All Things Distributed

JUNE 10, 2016

The Real-time File Processing reference architecture is a general-purpose, event-driven, parallel data processing architecture that uses AWS Lambda. This simple architecture is described in the "Fanout S3 Event Notifications to Multiple Endpoints" blog post on the AWS Compute Blog. IoT Backend Serverless Reference Architecture.

Lambda 107

Lambda Serverless AWS Architecture 107

All Things Distributed

MAY 10, 2016

The Real-time File Processing reference architecture is a general-purpose, event-driven, parallel data processing architecture that uses AWS Lambda. This simple architecture is described in the "Fanout S3 Event Notifications to Multiple Endpoints" blog post on the AWS Compute Blog. IoT Backend Serverless Reference Architecture.

Lambda 105

Lambda Serverless AWS Architecture 105

All Things Distributed

JUNE 20, 2017

After the launch of the AWS APAC (Hong Kong) Region, there will be 19 Availability Zones in Asia Pacific for customers to build flexible, scalable, secure, and highly available applications. In 2010, we opened our first AWS Region in Singapore and since then have opened additional regions: Japan, Australia, China, Korea, and India.

AWS 146

AWS Logistics Cloud Social Media 146

ScaleOut Software

JULY 14, 2021

The population of intelligent IoT devices is exploding, and they are generating more telemetry than ever. The Microsoft Azure IoT ecosystem offers a rich set of capabilities for processing IoT telemetry, from its arrival in the cloud through its storage in databases and data lakes.

IoT 52

IoT Azure Analytics Serverless 52

ScaleOut Software

JANUARY 9, 2020

It’s not enough just to pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark. What’s needed is the ability to easily track incoming telemetry from each individual store so that issues can be quickly analyzed, prioritized, and handled.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

It’s not enough just to pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark. What’s needed is the ability to easily track incoming telemetry from each individual store so that issues can be quickly analyzed, prioritized, and handled.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

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. 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.

Logistics 52

Logistics Retail Analytics Azure 52

Apica

NOVEMBER 15, 2019

To keep up with the testing demand there are a number of feature requirements needed in order to be called a modern performance testing platform: Mega-scale load testing — load testing should scale up to millions of users within seconds to emulate the speed and scale of virtually any high-profile event worldwide.

Performance Testing 40

Performance Testing Testing Performance Games 40

Apica

OCTOBER 29, 2019

including iPhones/ mobile devices, set-top boxes, game stations, and IoT devices. Mega-scale load testing –load testing should scale up to millions of users within seconds to emulate the speed and scale of virtually any high-profile event worldwide.

Performance Testing 40

Performance Testing Testing Performance Software 40

All Things Distributed

JULY 14, 2015

A more scalable option is to decouple these systems and build a pipe that connects these engines and feeds all change records from the source database to the data warehouse (e.g., Triggers are powerful mechanisms that react to events dynamically and in real time. Amazon Redshift) and Elasticsearch machines.

Database 167

Database Lambda AWS IoT 167

Dynatrace

OCTOBER 11, 2021

Scalability is a major feature of GCF. The Cloud Functions platform enables teams to craft functional, extensible applications that run code when triggered by preset conditions or events. GCF also has relevance in IoT and file processing tasks. How Google Cloud Functions works. Avoid lock-in with open-source technologies.

Google 222

Google Cloud Serverless Azure 222

All Things Distributed

APRIL 9, 2015

Amazon ML is highly scalable and can generate billions of predictions, and serve those predictions in real-time and at high throughput. When we designed Amazon EFS we decided to build along the AWS principles: Elastic, scalable, highly available, consistent performance, secure, and cost-effective.

Lambda 122

Lambda Cloud IoT AWS 122

Dynatrace

OCTOBER 1, 2021

Many organizations also adopt an observability solution to help them detect and analyze the significance of events to their operations, software development life cycles, application security, and end-user experiences. Making observability actionable and scalable for IT teams.

Metrics 363

Metrics Open Source Monitoring Infrastructure 363

ScaleOut Software

SEPTEMBER 20, 2018

This model organizes key information about each data source (for example, an IoT device, e-commerce shopper, or medical patient) in a software component that tracks the data source’s evolving state and encapsulates algorithms, such as predictive analytics, for interpreting that state and generating real-time feedback.

Programming 63

Programming IoT Java Analytics 63

Scalegrid

MAY 13, 2020

Greenplum uses an MPP database design that can help you develop a scalable, high performance deployment. This combination helps you improve the parallelism, scalability, and predictive accuracy of your Greenplum machine learning deployment. At a glance – TLDR. The Greenplum Architecture. Greenplum Advantages.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

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. Real-time monitoring and evaluation of events have led to a positive impact on performance or operations.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

ScaleOut Software

MAY 19, 2020

A second and equally daunting challenge for live systems is to maintain real-time situational awareness about the state of all data sources so that strategic responses can be implemented, especially when a rapid sequence of events is unfolding.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

MAY 19, 2020

A second and equally daunting challenge for live systems is to maintain real-time situational awareness about the state of all data sources so that strategic responses can be implemented, especially when a rapid sequence of events is unfolding.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

SEPTEMBER 20, 2018

This model organizes key information about each data source (for example, an IoT device, e-commerce shopper, or medical patient) in a software component that tracks the data source’s evolving state and encapsulates algorithms, such as predictive analytics, for interpreting that state and generating real-time feedback.

Programming 40

Programming IoT Java Analytics 40

ScaleOut Software

DECEMBER 10, 2019

Event streams typically combine messages from many data sources, as shown below. 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. We invite you to check it out.

Code 52

Code Analytics Healthcare IoT 52

ScaleOut Software

DECEMBER 10, 2019

Event streams typically combine messages from many data sources, as shown below. 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. We invite you to check it out.

Code 52

Code Analytics Healthcare IoT 52

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

DECEMBER 10, 2019

Event streams typically combine messages from many data sources, as shown below. 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. We invite you to check it out.

Code 52

Code Analytics Healthcare IoT 52

Enprowess

SEPTEMBER 7, 2021

uses an event-driven and non-blocking I/O model, so it is practical and lightweight. IoT-based applications. Scalability: Applications developed with Node.js Developers are able to create scalable and fast apps suitable for all platforms due to its “learn once write anywhere” principle. Developing API. Where to Use React.

Open Source 52

Open Source Virtualization Programming Servers 52