Dynatrace

FEBRUARY 8, 2021

Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Manual troubleshooting is painful, hurts the business, and slows down innovation.

Cloud 258

Cloud Azure Analytics AWS 258

Dynatrace

AUGUST 21, 2023

The collector processes the data and exports it to a predetermined monitoring, tracing, or logging back end, such as Dynatrace, for analysis. Challenges of adopting OpenTelemetry The first challenge is that OpenTelemetry only gathers and processes data—it has no back end, no storage, and no analytics.

Open Source 192

Open Source Analytics Lambda Metrics 192

Dynatrace

FEBRUARY 13, 2020

Echoing John Van Siclen’s sentiments from his Perform 2020 keynote, Steve cited Dynatrace customers as the inspiration and driving force for these innovations. “A Highlighting the company’s announcements from Perform 2020, Steve and a team of other Dynatrace product leaders introduced the audience to several of our latest innovations.

Performance 232

Performance Analytics Mobile Innovation 232

Scalegrid

FEBRUARY 8, 2024

Distributed storage technologies use innovative tools such as Hive, Apache Hadoop, and MongoDB, among others, to proficiently deal with processing extensive volumes encountered in multiple-node-based systems. Amazon S3 and Microsoft Azure Blob Storage leverage distributed storage solutions.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

JANUARY 31, 2024

Bringing together metrics, logs, traces, problem analytics, and root-cause information in dashboards and notebooks, Dynatrace offers an end-to-end unified operational view of cloud applications. Organizations benefit from detailed workflow analysis, resource allocation insights, and execution insights, end to end from prompt to response.

Cache 201

Cache Azure Infrastructure Monitoring 201

Dynatrace

FEBRUARY 12, 2024

Check out the following use cases to learn how to drive innovation from development to production efficiently and securely with platform engineering observability. Progressive delivery Next up, Adam Gardner, staff engineer at Dynatrace, talked about using observability for faster innovation in production to enhance new releases.

Engineering 179

Engineering Development Infrastructure Cloud 179

Dynatrace

NOVEMBER 22, 2021

The following resources demonstrate how Dynatrace’s causation-based AI is changing the game for AIOps to bring real-time root-cause analysis to your AWS environment and beyond.?. Dynatrace extends contextual analytics and AIOps for open observability. But most of that budget goes toward running the business—not software innovation.

AWS 225

AWS Lambda Artificial Intelligence Serverless 225

Dynatrace

JUNE 15, 2023

If your app runs in a public cloud, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), the provider secures the infrastructure, while you’re responsible for security measures within applications and configurations. The release cadence is rapid, sometimes daily or even multiple times per day.

Cloud 174

Cloud Open Source Architecture Monitoring 174

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.

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.

Analytics 52

Analytics IoT Lambda Big Data 52

Dynatrace

SEPTEMBER 29, 2021

Containers are the key technical enablers for tremendously accelerated deployment and innovation cycles. Amazon Elastic Kubernetes Service , Microsoft Azure Kubernetes Service , and Google Kubernetes Platform each offer their own managed Kubernetes service. Anomaly detection and precise root-cause-analysis for fast remediation.

Open Source 298

Open Source Traffic DevOps Cloud 298

Dynatrace

OCTOBER 1, 2021

An advanced observability solution can also be used to automate more processes, increasing efficiency and innovation among Ops and Apps teams. These organizational improvements open the door to further innovation and digital transformation. How do you make a system observable?

Metrics 363

Metrics Open Source Monitoring Infrastructure 363

Dynatrace

APRIL 22, 2022

This gives organizations visibility into their hybrid and multicloud infrastructures , providing teams with contextual insights and precise root-cause analysis. With a single source of truth, infrastructure teams can refocus on innovating, improving user experiences, transforming faster, and driving better business outcomes.

Cloud 166

Cloud Lambda Serverless AWS 166

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. Additionally. This also aids scalability down the line.

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

JUNE 1, 2021

Root-cause and impact analysis of application performance problems and business outcomes for faster, more reliable incident resolution. Business KPIs and user journey analysis (for example, login to check out) to optimize user experiences and provide transparency into how changes impact KPIs. Increased time spent on innovation.

Monitoring 148

Monitoring Mobile Social Media Artificial Intelligence 148

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.

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.

Logistics 52

Logistics Analytics Scalability Cloud 52

SQL Server According to Bob

OCTOBER 26, 2017

Analysis Services Innovations in SQL Server 2017. Microsoft Data Platform – SQL Server 2017 and Azure Data Services. Graph extensions in Microsoft SQL Server 2017 and Azure SQL Database. Delivering high performance analytics with columnstore index on traditional DW and HTAP workloads.

Servers 40

Servers Azure Open Source Database 40

O'Reilly

NOVEMBER 28, 2023

AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Data analysis showed a similar pattern: 70% total; 32% using AI, 38% experimenting with it. Using generative AI tools for tasks related to programming (including data analysis) is nearly universal.

Open Source 108

Open Source Artificial Intelligence Google Infrastructure 108

High Scalability

NOVEMBER 25, 2019

Tens of petabytes of data stored in our servers and other object stores such as GCS, S3 and Azure Blobstore. Version5: files metadata in MySQL, files stored in EOS/GCS/S3/Azure and served via HTTP, search in Lucene. Version6: files metadata in MySQL, files stored in EOS/GCS/S3/Azure served via HTTP, search in Elasticsearch.

Architecture 52

Architecture Cache Azure Storage 52