Dynatrace

AUGUST 25, 2023

It makes them available for a log analytics platform to gain automated, contextual, and actionable insights into the services and underlying platforms. You can filter logs based on their content, source, or process technology. This process can easily be automated for different types of problems.

Serverless 219

Serverless Analytics AWS Azure 219

Dynatrace

APRIL 25, 2023

For example, to handle traffic spikes and pay only for what they use. These functions are executed by a serverless platform or provider (such as AWS Lambda, Azure Functions or Google Cloud Functions) that manages the underlying infrastructure, scaling and billing. and GoLang to reduce the necessary boilerplate code to a minimum.

Serverless 203

Serverless Lambda Azure AWS 203

Dynatrace

NOVEMBER 17, 2020

PurePath 4 supports serverless computing out-of-the-box, including Kubernetes services from Amazon Web Services (AWS) , Microsoft Azure , and Google Cloud Platform (GCP). FaaS like AWS Lambda and Azure Functions are seamlessly integrated with no code changes. In this specific example these are embedded into a custom GoLang application.

Analytics 154

Analytics Technology Technology Cloud 154

Scalegrid

MAY 13, 2020

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. What Exactly is Greenplum? At a glance – TLDR.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

Scalegrid

FEBRUARY 8, 2024

This process effectively duplicates essential parts of information to safeguard against potential loss. It utilizes methodologies like DStore, which takes advantage of underused hard drive space by using it for storing vast amounts of collected datasets while enabling efficient recovery processes.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

OCTOBER 6, 2023

For example, a separate bucket could be used for detailed logs from Dynatrace Synthetic nodes. In conjunction with the previous example of keeping high-volume and short-lived logs separate, you might also need to keep your application data longer. Now, let’s take a look at a query example that puts this all into use.

Artificial Intelligence 258

Artificial Intelligence Analytics Metrics Storage 258

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

SEPTEMBER 13, 2023

And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks? This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem.

AWS 243

AWS Efficiency Azure Cloud 243

Dynatrace

MARCH 6, 2024

Grabner gave the example of one Dynatrace banking customer who built an IDP that enables developers to provision new Azure machines or Chef policies without administrative help. Observability is not only about measuring performance and speed, but also about capturing granular business analytics to support data-driven decision-making.

Engineering 237

Engineering Development DevOps Infrastructure 237

Dynatrace

JANUARY 31, 2024

For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Using the example of a chatbot, once the user submits a natural language prompt, RAG summarizes that prompt using semantic data. Consequently, AI model drift and hallucinations emerge as primary concerns.

Cache 201

Cache Azure Infrastructure Monitoring 201

Dynatrace

AUGUST 24, 2020

But there are other related components and processes (for example, cloud provider infrastructure) that can cause problems in applications running on Kubernetes. Similarly, integrations for Azure and VMware are available to help you monitor your infrastructure both in the cloud and on-premises. Dynatrace OneAgent documentation

Analytics 237

Analytics Infrastructure AWS Operating System 237

Dynatrace

FEBRUARY 12, 2024

At the 2024 Dynatrace Perform conference in Las Vegas, Michael Winkler, senior principal product management at Dynatrace, ran a technical session exploring just some of the many ways in which Dynatrace helps to automate the processes around development, releases, and operation. Automatically analyze and debug releases.

Engineering 179

Engineering Development Infrastructure Cloud 179

High Scalability

JANUARY 11, 2022

There are two major processes which gets executed when a user posts a photo on Instagram. Firstly, the synchronous process which is responsible for uploading image content on file storage, persisting the media metadata in graph data-storage, returning the confirmation message to the user and triggering the process to update the user activity.

Design 334

Design Media Storage Logistics 334

Dynatrace

APRIL 21, 2022

If you still use the legacy version of Log Monitoring ( Log Monitoring v1 ), the Log Monitoring v1 documentation is still available, but we strongly encourage you to switch to the latest Dynatrace Log Monitoring version and gain direct access to the log content of all your mission-critical processes. Kubernetes/OpenShift workload/pod details.

Azure 212

Azure Metrics AWS Monitoring 212

Dynatrace

APRIL 19, 2023

Enterprise data stores grow with the promise of analytics and the use of data to enable behavioral security solutions, cognitive analytics, and monitoring and supervision. For example, some security incidents have had a widespread, costly impact. For example, credit card numbers are excluded by default.”

Design 188

Design Storage Programming Analytics 188

Scalegrid

JANUARY 12, 2024

Various forms can take shape when discussing workloads within the realm of cloud computing environments – examples include order management databases, collaboration tools, videoconferencing systems, virtual desktops, and disaster recovery mechanisms. Ultimately improving efficiency while minimizing errors.

Cloud 130

Cloud Virtualization Storage Efficiency 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.

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

JANUARY 20, 2022

Azure supporting services (Synapse Analytics). WeThe Detected processes page has been clarified. Time aggregation corrected for `GC time` chart on process group instance page. (APM-343594). File descriptors metric now correctly aggregates PIDs on host’s process list and process details page. (APM-341616).

