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

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. The integration with Dynatrace has two sides: first, it pulls metrics from Dynatrace while Akamas is executing an experiment.

Performance 260

Performance Java Metrics Cloud 260

Dynatrace

FEBRUARY 16, 2023

How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 246

Analytics Innovation Metrics Database 246

Dynatrace

APRIL 21, 2021

In February 2021, Dynatrace announced full support for Google’s Core Web Vitals metrics , which will help site owners as they start optimizing Core Web Vitals performance for SEO. To do this effectively, you need a big data processing approach. Segregation of data by mobile and desktop. Dynatrace news. 28-day lookbacks.

Traffic 264

Traffic Mobile Metrics Analytics 264

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

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

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. But AIOps also improves metrics that matter to the bottom line.

Artificial Intelligence 325

Artificial Intelligence Cloud Innovation Strategy 325

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. The batch job creates a high-level summary that captures some key comparison metrics.

Traffic 347

Traffic Latency Tuning Systems 347

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

The Netflix TechBlog

JUNE 7, 2021

The Flow Exporter also publishes various operational metrics to Atlas. These metrics are visualized using Lumen , a self-service dashboarding infrastructure. The data is also used by security and other partner teams for insight and incident analysis. So how do we ingest and enrich these flows at scale ?

Network 327

Network Transportation AWS Cloud 327

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

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

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. The deviating metric is response time. Execute an action plan. This is now the starting node in the tree.

DevOps 246

DevOps Artificial Intelligence Healthcare Innovation 246

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. ITOps vs. AIOps.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

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

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

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.” Traditional AIOps solutions are built for vendor-agnostic data ingestion. The four stages of data processing.

Artificial Intelligence 246

Artificial Intelligence DevOps Innovation Metrics 246

Dynatrace

JUNE 26, 2024

Observability analytics enables users to gain new insights into traditional telemetry data such as logs, metrics, and traces by allowing users to dynamically query any data captured and to deliver actionable insights. Managing tool sprawl More observability tools means more data — and more complexity.

Analytics 246

Analytics Infrastructure Metrics Efficiency 246

DZone

AUGUST 4, 2022

Spark-Radiant will help optimize performance and cost considering catalyst optimizer rules, enhance auto-scaling in Spark, collect important metrics related to a Spark job, Bloom filter index in Spark, etc. Spark-Radiant is Apache Spark Performance and Cost Optimizer. Spark-Radiant is now available and ready to use.

Performance 246

Performance Metrics Availability Big Data 246

The Netflix TechBlog

JULY 26, 2021

A daily process ranks the records by timestamp to generate a data frame of compacted records. Old data files are overwritten with a set of new data files that contain only the compacted data. Data Quality Data Mesh provides metrics and dashboards at both the processor and pipeline level for operational observability.

Big Data 257

Big Data Government Processing Analytics 257

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

Highly Scalable

MAY 1, 2012

On the other hand, when one is interested only in simple additive metrics like total page views or average price of conversion, it is obvious that raw data can be efficiently summarized, for example, on a daily basis or using simple in-stream counters. what is the cardinality of the data set)? Case Study. Case Study.

Analytics 191

Analytics Traffic Big Data Efficiency 191

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

Scalegrid

MARCH 14, 2024

Workloads from web content, big data analytics, and artificial intelligence stand out as particularly well-suited for hybrid cloud infrastructure owing to their fluctuating computational needs and scalability demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Rigor

JANUARY 13, 2020

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. How to pioneer new metrics and create a culture of performance. Complete Web Monitoring. Time is Money.

Performance 75

Performance Social Media Website Website Performance 75

The Netflix TechBlog

OCTOBER 14, 2024

Overview At Netflix, the Analytics and Developer Experience organization, part of the Data Platform, offers a product called Workbench. Workbench is a remote development workspace based on Titus that allows data practitioners to work with big data and machine learning use cases at scale. We then exported the .har

Latency 214

Latency Virtualization Traffic Processing 214

Adrian Cockcroft

NOVEMBER 18, 2024

Discover data sources to gain insights into your resource efficiency and environmental impact, including the AWS Customer Carbon Footprint Tool and proxy metrics from the AWS Cost & Usage Reports. aggregates vast datasets, pinpoints emissions, and helps customers like ExxonMobil monitor and mitigate methane releases.

AWS 98

AWS Energy Lambda Government 98