Dynatrace

JULY 6, 2022

Monitoring and logging are fundamental building blocks of observability. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming. Similarly, digital experience monitoring is another ongoing process that lends itself to IT automation.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

Dynatrace

JUNE 1, 2020

Application Performance Monitoring (APM) in its simplest terms is what practitioners use to ensure consistent availability, performance, and response times to applications. Websites, mobile apps, and business applications are typical use cases for monitoring. APM can be referred to as: Application performance monitoring.

Artificial Intelligence 254

Artificial Intelligence Social Media Monitoring IoT 254

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Once products and services are live, IT teams must continuously monitor and manage them. What is AIOps, and how does it work?

Artificial Intelligence 325

Artificial Intelligence Cloud Innovation Strategy 325

Dynatrace

SEPTEMBER 9, 2019

Synthetic data, network data, system data, and the list goes on. In recent years, the amount of data we analyze has exploded as we look at the data collected by Real User Monitoring (RUM), meaning every session, every action, in every region and so on. As much as I love data, data is cold, it lacks emotion.

Big Data 210

Big Data Metrics Virtualization Network 210

Dynatrace

OCTOBER 4, 2022

Even in cases where all data is available, new challenges can arise. When one tool monitors logs, but traces, metrics, security, audit, observability, and business data sources are siloed elsewhere or monitored using other tools, teams can struggle to align or deliver a single version of the truth.

Analytics 264

Analytics Artificial Intelligence Storage Serverless 264

Dynatrace

APRIL 19, 2023

. “We relied on customers (our players) to call us and let us know if something was broken and had scattered monitoring tools,” Mehdiabadi says. Mehdiabadi says the company can now easily forecast both frontend and backend data to see everything that’s going on. “Our players just see the frontend.

Entertainment 237

Entertainment Analytics Healthcare Games 237

Dynatrace

AUGUST 14, 2023

Collect user behavior data Organizations typically use analytics software to collect a large volume of data on user behavior from relevant sources. These sources can include the website or app itself, a data warehouse or a customer data platform (CDP), or social media monitoring tools.

Analytics 246

Analytics Social Media Website IoT 246

DZone

FEBRUARY 6, 2024

Grafana is an open-source tool to visualize the metrics and logs from different data sources. It can query those metrics, send alerts, and can be actively used for monitoring and observability, making it a popular tool for gaining insights. What Is Grafana?

Big Data 173

Big Data Open Source Virtualization Metrics 173

Dynatrace

SEPTEMBER 15, 2020

After every experiment run Akamas changes application, runtime, database or cloud configuration based on monitoring data it captured during the previous experiment run. Supported technologies include cloud services, big data, databases, OS, containers, and application runtimes like the JVM.

Performance 260

Performance Java Metrics Cloud 260

Dynatrace

DECEMBER 15, 2022

The roles and responsibilities of ITOps team members include the following: A system administrator configures servers, installs applications, monitors the health of the system, and fixes and upgrades hardware. The primary goal of ITOps is to provide a high-performing, consistent IT environment. Functionality. ITOps vs. AIOps.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

Dynatrace

JUNE 29, 2022

A hybrid cloud, however, combines public infrastructure and services with on-premises resources or a private data center to create a flexible, interconnected IT environment. Hybrid environments provide more options for storing and analyzing ever-growing volumes of big data and for deploying digital services.

Infrastructure 246

Infrastructure Cloud Azure AWS 246

Dynatrace

AUGUST 10, 2021

Intelligent Observability includes the ability to not only monitor applications but gain actionable insights that can be used to transform services and create great customer experiences. She dispelled the myth that more big data equals better decisions, higher profits, or more customers. How can I make them better?

DevOps 246

DevOps Innovation Big Data Cloud 246

Dynatrace

OCTOBER 4, 2022

While data lakehouses combine the flexibility and cost-efficiency of data lakes with the querying capabilities of data warehouses, it’s important to understand how these storage environments differ. Data warehouses. Data warehouses were the original big data storage option. Download report now!

Artificial Intelligence 246

Artificial Intelligence Storage Analytics Government 246

Dynatrace

APRIL 20, 2023

Modern organizations ingest petabytes of data daily, but legacy approaches to log analysis and management cannot accommodate this volume of data. At Dynatrace Perform 2023 , Maciej Pawlowski, senior director of product management for infrastructure monitoring at Dynatrace, and a senior software engineer at a U.K.-based

Analytics 246

Analytics Infrastructure Storage Architecture 246

Dynatrace

MARCH 24, 2023

This orchestration includes provisioning, scheduling, networking, ensuring availability, and monitoring container lifecycles. Apache Mesos with the Marathon DC/OS is popular for large-scale production clusters running existing workloads on big data systems, such as Hadoop, Kafka, and Spark.

Infrastructure 246

Infrastructure Open Source Operating System Cloud 246

Dynatrace

APRIL 21, 2021

The Business Insights team at Dynatrace has been working with our largest Digital Experience Monitoring customers to help them turn the Core Web Vitals data they’re collecting with Dynatrace into actionable insights they can use to optimize pages ahead of this June 2021 change in Google’s search ranking algorithm.