Lambda 177

Lambda AWS Metrics Azure 177

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. It’s simply not practical to create a unique query tailored for each data source.

Analytics 59

Analytics Social Media Ecommerce Serverless 59

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. It’s simply not practical to create a unique query tailored for each data source.

Analytics 52

Analytics Social Media Serverless Cloud 52

Dynatrace

AUGUST 24, 2020

But there are other related components and processes ( for example, cloud provider infrastructure ) that can cause problems in applications running on Kubernetes. Similar ly, integrations for Azure and VMware are available to help you monitor your infrastructure both in the cloud and on-premises. .

Analytics 134

Analytics Infrastructure AWS Operating System 134

ScaleOut Software

APRIL 6, 2021

With the ScaleOut Digital Twin Streaming Service , an Azure-hosted cloud service, ScaleOut Software introduced breakthrough capabilities for streaming analytics using the real-time digital twin concept. This simplifies the installation process and ensures portability across operating systems.

IoT 52

IoT Azure Analytics Healthcare 52

Dynatrace

MAY 17, 2023

The next level of observability: OneAgent In the first two parts of our series, we used OpenTelemetry to manually instrument our application and send the telemetry data straight to the Dynatrace analytics back end. Let’s take our previous screenshot as an example. Yet, we have two entries for these two services in our list.

Metrics 168

Metrics Monitoring Database Network 168

Dynatrace

JUNE 15, 2023

Cloud application security is a combination of policies, processes, and controls that aim to reduce the risk of exposing cloud-based applications to compromise or failure from external or internal threats. This process can delay deployments or cause developers to skip security testing completely to meet project deadlines.

Cloud 174

Cloud Open Source Architecture Monitoring 174

Dynatrace

JANUARY 5, 2022

In white-box testing, we combine open-source load testing tools such as JMeter with Dynatrace’s observability and analytics capabilities. This is an example of the W3C Trace context allowing us to uniquely trace and connect test requests to Dynatrace PurePath. Automation : Single load test executions can be repeated and tracked.

Testing 214

Testing Open Source Metrics Infrastructure 214

ScaleOut Software

AUGUST 20, 2020

Simplifying the Development Process with Mock Environments. This blog post explains how a new software construct called a real-time digital twin running in a cloud-hosted service can create a breakthrough for streaming analytics. The method contains domain-specific knowledge, such as a predictive analytics algorithm for truck engines.

Cloud 52

Cloud Development Analytics Azure 52

ScaleOut Software

AUGUST 20, 2020

Simplifying the Development Process with Mock Environments. This blog post explains how a new software construct called a real-time digital twin running in a cloud-hosted service can create a breakthrough for streaming analytics. The method contains domain-specific knowledge, such as a predictive analytics algorithm for truck engines.

Cloud 52

Cloud Development Analytics Azure 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

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. For example, heart-rate telemetry from a smart watch cannot be effectively evaluated in isolation. Sign up to use the service here.

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. For example, heart-rate telemetry from a smart watch cannot be effectively evaluated in isolation. Sign up to use the service here.

Logistics 52

Logistics IoT Analytics Azure 52

ScaleOut Software

JUNE 22, 2020

For example, ecommerce applications adopted distributed caching to store session-state, shopping carts, product descriptions, and other data that shoppers need to be able to access quickly. The following diagram shows the evolution of in-memory computing from distributed caching to stream-processing with real-time digital twins.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

ScaleOut Software

JUNE 22, 2020

For example, ecommerce applications adopted distributed caching to store session-state, shopping carts, product descriptions, and other data that shoppers need to be able to access quickly. The following diagram shows the evolution of in-memory computing from distributed caching to stream-processing with real-time digital twins.

Ecommerce 52

Ecommerce Cache Scalability IoT 52

Dynatrace

JUNE 1, 2021

However, with today’s highly connected digital world, monitoring use cases expand to the services, processes, hosts, logs, networks, and of course, end-users that access these applications — including a company’s customers and employees. Mobile apps, websites, and business applications are typical use cases for monitoring.

Monitoring 148

Monitoring Mobile Social Media Artificial Intelligence 148

Dynatrace

MAY 28, 2020

While Google’s SRE Handbook mostly focuses on the production use case for SLIs/SLOs, Keptn is “Shifting-Left” this approach and using SLIs/SLOs to enforce Quality Gates as part of your progressive delivery process. The app description and supporting files such as load testing scripts are on the Keptn Example GitHub.

Engineering 189

Engineering Performance Metrics Best Practices 189

Dotcom-Montior

OCTOBER 19, 2021

For example, in smaller organizations or startups, personnel tend to be more limited, so a site reliability engineer may need to be well-versed in multiple tools, such as Golang, Terraform, Docker, CircleCI, and Puppet, just to name a few. In the past, this process would have taken an entire team of individuals.

Engineering 86

Engineering Open Source Programming Monitoring 86

The Morning Paper

SEPTEMBER 8, 2019

I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. These clusters are in high demand and approximate query processing both saves their users time and lightens the overall load on the cluster. Col1 Col2 Uniform(0.5)

Big Data 57

Big Data Analytics Latency Azure 57