Dynatrace

APRIL 5, 2021

AWS Lambda is a serverless compute service that can run code in response to predetermined events or conditions and automatically manage all the computing resources required for those processes. Real-time file processing, for quickly indexing files, processing logs, and validating content. The Amazon Web Services ecosystem.

Lambda 173

Lambda AWS Serverless Hardware 173

ScaleOut Software

JULY 19, 2021

We are increasingly surrounded by intelligent IoT devices, which have become an essential part of our lives and an integral component of business and industrial infrastructures. In-memory computing has the speed and scalability needed to generate responses within milliseconds, and it can evaluate and report aggregate trends every few seconds.

IoT 78

IoT Analytics Big Data Architecture 78

ScaleOut Software

MAY 4, 2018

Traditional stream-processing and complex event processing systems, such as Apache Storm and Software AG’s Apama , have focused on extracting interesting patterns from incoming data with stateless applications. Michigan) in 2002 for use in product life cycle management, it was recently popularized for IoT by Gartner in a 2017 report.

Processing 40

Processing IoT Programming Software Engineering 40

ScaleOut Software

MAY 4, 2018

Traditional stream-processing and complex event processing systems, such as Apache Storm and Software AG’s Apama , have focused on extracting interesting patterns from incoming data with stateless applications. Michigan) in 2002 for use in product life cycle management, it was recently popularized for IoT by Gartner in a 2017 report.

Processing 40

Processing IoT Programming Software Engineering 40

ScaleOut Software

AUGUST 18, 2018

In previous blogs , we have explored the power of the digital twin model for stateful stream-processing. Digital twins are software abstractions that track the behavior of individual devices in IoT applications. Because real-world IoT applications can track thousands of devices or other entities (e.g.,

IoT 40

IoT Ecommerce Analytics Latency 40

Dynatrace

AUGUST 28, 2020

Data silos – Multiple agents, disparate data sources, and siloed monitoring tools make it hard to understand interdependencies across applications, multiple clouds, and digital channels such as web, mobile, and IoT. Making observability actionable and scalable for IT teams.

IoT 158

IoT Infrastructure Monitoring Innovation 158

ScaleOut Software

SEPTEMBER 20, 2018

These are exciting times in the evolution of stream-processing. As we have seen in previous blogs , the digital twin model offers a breakthrough approach to structuring stateful stream-processing applications. It represents a big step forward for building stream-processing applications.

Programming 63

Programming IoT Java Analytics 63

All Things Distributed

JUNE 10, 2016

The notes are stored in Amazon DynamoDB, and are processed asynchronously using DynamoDB streams and a Lambda function to add them to an Amazon CloudSearch domain. Real-time File Processing Serverless Reference Architecture. IoT Backend Serverless Reference Architecture.

Lambda 107

Lambda Serverless AWS Architecture 107

All Things Distributed

MAY 10, 2016

The notes are stored in Amazon DynamoDB, and are processed asynchronously using DynamoDB streams and a Lambda function to add them to an Amazon CloudSearch domain. Real-time File Processing Serverless Reference Architecture. IoT Backend Serverless Reference Architecture.

Lambda 105

Lambda Serverless AWS Architecture 105

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

Enprowess

JANUARY 12, 2022

Internet of Things (IoT). Convenient Debugging : Debugging process is easy for the developers as single page application SPA offer developers tools. According to Ericsson’s analysis, international mobile data traffic is projected to become 4.5 Internet of Things (IoT). How does IoT work? AI-powered Chatbots.

Blockchain 59

Blockchain IoT Development Serverless 59

Apica

APRIL 11, 2023

Businesses today use automation for everything from payroll to report generation, and they often turn to a robotic process automation (RPA) system to help. You also want a highly scalable automation solution. Scalable testing helps businesses check performance for thousands of users or even millions.

IoT 52

IoT Benchmarking Scalability Network 52

ScaleOut Software

DECEMBER 10, 2019

For these reasons, most streaming applications only perform rudimentary analysis (often in the form of queries) on the incoming data stream and push most of the event messages into a data lake for offline examination. Others include fleet and traffic management, healthcare, financial services, IoT, and e-commerce recommendations.

Code 52

Code Analytics Healthcare IoT 52

ScaleOut Software

DECEMBER 10, 2019

For these reasons, most streaming applications only perform rudimentary analysis (often in the form of queries) on the incoming data stream and push most of the event messages into a data lake for offline examination. Others include fleet and traffic management, healthcare, financial services, IoT, and e-commerce recommendations.

Code 52

Code Analytics Healthcare IoT 52

ScaleOut Software

DECEMBER 10, 2019

For these reasons, most streaming applications only perform rudimentary analysis (often in the form of queries) on the incoming data stream and push most of the event messages into a data lake for offline examination. Others include fleet and traffic management, healthcare, financial services, IoT, and e-commerce recommendations.

Code 52

Code Analytics Healthcare IoT 52

ScaleOut Software

SEPTEMBER 20, 2018

These are exciting times in the evolution of stream-processing. As we have seen in previous blogs , the digital twin model offers a breakthrough approach to structuring stateful stream-processing applications. It represents a big step forward for building stream-processing applications.

Programming 40

Programming IoT Java Analytics 40

ScaleOut Software

MAY 19, 2020

A major challenge for stream-processing applications that track numerous data sources in real time is to analyze telemetry relevant to each specific data source and combine this with dynamic, contextual information about the data source to enable immediate action when necessary. Sign up to use the service here.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

MAY 19, 2020

A major challenge for stream-processing applications that track numerous data sources in real time is to analyze telemetry relevant to each specific data source and combine this with dynamic, contextual information about the data source to enable immediate action when necessary. Sign up to use the service here.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

Traditional platforms for streaming analytics don’t offer the combination of granular data tracking and real-time aggregate analysis that logistics applications in operational environments such as these require.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

Traditional platforms for streaming analytics don’t offer the combination of granular data tracking and real-time aggregate analysis that logistics applications in operational environments such as these require.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

Traditional platforms for streaming analytics don’t offer the combination of granular data tracking and real-time aggregate analysis that logistics applications such as these require. It’s not enough to just pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark.

Logistics 52

Logistics Retail Analytics Azure 52

Dynatrace

JUNE 13, 2019

Data analysis within large and highly dynamic microservices environments is the biggest challenge that Application Performance Monitoring (APM) vendors face today. The real challenge is robust and effective analysis of such data. Why are we doing this? What Dynatrace will contribute.

IoT 44

IoT C++ Analytics Java 44

All Things Distributed

DECEMBER 13, 2016

The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes. Take GoSquared , a UK startup that runs all its development and production processes on AWS, as an example.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

APRIL 9, 2015

The Amazon ML console and API provide data and model visualization tools, as well as wizards to guide you through the process of creating machine learning models, measuring their quality and fine-tuning the predictions to match your application requirements.

Lambda 122

Lambda Cloud IoT AWS 122

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g., Big data challenges.

Cloud 137

Cloud Big Data AWS Analytics 137

All Things Distributed

JUNE 21, 2018

As I have talked about before, one of the reasons why we built Amazon DynamoDB was that Amazon was pushing the limits of what was a leading commercial database at the time and we were unable to sustain the availability, scalability, and performance needs that our growing Amazon.com business demanded. The opposite is true.

Database 167

Database AWS Games Latency 167