ScaleOut Software

JULY 27, 2018

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. Looking beyond distributed caching, it’s their ability to perform data-parallel analysis that gives IMDGs such exciting capabilities.

Scalability 40

Scalability Cache Airlines Healthcare 40

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 160

IoT Infrastructure Monitoring Innovation 160

ScaleOut Software

AUGUST 18, 2018

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., The digital twin model is worth a close look when designing the next generation of IoT applications.

IoT 40

IoT Ecommerce Analytics Latency 40

Dynatrace

APRIL 5, 2021

These include website hosting, database management, backup and restore, IoT capabilities, e-commerce solutions, app development tools and more, with new services released regularly. Lambda’s toolbox of automated processes helps developers streamline to build fast, robust, and scalable applications on accelerated timelines.

Lambda 178

Lambda AWS Serverless Hardware 178

Enprowess

JANUARY 12, 2022

Internet of Things (IoT). Serverless Architecture. According to Ericsson’s analysis, international mobile data traffic is projected to become 4.5 Internet of Things (IoT). IoT can be defined as a technology of interconnected devices where human involvement is not required for data transfer. How does IoT work?

Blockchain 59

Blockchain IoT Development Serverless 59

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

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. The list goes on.

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. The list goes on.

Analytics 52

Analytics IoT Lambda Big Data 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

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

MAY 4, 2018

For example, if an IoT application is attempting to detect whether data from a temperature sensor is predicting the failure of the medical freezer to which it is attached, it looks at patterns in the temperature changes, such as sudden spikes or a continuously upward trend, without regard to the freezer’s usage or service history.

Processing 40

Processing IoT Programming Software Engineering 40

ScaleOut Software

MAY 4, 2018

For example, if an IoT application is attempting to detect whether data from a temperature sensor is predicting the failure of the medical freezer to which it is attached, it looks at patterns in the temperature changes, such as sudden spikes or a continuously upward trend, without regard to the freezer’s usage or service history.

Processing 40

Processing IoT Programming Software Engineering 40

ScaleOut Software

MAY 19, 2020

But this approach is complex and leads to superficial analysis in real time, forcing telemetry to be logged into a data lake for later analysis using Spark or other tools. It then triggers application code to process the messages and gives it immediate access to memory-based contextual information for the data source.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

MAY 19, 2020

But this approach is complex and leads to superficial analysis in real time, forcing telemetry to be logged into a data lake for later analysis using Spark or other tools. It then triggers application code to process the messages and gives it immediate access to memory-based contextual information for the data source.

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