Dynatrace

FEBRUARY 15, 2023

Additionally, its modern architecture delivers cost-effective storage and compute. As a result, teams benefit from low-cost cloud storage that provides access to all data and doesn’t require data rehydration. Teams can visualize an application’s vital signs, including its security posture. Site Reliability Guardian.

Analytics 130

Analytics Storage Cloud Code 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

Dynatrace

DECEMBER 15, 2022

In this blog, we share three log ingestion strategies from the field that demonstrate how building up efficient log collection can be environment-agnostic by using our generic log ingestion application programming interface (API). In the “Storage and Logging” section, select “ awsfirelens ” as the log driver.

Strategy 180

Strategy AWS Open Source Transportation 180

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

Scalegrid

DECEMBER 6, 2023

ScaleGrid’s comprehensive solutions provide automated efficiency and cost reduction while offering tailored features such as predictive analytics for businesses of all sizes. All the tedious tasks such as storage, backups, and configuration are managed by an attentive crew so that you can concentrate on making your vacation special.

Database 130

Database Cloud Hardware Storage 130

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

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Because IMDGs have highly scalable storage capacity, they can easily return large numbers of matching objects to the client application, and this leads to network bottlenecks transferring large amounts of data from the IMDG back to the client.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Because IMDGs have highly scalable storage capacity, they can easily return large numbers of matching objects to the client application, and this leads to network bottlenecks transferring large amounts of data from the IMDG back to the client.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JUNE 29, 2018

Having just concluded participation in another In-Memory Computing Summit , it has become even more clear to me that the key to mainstream adoption of in-memory computing software platforms is architecture — the root of a platform’s value to applications. These priorities tend to push the architecture to the back burner.

Software Architecture 40

Software Architecture Architecture Software Software 40

ScaleOut Software

JUNE 29, 2018

Having just concluded participation in another In-Memory Computing Summit , it has become even more clear to me that the key to mainstream adoption of in-memory computing software platforms is architecture — the root of a platform’s value to applications. These priorities tend to push the architecture to the back burner.

Software Architecture 40

Software Architecture Architecture Software Software 40

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

Percona

MARCH 30, 2023

This comprehensive overview examines open source database architecture, types, pros and cons, uses by industry, and how open source databases compare with proprietary databases. Further, open source databases can be modified in infinite ways, enabling institutions to meet their specific needs for data storage, retrieval, and processing.

Open Source 76

Open Source Database Storage Scalability 76

All Things Distributed

SEPTEMBER 6, 2013

Shell leverages AWS for big data analytics to help achieve these goals. Unilever – Unilever R&D program intended to accelerate the company’s scientific progress through improved access to global information. In addition, its robust architecture supports ten times as many scientists, all working simultaneously.

Cloud 129

Cloud Energy AWS Healthcare 129

The Netflix TechBlog

MAY 21, 2019

There are several problems efficient test scheduling could help us solve: Quickly detect a regression in the integration of the Netflix SDK on a consumer electronic or MVPD (multichannel video programming distributor) device. The data can be truncated after each execution and offloaded to a long-term storage for further analysis.

Testing 163

Testing AWS Lambda Network 163

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

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

APRIL 3, 2020

For example, the parameters for a ventilator could include its identifier, make and model, current location, status (in use, in storage, broken), time in use, technical issues and repairs, and contact information. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

For example, the parameters for a ventilator could include its identifier, make and model, current location, status (in use, in storage, broken), time in use, technical issues and repairs, and contact information. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

Alex Russell

JULY 14, 2021

Here, native apps are doing work related to their core function; storage and tracking of user data are squarely within the four corners of the app's natural responsibilities. Without the ability to catch all navigations sent to the OS, users who downloaded these programs suffered frequent computing amnesia. How can that be?

Google 121

Google Mobile Engineering Internet 121

ScaleOut Software

MAY 4, 2014

Usually this means moving to a server with more CPU cores, greater memory capacity, and higher-end networking and storage options. Now CPU, memory, and storage resources can grow without predefined limits. At some point, scaling up becomes costly, and workloads grow beyond what even a high end server can handle.

Network 40

Network Servers Analytics Storage 40

The Netflix TechBlog

MARCH 10, 2023

are stored in secure storage layers. Amsterdam is built on top of three storage layers. And finally, we have an Apache Iceberg layer which stores assets in a denormalized fashion to help answer heavy queries for analytics use cases. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Strategy 258

Strategy Cache Analytics Storage 258

All Things Distributed

NOVEMBER 15, 2010

We believe that making these GPU resources available for everyone to use at low cost will drive new innovation in the application of highly parallel programming models. These trade-offs have even impacted the way the lowest level building blocks in our computer architectures have been designed. General Purpose GPU programming.

AWS 136

AWS Latency Programming Architecture 136

High Scalability

NOVEMBER 25, 2019

It is limited by the disk space; it can’t expand storage elastically; it chokes if you run few I/O intensive processes or try collaborating with 100 other users. Over time, costs for S3 and GCS became reasonable and with Egnyte’s storage plugin architecture, our customers can now bring in any storage backend of their choice.

Architecture 52

Architecture Cache Azure Storage 52