Lena is a Software Engineer at Google.

Architecture 74

Architecture Open Source Hardware Software Engineering 74

All Things Distributed

SEPTEMBER 6, 2013

Shell leverages AWS for big data analytics to help achieve these goals. Shell''s scientists, especially the geophysicists and drilling engineers, frequently use cloud computing to run models. In addition, its robust architecture supports ten times as many scientists, all working simultaneously.

Cloud 129

Cloud Energy AWS Healthcare 129

All Things Distributed

NOVEMBER 30, 2016

Around 20 years ago, we used machine learning in our recommendation engine to generate personalized recommendations for our customers. There are many more application areas where we use ML extensively: search, autonomous drones, robotics in fulfillment centers, text processing and speech recognition (such as in Alexa) etc.

AWS 165

AWS Lambda Artificial Intelligence Mobile 165

All Things Distributed

NOVEMBER 15, 2010

Today Amazon Web Services takes another step on the continuous innovation path by announcing a new Amazon EC2 instance type: The Cluster GPU Instance. We believe that making these GPU resources available for everyone to use at low cost will drive new innovation in the application of highly parallel programming models. Comments ().

AWS 136

AWS Latency Programming Architecture 136

All Things Distributed

APRIL 27, 2011

We use high-performance transactions systems, complex rendering and object caching, workflow and queuing systems, business intelligence and data analytics, machine learning and pattern recognition, neural networks and probabilistic decision making, and a wide variety of other techniques. It is deeply integrated into everything we do.

Technology 151

Technology Technology AWS Storage 151

Rigor

SEPTEMBER 11, 2019

Reading time 16 min Whether you’re a web performance expert, an evangelist for the culture of performance, a web engineer incorporating performance into your process, or someone new to the web performance entirely, you probably identify as curious, excited about new ideas, and always learning. Rick Byers. Rick Byers.

Performance 89

Performance Education Google Website 89

All Things Distributed

MARCH 28, 2011

What used to be only available in physical formats now often has digital equivalents and this digitalization is driving great new innovations. I am excited about this because it is already making my digital music experience simpler and I am looking forward to the innovation that these services will drive on behalf of our customers.

AWS 138

AWS Cloud Storage Internet 138

ACM Sigarch

NOVEMBER 1, 2019

As is the case for many high-quality computer systems conferences, the papers presented here involve a significant amount of engineering and experimentation on real hardware to convincingly evaluate innovative concepts end-to-end in a realistic setting. ATC ’19 was refreshingly different.

Architecture 12

Architecture Hardware Cache Storage 12

Highly Scalable

MARCH 10, 2015

More specifically, the article was inspired by three major case studies from Albert Heijn [KOK07], the largest supermarket chain in the Netherlands, Zara [CA12], an international apparel retailer, and RueLaLa [JH14], an innovative online fashion retailer. At the same time, we avoid academic results with little or no empirical support.

Retail 152

Retail C++ Analytics Metrics 152