Dynatrace

FEBRUARY 15, 2023

Additionally, its modern architecture delivers cost-effective storage and compute. It provides automatic scalability, runtime application security, secure connections and integrations across hybrid and multicloud ecosystems, and full lifecycle support, including security and quality certifications. 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

While engaging the automatic instrumentation of the Dynatrace OneAgent makes log ingestion automatic and scalable , our customers have set up multiple other log ingestion methods. But there are cases where you might be limited in setting up a dedicated syslog server with OneAgent because of environment architecture or resources.

Strategy 171

Strategy AWS Open Source Transportation 171

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 226

Retail Storage Best Practices Architecture 226

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

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. It also transparently makes use of the IMDG’s scalable computing resources to accelerate the analysis.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. It also transparently makes use of the IMDG’s scalable computing resources to accelerate the analysis.

Analytics 52

Analytics Airlines Cache Scalability 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

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. Vendors ScaleGrid provides the flexibility to select a suitable database architecture, minimizing worries of vendor lock-in. You are both captain and crew.

Database 130

Database Cloud Hardware Storage 130

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

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

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

Dynatrace

NOVEMBER 3, 2021

Organizations are depending more and more on distributed architectures to provide application services. 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. Dynatrace news.

Monitoring 209

Monitoring Metrics DevOps Scalability 209

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

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

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. Conventional, enterprise data architectures take months to develop and are complex to change.

Logistics 84

Logistics Airlines Analytics Ecommerce 84

ScaleOut Software

NOVEMBER 24, 2020

Real-Time Digital Twins Can Add Important New Capabilities to Telematics Systems and Eliminate Scalability Bottlenecks. However, telematics architectures face challenges in responding to telemetry in real time. Current Telematics Architecture. Challenges for Current Architectures.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

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. For example, an analytics application would work best with unstructured image files stored in a non-relational graph database.

Open Source 73

Open Source Database Storage Scalability 73

HammerDB

NOVEMBER 30, 2018

This guide to HammerDB concepts and architectures is aimed at helping you understand how HammerDB is built and how it can be extended and modified. Programming Languages. When you download the source code for HammerDB you can see that the programming language it is written in is called TCL. program in TCL. #!/usr/local/bin/tclsh8.6

Architecture 40

Architecture Virtualization Open Source C++ 40

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. Agile = Flow.

Government 105

Government Programming Metrics Software 105

High Scalability

NOVEMBER 25, 2019

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. In general, Egnyte connect architecture shards and caches data at different levels based on: Amount of data. SOA architecture based on REST APIs. Edge caching.

Architecture 52

Architecture Cache Azure Storage 52

Dotcom-Montior

NOVEMBER 24, 2021

Gone are the days of monolithic architecture. With the rapid advancements in web application technologies, programming languages, cloud computing services, microservices, hybrid environments, etc., Today, there are a variety of architectures and systems in use. Scalability. Multi-Tier.

Systems 74

Systems Monitoring Hardware Network 74

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. We wanted the to tool be available as a standalone library as well as scalable API service.

Testing 163

Testing AWS Lambda Network 163

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

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

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

O'Reilly Software

APRIL 4, 2018

Considerations for setting the architectural foundations for a fast data platform. Google was among the pioneers that created “web scale” architectures to analyze the massive data sets that resulted from “crawling” the web that gave birth to Apache Hadoop, MapReduce, and NoSQL databases. Back in the days of Web 1.0, At least once?

Architecture 80

Architecture Scalability Google Operating System 80

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

MAY 4, 2018

The following diagram depicts a typical stream processing pipeline processing events from many data sources: More recent stream-processing platforms, such as Apache Flink , have incorporated stateful stream processing into their architectures in the form of key-value stores or databases that the application can make use of to enhance its analysis.

Processing 40

Processing IoT Programming Software Engineering 40

ScaleOut Software

MAY 4, 2018

The following diagram depicts a typical stream processing pipeline processing events from many data sources: More recent stream-processing platforms, such as Apache Flink , have incorporated stateful stream processing into their architectures in the form of key-value stores or databases that the application can make use of to enhance its analysis.

Processing 40

Processing IoT Programming Software Engineering 40

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