Traffic 264

Traffic Mobile Metrics Analytics 264

The Netflix TechBlog

APRIL 29, 2019

Python is also a tool we typically use for automation tasks, data exploration and cleaning, and as a convenient source for visualization work. Monitoring, alerting and auto-remediation The Insight Engineering team is responsible for building and operating the tools for operational insight, alerting, diagnostics, and auto-remediation.

Open Source 280

Open Source Network Infrastructure Big Data 280

Dynatrace

OCTOBER 17, 2022

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. Analyze the data. Execute an action plan. Introduce a machine-learning-based AIOps platform as a logical evolution in IT management tooling.

DevOps 246

DevOps Artificial Intelligence Healthcare Innovation 246

DZone

MAY 25, 2024

As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and declining stability come. As NetEase's pick among all big data components for platform upgrades, Apache Doris fits into both scenarios and brings much faster query performance.

Best Practices 130

Best Practices Big Data Games Storage 130

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. But logs are just one pillar of the observability triumvirate.

Analytics 246

Analytics Innovation Metrics Database 246

The Netflix TechBlog

JULY 26, 2021

However, it is paramount that we validate the complete set of identifiers such as a list of movie ids across producers and consumers for higher overall confidence in the data transport layer of choice. Trackers are now easy for the team to create, review, change, monitor and discover. The audits check for equality (i.e.

Big Data 257

Big Data Government Processing Analytics 257

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. Statistically analyzing Dynatrace’s event and problem data. Usually, in single environment setups, you would do that by adjusting parameters step by step and adopt as you learn more until you find the combination that works best for you.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

OCTOBER 14, 2021

Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” The second challenge with traditional AIOps centers around the data processing cycle. But what is AIOps, exactly? What is AIOps?

Artificial Intelligence 246

Artificial Intelligence DevOps Innovation Metrics 246

The Netflix TechBlog

JUNE 7, 2021

The attributed flow data drives various use cases within Netflix like network monitoring and network usage forecasting available via Lumen dashboards and machine learning based network segmentation. The data is also used by security and other partner teams for insight and incident analysis.

Network 327

Network Transportation AWS Cloud 327

All Things Distributed

DECEMBER 13, 2016

With the launch of the AWS Europe (London) Region, AWS can enable many more UK enterprise, public sector and startup customers to reduce IT costs, address data locality needs, and embark on rapid transformations in critical new areas, such as big data analysis and Internet of Things. Fraud.net is a good example of this.

AWS 155

AWS Cloud Artificial Intelligence IoT 155

The Netflix TechBlog

MAY 4, 2023

The second phase involves migrating the traffic over to the new systems in a manner that mitigates the risk of incidents while continually monitoring and confirming that we are meeting crucial metrics tracked at multiple levels. We use this additional logging to debug and identify the root cause of issues driving the mismatches.

Traffic 347

Traffic Latency Tuning Systems 347

Dynatrace

JUNE 26, 2024

Managing tool sprawl More observability tools means more data — and more complexity. To effectively monitor and manage these services, organizations often rely on multiple monitoring tools, each with its own feature set and focus. In some cases, these features overlap.

Analytics 246

Analytics Infrastructure Metrics Efficiency 246

Dynatrace

MARCH 14, 2022

AIOps (or “AI for IT operations”) uses artificial intelligence so that big data can help IT teams work faster and more effectively. Pediatricians use these applications to monitor the heart rate of child patients. This perfect storm of challenges has led to the accelerated adoption of artificial intelligence, including AIOps.

Healthcare 246

Healthcare Artificial Intelligence Innovation Strategy 246

Dynatrace

APRIL 11, 2023

Convergence of observability and security data is a must As digital transformation accelerates, most organizations house hybrid cloud environments for which observability and security are paramount concerns. This includes collecting metrics, logs, and traces from all applications and infrastructure components.

Cloud 246

Cloud DevOps Open Source Retail 246

The Netflix TechBlog

NOVEMBER 20, 2023

For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

Processing 219

Processing Big Data Efficiency Engineering 219

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 211

Java Scalability Traffic Architecture 211

Scalegrid

FEBRUARY 8, 2024

These distributed storage services also play a pivotal role in big data and analytics operations. Big data analytics mines expansive datasets collected from hospitals and personal medical devices at home. The health sector provides an illustration of the critical importance of analyzing large volumes of information.

Storage 130

Storage Systems Big Data Azure 130

Scalegrid

JANUARY 10, 2025

They keep the features that developers like but can handle much more data, similar to NoSQL systems. Notably, they simplify handling big data flows, offer consistent transactions, and sustain high performance even when they’re used for real-time data analysis and complex queries.

Database 130

Database Best Practices Scalability Blockchain 130

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

Big Data 45

Big Data Cloud Performance Hardware 45

Highly Scalable

MAY 1, 2012

Let us start with a simple example that illustrates capabilities of probabilistic data structures: Let us have a data set that is simply a heap of ten million random integer values and we know that it contains not more than one million distinct values (there are many duplicates). what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

All Things Distributed

SEPTEMBER 5, 2013

Over the past few years, two important trends that have been disrupting the database industry are mobile applications and big data. The explosive growth in mobile devices and mobile apps is generating a huge amount of data, which has fueled the demand for big data services and for high scale databases.

Big Data 78

Big Data Mobile Latency Database 78