Dynatrace

DECEMBER 22, 2019

AWS offers a broad set of global, cloud-based services including computing, storage, networking, Internet of Things (IoT), and many others. You can use these services in combinations that are tailored to help your business move faster, lower IT costs, and support scalability. Amazon Kinesis Data Analytics. Dynatrace news.

AWS 133

AWS Metrics IoT Storage 133

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. 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. The list goes on.

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. Because real-world IoT applications can track thousands of devices or other entities (e.g., The higher-level digital twins continue to run in the cloud or on-premises – wherever the required computing resources are located.

IoT 45

IoT Ecommerce Analytics Latency 45

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 higher-level digital twins continue to run in the cloud or on-premises – wherever the required computing resources are located.

IoT 40

IoT Ecommerce Analytics Latency 40

ScaleOut Software

MAY 19, 2020

Today ScaleOut Software announces the release of its ground-breaking cloud service for streaming analytics using the real-time digital twin model. Traditional platforms for streaming analytics attempt to look at the entire telemetry pipeline using techniques such as SQL query to uncover and act on patterns of interest.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

MAY 19, 2020

Today ScaleOut Software announces the release of its ground-breaking cloud service for streaming analytics using the real-time digital twin model. Traditional platforms for streaming analytics attempt to look at the entire telemetry pipeline using techniques such as SQL query to uncover and act on patterns of interest.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

JUNE 22, 2020

Going back to the mid-1990s, online systems have seen relentless, explosive growth in usage, driven by ecommerce, mobile applications, and more recently, IoT. It can also take advantage of the elastic computing resources available in cloud infrastructures to quickly and cost-effectively scale throughput to meet changes in demand.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JUNE 22, 2020

Going back to the mid-1990s, online systems have seen relentless, explosive growth in usage, driven by ecommerce, mobile applications, and more recently, IoT. It can also take advantage of the elastic computing resources available in cloud infrastructures to quickly and cost-effectively scale throughput to meet changes in demand.

Ecommerce 52

Ecommerce Cache Scalability IoT 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. With the real-time digital twin model, the next generation of streaming analytics has arrived.

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. With the real-time digital twin model, the next generation of streaming analytics has arrived.

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. With the real-time digital twin model, the next generation of streaming analytics has arrived.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

DECEMBER 10, 2019

In its usage in streaming analytics, a real-time digital twin hosts an application-defined method for analyzing event messages from a single data source combined with an associated data object: The data object holds dynamic, contextual information about a single data source and the evolving results derived from analyzing incoming telemetry.

Code 52

Code Analytics Healthcare IoT 52

ScaleOut Software

DECEMBER 10, 2019

In its usage in streaming analytics, a real-time digital twin hosts an application-defined method for analyzing event messages from a single data source combined with an associated data object: The data object holds dynamic, contextual information about a single data source and the evolving results derived from analyzing incoming telemetry.

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 63

Programming IoT Java Analytics 63

ScaleOut Software

DECEMBER 10, 2019

In its usage in streaming analytics, a real-time digital twin hosts an application-defined method for analyzing event messages from a single data source combined with an associated data object: The data object holds dynamic, contextual information about a single data source and the evolving results derived from analyzing incoming telemetry.

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

Apica

NOVEMBER 15, 2019

including iPhones/ mobile devices, set-top boxes, game stations, and IoT devices. Support for capacity planning, including “prescale clustering” –The Cloud, by definition, has no capacity limit, and many organizations rely on dynamic scaling to address usage spikes.

Performance Testing 40

Performance Testing Testing Performance Games 40

All Things Distributed

DECEMBER 13, 2016

The British Government is also helping to drive innovation and has embraced a cloud-first policy for technology adoption. 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.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

OCTOBER 7, 2015

QuickSight is a fast, cloud native, scalable, business intelligence service for the 1/10th the cost of old-guard BI solutions. QuickSight is a cloud-native BI service built from the ground up to address the big data challenges around speed, complexity, and cost. Big data challenges. Enter Amazon QuickSight.

Cloud 137

Cloud Big Data AWS Analytics 137

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., Also, you can choose to program post-commit actions, such as running aggregate analytical functions or updating other dependent tables.

Database 167

Database Lambda AWS IoT 167

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

Abhishek Tiwari

NOVEMBER 3, 2018

Most of the CMS vendors dodge questions of evolution by talking about incremental innovation primarily focused on customer experience (CX) such as analytics and personalisation. Although some vendors have added support for APIs and cloud services most have not even bothered to adapt with changing technology landscape.

Systems 63

Systems Cache Website Network 63