ScaleOut Software

AUGUST 18, 2018

Digital twins are software abstractions that track the behavior of individual devices in IoT applications. They combine an event handling function with state information about each device. Also, the use of digital twins provides automatic correlation of incoming events for each device, thereby simplifying applications.

IoT 45

IoT Ecommerce Analytics Latency 45

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. Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives.

IoT 78

IoT Analytics Big Data Architecture 78

ScaleOut Software

AUGUST 18, 2018

Digital twins are software abstractions that track the behavior of individual devices in IoT applications. They combine an event handling function with state information about each device. Also, the use of digital twins provides automatic correlation of incoming events for each device, thereby simplifying applications.

IoT 40

IoT Ecommerce Analytics Latency 40

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. Many events can trigger a lambda function. What is AWS Lambda? The Amazon Web Services ecosystem. A new record entering a database table.

Lambda 184

Lambda AWS Serverless Hardware 184

Dynatrace

AUGUST 28, 2020

With the acceleration of complexity, scale, and dynamic systems architectures, under-resourced IT teams are under increasing pressure to understand when there is abnormal behavior, identify the precise reason why this occurred, quickly remediate the issue, and prevent this behavior in the future. Dynatrace news.

IoT 162

IoT Infrastructure Monitoring Innovation 162

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

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

Enprowess

SEPTEMBER 7, 2021

uses an event-driven and non-blocking I/O model, so it is practical and lightweight. The technology develops single-page applications , websites, and backend API services designed with real-time and push-based architectures. IoT-based applications. Scalability: Applications developed with Node.js Use cases of Node.js

Open Source 52

Open Source Virtualization Programming Servers 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

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

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

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