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

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

All Things Distributed

JUNE 21, 2018

The answer for me is simple: Developers want their applications to be well architected and scale effectively. The days of the one-size-fits-all monolithic database are behind us, and developers are now building highly distributed applications using a multitude of purpose-built databases. The opposite is true.

Database 167

Database AWS Games Latency 167

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

ACM Sigarch

DECEMBER 6, 2018

smart cameras & analytics) to interactive/immersive environments and autonomous driving (e.g. In a traditional visual analytics pipeline, we compress the data by exploiting the redundancies in time and space. For many IoT applications involving wireless video sensors (e.g. Quality vs Bandwidth.

Wireless 54

Wireless IoT Analytics Architecture 54

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., Amazon Redshift, DynamoDB, Amazon EMR) whereas insights need to be derived by not just developers but also non-technical business users.

Cloud 137

Cloud Big Data AWS Analytics 137

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. From Distributed Caches to Real-Time Digital Twins. For more than two decades, the answer to this challenge has proven to be a technology called in-memory computing.

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. From Distributed Caches to Real-Time Digital Twins. For more than two decades, the answer to this challenge has proven to be a technology called in-memory computing.

Ecommerce 52

Ecommerce Cache Scalability 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

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. Unfortunately, due to the lack of a good developer experience (DX) total cost of ownership of a traditional CMS and implementation failure rate remains all-time high.

Systems 63

Systems Cache Website Network 63