Scalegrid

JANUARY 8, 2024

Key Takeaways AI integration in cloud computing increases operational efficiency by automating processes, optimizing resource allocation, and improving scalability, leading to cost savings and allowing IT teams to concentrate on strategic initiatives. Key among these trends is the emphasis on security and intelligent analytics.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Dynatrace

SEPTEMBER 13, 2023

This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem. This process enables you to continuously evaluate software against predefined quality criteria and service level objectives (SLOs) in pre-production environments.

AWS 246

AWS Efficiency Azure Cloud 246

Dynatrace

JANUARY 31, 2024

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. RAG augments user prompts with relevant data retrieved from outside the LLM.

Cache 204

Cache Azure Infrastructure Monitoring 204

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Anchored in the primary use case of supporting Google’s YouTube business, what we’re looking at here could well be the future of data processing at Google. Because they had too many data processing systems! ;). VLDB’19. are divided.

Analytics 75

Analytics Latency Cache Google 75

Dynatrace

DECEMBER 15, 2022

This transition to public, private, and hybrid cloud is driving organizations to automate and virtualize IT operations to lower costs and optimize cloud processes and systems. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. So, what is ITOps? What is ITOps? Performance.

Artificial Intelligence 202

Artificial Intelligence DevOps Hardware Virtualization 202

Dynatrace

APRIL 6, 2022

Observability can identify the baseline user experience and allow teams to improve it by optimizing page load times or reducing latency. With improved diagnostic and analytic capabilities, DevOps teams can spend less time troubleshooting. Improve business decisions with precision analytics. Why full-stack observability matters.

DevOps 231

DevOps Innovation Infrastructure Analytics 231

Scalegrid

FEBRUARY 8, 2024

This process effectively duplicates essential parts of information to safeguard against potential loss. Durability Availability Fault tolerance These combined outcomes help minimize latency experienced by clients spread across different geographical regions.

Storage 130

Storage Systems Big Data Azure 130

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. This leads us to the process page of our specific Apache instance. Let’s take our previous screenshot as an example.

Metrics 171

Metrics Monitoring Database Network 171

Dynatrace

APRIL 25, 2023

Higher latency and cold start issues due to the initialization time of the functions. Data analysis : how to process, aggregate and query observability data from serverless functions effectively, accurately, and comprehensively? Enable faster development and deployment cycles by abstracting away the infrastructure complexity.

Serverless 207

Serverless Lambda Azure AWS 207

Dynatrace

JULY 24, 2023

We will also explore the evolution of DevOps automation and the significance of data-driven answers in unlocking streamlined, automated DevOps and SRE processes. Business process automation Business process automation is the foundation for improving operational efficiency.

DevOps 220

DevOps Traffic Efficiency Servers 220

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. Ultimately improving efficiency while minimizing errors.

Cloud 130

Cloud Virtualization Storage Efficiency 130

Dynatrace

OCTOBER 4, 2021

API monitoring is the process of collecting and analyzing data about the performance of an API in order to identify problems that impact users. While synthetic monitoring and RUM gather their insights in different ways, they both facilitate a smoother application development process and end-user experience. What is API Monitoring?

Monitoring 178

Monitoring Latency Metrics Availability 178

Dynatrace

MAY 5, 2021

As more organizations respond to the pressure to release better software faster, there is an increasing need to build quality gates into every stage of BizDevOps processes , from early development to deployment. Automating quality gates creates reliable checks and balances and speeds up the process by avoiding manual intervention.

Benchmarking 182

Benchmarking Latency Speed Software 182

Dynatrace

FEBRUARY 2, 2022

service availability with <50ms latency for an application with no revenue impact. A broken SLO with no owner can take longer to remediate and is more likely to recur compared to an SLO with an owner and a well-defined remediation process. To avoid this, start the SLO discussion early in the design process.

DevOps 196

DevOps Metrics Best Practices Latency 196

Dynatrace

APRIL 26, 2022

STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables). One use case for STM is to model the behavior of a customer in the form of a flow of transactions along the buyer’s journey.

Monitoring 203

Monitoring Social Media IoT Metrics 203

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

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191

Analytics Traffic Big Data Efficiency 191

Percona

JUNE 22, 2023

Number of slow queries recorded Select types, sorts, locks, and total questions against a database Command counters and handlers used by queries give an overall traffic summary Along with this, PMM also comes with Query Analytics giving much detailed information about queries getting executed.

Performance 106

Performance Monitoring Traffic Database 106

Dynatrace

OCTOBER 1, 2021

As more organizations adopt cloud-native architectures, they are also looking for ways to implement AIOps, harnessing AI as a way to automate more processes throughout the DevSecOps life cycle. An advanced observability solution can also be used to automate more processes, increasing efficiency and innovation among Ops and Apps teams.

Metrics 363

Metrics Open Source Monitoring Infrastructure 363

Dynatrace

NOVEMBER 3, 2021

Monitoring , by textbook definition, is the process of collecting, analyzing, and using information to track a program’s progress toward reaching its objectives and to guide management decisions. Log entries describe events, such as starting a process, handling an error, or simply completing some part of a workload.

