ScaleOut Software

JULY 14, 2021

The Microsoft Azure IoT ecosystem offers a rich set of capabilities for processing IoT telemetry, from its arrival in the cloud through its storage in databases and data lakes. Acting as a switchboard for incoming and outgoing messages, Azure IoT Hub forms the core of these capabilities.

IoT 52

IoT Azure Analytics Serverless 52

Dynatrace

JANUARY 5, 2022

To do so we have successfully established AI-based White box load and resiliency testing with JMeter and Dynatrace, helping identify and resolve major performance and scalability problems in recent projects before deploying to production. Each step is automated from provisioning infrastructure to problem analysis.

Testing 223

Testing Open Source Metrics Infrastructure 223

Dynatrace

MARCH 18, 2024

Following FinOps practices, engineering, finance, and business teams take responsibility for their cloud usage, making data-driven spending decisions in a scalable and sustainable manner. Hyperscaler cloud service providers such as AWS, Microsoft Azure, and Google Cloud Platform can do this, too. ” But Dynatrace goes further.

Best Practices 238

Best Practices Strategy Cloud AWS 238

Scalegrid

JANUARY 8, 2024

This article delves into the specifics of how AI optimizes cloud efficiency, ensures scalability, and reinforces security, providing a glimpse at its transformative role without giving away extensive details. Exploring artificial intelligence in cloud computing reveals a game-changing synergy.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Dynatrace

NOVEMBER 11, 2022

Therefore, they need an environment that offers scalable computing, storage, and networking. HCI, meanwhile, is a software-defined framework that virtualizes and combines compute, network, storage, and server resources into a unified platform designed for simplicity and scalability. What is hyperconverged infrastructure?

Infrastructure 206

Infrastructure Storage Virtualization Network 206

Dynatrace

AUGUST 16, 2021

inventory and analysi s? Providing standardized self-service pipeline templates, best practices, and scalable automation for monitoring, testing, and SLO validation. Kubernetes deployments can be managed using a combination of both the open-source Azure Kubernetes set context Action and Kubernetes deployment GitHub Action.

Software 246

Software Software Open Source Azure 246

Dynatrace

JANUARY 16, 2023

Most Kubernetes clusters in the cloud (73%) are built on top of managed distributions from the hyperscalers like AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE). Through effortless provisioning, a larger number of small hosts provide a cost-effective and scalable platform.

Open Source 347

Open Source Java Operating System Programming 347

Dynatrace

SEPTEMBER 29, 2021

This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. Amazon Elastic Kubernetes Service , Microsoft Azure Kubernetes Service , and Google Kubernetes Platform each offer their own managed Kubernetes service. Event logs for ad-hoc analysis and auditing.

Open Source 307

Open Source Traffic DevOps Cloud 307

Dynatrace

OCTOBER 11, 2021

In a time when modern microservices are easier to deploy, GCF, like its counterparts AWS Lambda and Microsoft Azure Functions , gives development teams an agility boost for delivering value to their customers quickly with low overhead costs. Scalability is a major feature of GCF. Using GCF within a video analysis workflow.

Google 227

Google Cloud Serverless Azure 227

Dynatrace

NOVEMBER 17, 2020

Dynatrace has continuously extended and improved PurePath to the point where it now provides industry-leading transaction capturing and code-level analysis for the broadest set of technologies, with some additional and unique capabilities: Frontend to backend. Automatic topology analysis. Near-zero overhead.

Analytics 159

Analytics Technology Technology Cloud 159

Dynatrace

JANUARY 31, 2024

The exponential growth of data volume—including observability, security, software lifecycle, and business data—forces organizations to deal with cost increases while providing flexible, robust, and scalable ingest. Understanding the context. Automation and business analytics require precise results.

Analytics 198

Analytics Processing Transportation Storage 198

Dynatrace

FEBRUARY 10, 2021

Critical success factors – velocity, resilience, and scalability. Lots of data intended to help and aid, but instead results in too much noise and unnecessary alerts due to the lack of intelligent root-cause analysis and transparency.

Cloud 171

Cloud DevOps Speed Azure 171

Dynatrace

FEBRUARY 9, 2021

You don’t even have to know which technologies are running to get information beyond observability plus automatic anomaly detection and root cause analysis. The Hub includes the most prominent platforms like Kubernetes and Red Hat OpenShift as well as public cloud vendors like AWS, GCP, and Azure.

Software 146

Software Software Technology Technology 146

Scalegrid

JANUARY 12, 2024

Key Takeaways A cloud workload encompasses any application or service running on a cloud infrastructure, facilitating tasks ranging from basic functions to advanced data analysis with the help of resources like databases, collaboration tools, and disaster recovery systems. This also aids scalability down the line. Additionally.

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

MAY 17, 2023

It is available for the major OS and cloud platforms (for example, Windows, Linux, Solaris, AWS, Azure, and more) and only requires the deployment of a single service to monitor its environment. Among the usual details, like the top N errors and pages, we’ll find here the two main areas for performance analysis and user behavior.

Metrics 176

Metrics Monitoring Database Network 176

Dynatrace

JULY 7, 2022

Smaller teams can launch services much faster using flexible containerized environments, such as Kubernetes, or serverless functions, such as AWS Lambda, Google Cloud Functions, and Azure Functions. The demand for adaptable, highly scalable, and modular application designs has led many developers to move from SOA to a microservices approach.

Architecture 206

Architecture Serverless Azure Programming 206

Dynatrace

SEPTEMBER 30, 2021

Cloud services platforms like AWS, Azure, and GCP are reshaping how organizations deliver value to their customers, making cloud migration an increasingly attractive option for running applications. Increased scalability. But what does it take to migrate your existing applications to the cloud? Reduced cost.

