Scalegrid

MARCH 28, 2024

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. can enhance Redis by handling management tasks, backups, and scalability, facilitating global reach and easy cloud integration for global businesses.

Cache 130

Cache Storage Scalability Architecture 130

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

ScaleOut Software

JANUARY 31, 2020

Consider the typical, conventional streaming analytics pipeline available on popular cloud platforms: A conventional pipeline combines telemetry from all data sources into a single stream which is queried by the user’s streaming analytics application. However, real-time digital twins easily bring these capabilities within reach.

Analytics 59

Analytics Social Media Ecommerce 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

Dynatrace

OCTOBER 17, 2023

The rise of cloud-native microservice architectures further exacerbates this change. Grail is designed for scalability, with no technical prerequisites or additional hosting and storage costs as ingestion rates increase. In today’s data-driven landscape, businesses are grappling with an unprecedented surge in data volume.

Retail 237

Retail Storage Best Practices Architecture 237

ScaleOut Software

JANUARY 31, 2020

Consider the typical, conventional streaming analytics pipeline available on popular cloud platforms: A conventional pipeline combines telemetry from all data sources into a single stream which is queried by the user’s streaming analytics application. However, real-time digital twins easily bring these capabilities within reach.

Analytics 52

Analytics Social Media Serverless Cloud 52

ScaleOut Software

AUGUST 5, 2020

And how are they different from streaming pipelines like Azure Stream Analytics and Apache Flink/Beam? What Problems Does Streaming Analytics Solve? To understand why we need real-time digital twins for streaming analytics, we first need to look at what problems are tackled by popular streaming platforms.

Analytics 69

Analytics Azure Processing Speed 69

Dynatrace

DECEMBER 15, 2022

While many organizations have embraced cloud observability to better manage their cloud environments, they may still struggle with the volume of entities that observability platforms monitor. Fluentd is known for its flexibility and is also highly scalable, which makes it a good choice for high-volume environments.

Strategy 180

Strategy AWS Open Source Transportation 180

All Things Distributed

SEPTEMBER 6, 2013

Given that I am originally from the Netherlands I have, of course, a special interest in how Dutch companies are using our cloud services. . But it is not just Dutch entrepreneurs who build their business in the cloud, also traditional Dutch enterprises are moving to the cloud to improve their agility and cost-effectiveness.

Cloud 129

Cloud Energy AWS Healthcare 129

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

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

JANUARY 28, 2021

Given the unique and highly dynamic nature of this challenge, we need software solutions that are agile enough to adapt to evolving needs and scalable enough to quickly handle a daunting amount of fast-changing data. Its two core competencies are speed and scalability. Using Real-Time Digital Twins to Help Vaccine Distribution.

Logistics 84

Logistics Airlines Analytics Ecommerce 84

Percona

MARCH 30, 2023

For example, an analytics application would work best with unstructured image files stored in a non-relational graph database. Open source standards and community support enable developers and DBAs to focus on accelerating feature creation and on enhancing availability, performance, scalability, and security.

Open Source 76

Open Source Database Storage Scalability 76

Dynatrace

NOVEMBER 3, 2021

Monitoring , by textbook definition, is the process of collecting, analyzing, and using information to track a program’s progress toward reaching its objectives and to guide management decisions. Distributed tracing is a way of observing requests as they move through distributed cloud environments.

Monitoring 220

Monitoring Metrics DevOps Scalability 220

ScaleOut Software

JUNE 22, 2020

In general terms, in-memory computing refers to the related concepts of (a) storing fast-changing data in primary memory instead of in secondary storage and (b) employing scalable computing techniques to distribute a workload across a cluster of servers.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JUNE 22, 2020

In general terms, in-memory computing refers to the related concepts of (a) storing fast-changing data in primary memory instead of in secondary storage and (b) employing scalable computing techniques to distribute a workload across a cluster of servers.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

HammerDB

JUNE 27, 2022

The TPC designed benchmarks for transaction processing (OLTP) and analytics (OLAP) and anyone can run these benchmarks, have them audited by the TPC and published on the official benchmark rankings. What programming languages does HammerDB use and why does it matter? Adoption by the TPC. HammerDB Licensing. Derived Workloads.

Benchmarking 62

Benchmarking Open Source C++ Cache 62

All Things Distributed

JANUARY 19, 2011

Werner Vogels weblog on building scalable and robust distributed systems. AWS Elastic Beanstalk: A Quick and Simple Way into the Cloud. Elastic Beanstalk makes it easy for developers to deploy and manage scalable and fault-tolerant applications on the AWS cloud. Expanding the Cloud - The AWS Storage Gateway.

AWS 115

AWS Cloud Java Scalability 115

ScaleOut Software

NOVEMBER 24, 2020

Real-Time Digital Twins Can Add Important New Capabilities to Telematics Systems and Eliminate Scalability Bottlenecks. This telematics architecture has evolved to handle ever increasing message rates (often reaching 2K messages per second), make up-to-the-minute information available to dispatchers, and feed offline analytics.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Tasktop

OCTOBER 20, 2020

federal government, IT and program leaders must find a better way to manage their software delivery organizations to improve decision-making where it matters. From AI to the cloud, to predictive analytics and innovative systems, there are few places that software isn’t impacting the government. Think Big, Start Small.

Government 105

Government Programming Metrics Software 105

ScaleOut Software

APRIL 3, 2020

An innovative new software approach called “real-time digital twins” running on a cloud-hosted, highly scalable, in-memory computing platform can help address this challenge. By avoiding the need to create or connect to complex databases and ship data to offline analytics systems, it can provide timely answers quickly and easily.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

An innovative new software approach called “real-time digital twins” running on a cloud-hosted, highly scalable, in-memory computing platform can help address this challenge. By avoiding the need to create or connect to complex databases and ship data to offline analytics systems, it can provide timely answers quickly and easily.

Logistics 52

Logistics Analytics Scalability Cloud 52

All Things Distributed

AUGUST 16, 2011

Werner Vogels weblog on building scalable and robust distributed systems. Expanding the Cloud - The AWS GovCloud (US) Region. AWS GovCloud (US) implements specific requirements of the US government such that agencies at the federal, state and local levels can use the AWS cloud for their more sensitive workloads. Cloud First.

AWS 111

AWS Government Big Data Cloud 111

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

MAY 6, 2020

Writing applications for streaming analytics can be complex and time consuming. Existing programming models make it too difficult to perform meaningful analysis in real time. This application cannot be implemented by most streaming analytics platforms. A database service to host state information for each user. Summing Up.

Development 52

Development Analytics Database Programming 52

ScaleOut Software

MAY 6, 2020

Writing applications for streaming analytics can be complex and time consuming. Existing programming models make it too difficult to perform meaningful analysis in real time. This application cannot be implemented by most streaming analytics platforms. A database service to host state information for each user. Summing Up.

Development 52

Development Analytics Database Programming 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

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

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

Dotcom-Montior

NOVEMBER 24, 2021

With the rapid advancements in web application technologies, programming languages, cloud computing services, microservices, hybrid environments, etc., This complexity is “hidden” to the end user, like how an API (Application Programming Interface) operates, whether that is an actual user or another computer. Scalability.

Systems 74

Systems Monitoring Hardware Network 74

ScaleOut Software

AUGUST 18, 2018

This minimizes event-handling latency and enables better management of local operations, while still providing strategic analysis and control by remote (cloud-based or on-premises) IoT applications. 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