Monitoring 220

Monitoring Metrics DevOps Scalability 220

Dynatrace

DECEMBER 2, 2021

And why have SLOs and SLIs become so important as teams automate processes to consistently meet SLAs and error budgets? As defined by Gartner , service-level objectives are an agreed-upon target within an SLA that must be achieved for each activity, function, and process to provide the best opportunity for customer success.

Metrics 191

Metrics Best Practices DevOps Infrastructure 191

ACM Sigarch

MARCH 18, 2019

This Part 1 discusses Bottleneck Analysis and Little’s Law, while Part 2 presents the M/M/1 Queue. Analytic models—including simple ones like Amdahl’s Law —represent a third, often underused, evaluation method that can provide insight for both practice and research, albeit with less accuracy. Bottleneck Analysis.

Systems 60

Systems Latency Performance Analytics 60

IO River

NOVEMBER 2, 2023

A CDN (Content Delivery Network) is a network of geographically distributed servers that brings web content closer to where end users are located, to ensure high availability, optimized performance and low latency. It is also recommended to have experienced professionals accompany you throughout the process.

Best Practices 52

Best Practices Strategy Traffic Virtualization 52

VoltDB

JUNE 9, 2017

Historically, telco analytics have been limited and difficult. Analytics and insights have always taken a back seat to the first two priorities – accurate data processing and billing. Information needs to be handled in real-time, not via batch processing. Does this affect our analytics strategy?

Analytics 40

Analytics Strategy Network Games 40

VoltDB

JUNE 9, 2017

Historically, telco analytics have been limited and difficult. Analytics and insights have always taken a back seat to the first two priorities – accurate data processing and billing. Information needs to be handled in real-time, not via batch processing. Does this affect our analytics strategy?

Analytics 40

Analytics Strategy Network Games 40

All Things Distributed

JUNE 7, 2017

This is because data gets more valuable when it can be processed together with other data. At the same time, it can be valuable to process some data right at the source where it is generated. Some applications may also rely on timely decisions: when maneuvering heavy machinery, an absolute minimum of latency is critical.

AWS 118

AWS IoT Lambda Storage 118

The Netflix TechBlog

JANUARY 10, 2024

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

Processing 229

Processing Media Latency Innovation 229

ACM Sigarch

DECEMBER 6, 2018

smart cameras & analytics) to interactive/immersive environments and autonomous driving (e.g. Orchestrate the processing flow across an end-to-end infrastructure. Each of these categories opens up challenging problems in AI/visual algorithms, high-density computing, bandwidth/latency, distributed systems. Yolo , ResNet50 ).

Wireless 54

Wireless IoT Analytics Architecture 54

Smashing Magazine

AUGUST 3, 2023

However, achieving a good LCP score is often a multi-faceted process that involves optimizing several stages of loading and rendering. HTML Processing Once a web page’s HTML file has been downloaded, the browser begins to process the contents line by line, translating code into the visual website that users interact with.

Performance 74

Performance Cache Traffic Metrics 74

IO River

NOVEMBER 2, 2023

A CDN (Content Delivery Network) is a network of geographically distributed servers that brings web content closer to where end users are located, to ensure high availability, optimized performance and low latency. It is also recommended to have experienced professionals accompany you throughout the process.

Best Practices 40

Best Practices Strategy Traffic Virtualization 40

ACM Sigarch

MARCH 25, 2019

Part 1 previously discussed Bottleneck Analysis and Little’s Law, while this post (Part 2) presents the M/M/1 Queue. It also presented Bottleneck Analysis and Little’s Law that can give initial answers to questions like: What is the maximum throughput through several subsystems in series and parallel? and 1/S as ?.

Systems 53

Systems Latency Performance C++ 53

Dotcom-Montior

DECEMBER 8, 2021

Now that we have talked about what an incident is, incident management is the process by which teams resolve these events and bring systems and services back to normal operation. ITSM is the policies, processes, and structure behind the lifecycle of IT services. Incident Management Lifecyle: Process and Steps. Service Desk.

Social Media 90

Social Media Monitoring Latency DevOps 90

IO River

NOVEMBER 15, 2023

It's an ongoing, iterative process. You'll have logs and analytics scattered across different CDNs. Aggregating this data into a single, coherent view for analysis becomes an additional hurdle. WAF itself can block known malicious bots based on its rulebook, but its capabilities are limited in terms of behavioral analysis.

Traffic 52

Traffic Network Logistics Architecture 52

ScaleOut Software

AUGUST 18, 2018

In previous blogs , we have explored the power of the digital twin model for stateful stream-processing. This minimizes event-handling latency and enables better management of local operations, while still providing strategic analysis and control by remote (cloud-based or on-premises) IoT applications.

IoT 45

IoT Ecommerce Analytics Latency 45

John McCalpin

APRIL 2, 2020

With a staff of slightly over 175 full-time employees (less than 1/2 in consulting roles), we must therefore focus on highly-leveraged performance analysis projects, rather than labor-intensive ones. Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.

Benchmarking 59

Benchmarking Performance Latency Architecture 59

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.

Big Data 57

Big Data Analytics Latency Azure 57