All Things Distributed

MARCH 11, 2015

Many of these innovations will have a significant analytics component or may even be completely driven by it. For example many of the Internet of Things innovations that we have seen come to life in the past years on AWS all have a significant analytics components to it. Cloud analytics are everywhere.

Analytics 135

Analytics Cloud Healthcare Retail 135

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. Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (KDS). Amazon Simple Storage Service (S3). Dynatrace news.

AWS 136

AWS Metrics IoT Storage 136

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. Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (KDS). Amazon Simple Storage Service (S3). Dynatrace news.

AWS 133

AWS Metrics IoT Storage 133

Dynatrace

SEPTEMBER 30, 2021

AWS Certified Machine Learning – Specialty: Data scientists or software developers who already have some exposure to machine learning in AWS may find this certification worthwhile. Data analytics. However, AWS recommends getting the AWS Certified Cloud Practitioner certificate or an equivalent Associate-level cert beforehand.

AWS 209

AWS Best Practices Analytics Cloud 209

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. 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

Testsigma

FEBRUARY 12, 2021

When more companies transition into digital-first projects, there must be an expanded number of processes and IT data departments to keep IT teams on track. IoT Test Automation. The Internet of Things is generally referred to as IoT which encompasses computers, cars, houses or some other technological system related.

Artificial Intelligence 92

Artificial Intelligence Software Software IoT 92

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. Analyzing visual data is predominantly accomplished using deep learning. Apart from putting unprecedented strain on the computing subsystems, such workloads are heavy on data usage requiring a new way to analyze, store and retrieve data.

Wireless 54

Wireless IoT Analytics Architecture 54

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. The pace of these changes has made it challenging for server-based infrastructures to manage fast-growing populations of users and data sources while maintaining fast response times.

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. The pace of these changes has made it challenging for server-based infrastructures to manage fast-growing populations of users and data sources while maintaining fast response times.

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

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 Peterborough City Council as an example.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

OCTOBER 7, 2015

We live in a world where massive volumes of data are generated from websites, connected devices and mobile apps. In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data.

Cloud 137

Cloud Big Data AWS Analytics 137

All Things Distributed

JUNE 21, 2018

To do this, they need to be able to use multiple databases and data models within the same application. For decades because the only database choice was a relational database, no matter the shape or function of the data in the application, the data was modeled as relational.

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. Using raw content data (such as Markdown, YAML, JSON files) and templates an SSG engine (such as Jekyll, Hugo, Gatsby, etc.) Decoupled CMS vs. headless CMS.

Systems 63

Systems Cache Website Network 63