Cloud 169

Cloud Traffic Best Practices Strategy 169

Dynatrace

NOVEMBER 20, 2023

 Using fault-tree analysis, this kind of AI provides critical detail about how its models arrive at a given conclusion. Dynatrace ingests this data to perform root-cause analysis. To solve this problem, organizations can use causal AI and predictive AI to provide that high-quality input.

AWS 214

AWS Artificial Intelligence Analytics Cloud 214

Dynatrace

OCTOBER 1, 2021

Volume, velocity, variety, and complexity : It’s nearly impossible to get answers from the sheer amount of raw data collected from every component in ever-changing modern cloud environments, such as AWS , Azure , and Google Cloud Platform (GCP). Making observability actionable and scalable for IT teams.

Metrics 363

Metrics Open Source Monitoring Infrastructure 363

Dynatrace

NOVEMBER 29, 2021

Self-hosted Kubernetes installations or services — such as Amazon EKS, Azure Kubernetes Service, or the Google Kubernetes Engine — make it possible for enterprises to select and implement best-fit functions. Along with security and developer convenience features, OpenShift and Kubernetes integration takes scalability to a further level.

Monitoring 147

Monitoring DevOps Infrastructure Metrics 147

Dynatrace

JULY 9, 2020

This makes this use case very powerful and very easy to integrate into your existing CI/CD tools such as Jenkins, GitLab Pipelines, Azure DevOps or others. While the Keptn API is easy to call from any of your CI/CD tools we can also point you to projects such as the Jenkins Shared Library, Azure DevOps Extension or the GitLab Plugin.

Cloud 192

Cloud DevOps Azure Architecture 192

ScaleOut Software

AUGUST 5, 2020

And how are they different from streaming pipelines like Azure Stream Analytics and Apache Flink/Beam? For each individual data source, they let the application incorporate dynamic information about that data source in the analysis of incoming messages, and the application can also update this state over time.

Analytics 69

Analytics Azure Processing Speed 69

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.

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.

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

Dynatrace

NOVEMBER 23, 2020

When using managed environments like Google Kubernetes Engine (GKE) , Amazon Elastic Kubernetes (EKS) , or Azure Kubernetes Service it’s easy to spin up a new cluster. When designing and running modern, scalable, and distributed applications, Kubernetes seems to be the solution for all your needs. The Kubernetes experience.

Virtualization 162

Virtualization Infrastructure Monitoring Cloud 162

ScaleOut Software

MAY 19, 2020

But this approach is complex and leads to superficial analysis in real time, forcing telemetry to be logged into a data lake for later analysis using Spark or other tools. It then triggers application code to process the messages and gives it immediate access to memory-based contextual information for the data source.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

MAY 19, 2020

But this approach is complex and leads to superficial analysis in real time, forcing telemetry to be logged into a data lake for later analysis using Spark or other tools. It then triggers application code to process the messages and gives it immediate access to memory-based contextual information for the data source.

Logistics 52

Logistics IoT Analytics Azure 52

The Morning Paper

NOVEMBER 1, 2020

As a reminder: Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relationals engines. For example, the log data generated by Azure Cosmos. Today we’re going to dive into the details of Helios itself.

Cloud 80

Cloud Azure Government Servers 80

Dynatrace

SEPTEMBER 17, 2019

Another important view is the historical data across all nodes, which allows administrators to spot deployment imbalances as well as under or overprovisioned nodes: The node analysis view makes it easy to spot deployment imbalances as well as misconfigured and unhealthy nodes. #3 3 Utilization from a Container perspective.

AWS 128

AWS Monitoring Ecommerce Lambda 128

High Scalability

APRIL 16, 2019

At ScaleGrid, we offer highly available hosting for MySQL on AWS and MySQL on Azure that is implemented based on the concepts explained in this blog series. This concludes our 3-part blog series on the MySQL High Availability (HA) framework using semisynchronous replication and the Corosync plus Pacemaker stack.

Availability 149

Availability Network Azure AWS 149

ScaleOut Software

JUNE 15, 2020

With in-memory computing, aggregate analysis can be performed continuously every few seconds instead of waiting for offline analytics in a data lake. Aggregate analysis can then compare this data across all hospitals by region to identify which regions have the greatest immediate need and track how fast and where overall needs are evolving.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

With in-memory computing, aggregate analysis can be performed continuously every few seconds instead of waiting for offline analytics in a data lake. Aggregate analysis can then compare this data across all hospitals by region to identify which regions have the greatest immediate need and track how fast and where overall needs are evolving.

Analytics 52

Analytics IoT Lambda Big Data 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. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins. How many ventilators currently are in repair by make?”.

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. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins. How many ventilators currently are in repair by make?”.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

JANUARY 31, 2020

Fine-grained analysis by data source must be relegated to offline processing in the data lake, making it impossible to craft individualized, real-time responses to the data sources. The power of real-time digital twins lies in their ability to make fine-grained analysis and responses possible in real time for thousands of data sources.

Analytics 59

Analytics Social Media Ecommerce Serverless 59

ScaleOut Software

JANUARY 31, 2020

Fine-grained analysis by data source must be relegated to offline processing in the data lake, making it impossible to craft individualized, real-time responses to the data sources. The power of real-time digital twins lies in their ability to make fine-grained analysis and responses possible in real time for thousands of data sources.

Analytics 52

Analytics Social Media Serverless Cloud 52