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. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 278

Artificial Intelligence Cloud Innovation Strategy 278

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. AIOps brings an additional level of analysis to observability, as well as the ability to respond to events that warrant it.

Artificial Intelligence 182

Artificial Intelligence Tuning Strategy Big Data 182

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. Continuously gather high-fidelity data in context without manual configuration or scripting. Functionality. ITOps vs. AIOps.

Artificial Intelligence 195

Artificial Intelligence DevOps Hardware Virtualization 195

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 223

Analytics Social Media Website IoT 223

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. In extensive large-scale data assessments, leveraging such technologies is common practice.

Storage 130

Storage Systems Big Data Azure 130

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. This second solution picks up at data collection, aggregation, and analysis, preparing it for execution. Deterministic AI.

DevOps 189

DevOps Artificial Intelligence Healthcare Innovation 189

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. Dedicated Replay Service This approach centralizes the replay logic in an isolated, dedicated code base.

Traffic 339

Traffic Latency Tuning Systems 339

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 201

Artificial Intelligence Analytics Storage Government 201

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. For this I didn’t want to use simple visualization, I wanted to analyze the problem data itself. Getting the raw event and problem data.

Tuning 261

Tuning Architecture Monitoring Big Data 261

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 179

Analytics Innovation Metrics Database 179

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 205

Artificial Intelligence DevOps Innovation Metrics 205

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 175

Infrastructure Cloud Azure AWS 175

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. Tell me more!

Traffic 261

Traffic Metrics Mobile Analytics 261

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

DZone

MAY 5, 2021

Anytime, every time or sometime you would have heard someone going around with data analysis and saying maybe this could have happened because of this, maybe users did not like the feature or maybe we were wrong all the time. Any analysis and prediction in data analytics across industries experience what I call maybe syndrome.

Analytics 195

Analytics Testing Big Data Code 195

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 180

Performance Java Metrics Cloud 180

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 325

Network Transportation AWS Cloud 325

Dynatrace

APRIL 11, 2023

Dynatrace Runtime Vulnerability Analysis now covers the entire application stack – blog Automatic vulnerability detection at runtime and AI-powered risk assessment further enable DevSecOps automation. Learn more. This includes collecting metrics, logs, and traces from all applications and infrastructure components.

Cloud 180

Cloud DevOps Open Source Retail 180

ScaleOut Software

JULY 19, 2021

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. What’s missing in this picture?

IoT 78

IoT Analytics Big Data Architecture 78

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

Instead, most applications just sift through the telemetry for patterns that might indicate exceptional conditions and forward the bulk of incoming messages to a data lake for offline scrubbing with a big data tool such as Spark. Maintain State Information for Each Data Source.

Analytics 52

Analytics IoT Lambda Big Data 52

All Things Distributed

JANUARY 6, 2016

We believe that with the launch of the Seoul Region, AWS will enable many more enterprise customers in Korea to reduce the cost of their IT operations and innovate faster in critical new areas such as big data analysis, Internet of Things, and more.

AWS 110

AWS Cloud Games Latency 110

ACM Sigarch

MAY 31, 2023

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. However, building and utilizing HCM presents challenges, including interconnecting various memory technologies (e.g.,

Latency 52

Latency Hardware Cache Architecture 52

ScaleOut Software

NOVEMBER 24, 2020

At the same time, telemetry snapshots are stored in a data lake, such as HDFS , for offline batch analysis and visualization using big data tools like Spark. The results of batch analysis are typically produced after an hour’s delay or more. A second key limitation is the lack of real-time aggregate analysis.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

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

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. Complete Web Monitoring. Speed Up Your Site. Still good. There's a time bomb on the web: user patience.

Performance 75

Performance Social Media Website Website Performance 75

O'Reilly

OCTOBER 21, 2019

Blockchains enable a permanent and tamper-proof record of a good’s journey from origin to ultimate destination that anyone in the community can monitor and audit. Not only are consumers demanding more transparency, but there are two business reasons for it: transparency can help optimize operations, and it can increase customer preference.

Blockchain 52

Blockchain Social Media Innovation Internet 52

Brendan Gregg

APRIL 30, 2018

In this year's CFP we’re looking for topics covering the latest trends and best practices in cloud computing, containerization, machine learning, big data, infrastructure, scalability, DevOps, IT management, automation, reliability, monitoring, performance tuning, security, databases, programming, datacenters, and more.

DevOps 43

DevOps Network Best Practices Programming 43

Brendan Gregg

APRIL 29, 2018

In this year's CFP we’re looking for topics covering the latest trends and best practices in cloud computing, containerization, machine learning, big data, infrastructure, scalability, DevOps, IT management, automation, reliability, monitoring, performance tuning, security, databases, programming, datacenters, and more.

DevOps 40

DevOps Network Best Practices Programming 40

The Netflix TechBlog

APRIL 29, 2019

The service that orchestrates failover uses numpy and scipy to perform numerical analysis, boto3 to make changes to our AWS infrastructure, rq to run asynchronous workloads and we wrap it all up in a thin layer of Flask APIs. These libraries are the primary way users interface programmatically with work in the Big Data platform.

Open Source 279

Open Source Network Infrastructure Big Data 279

The Netflix TechBlog

JULY 26, 2021

Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain. The Netflix Data Warehouse offers support for users to create data movement workflows that are managed through our Big Data Scheduler, powered by Titus.

Big Data 253

Big Data Government Analytics Processing 253

Percona

JANUARY 14, 2019

Currently, an issue has been opened to make the “tailing” based on the primary key much faster: slow order by primary key with small limit on big data. Text analysis. database: reddit table: rc_2017 mutation_id: mutation_858.txt ClickHouse does not offer full text search, however we can use some text functions.

Database 40

Database Analytics Blockchain Healthcare 40

The Netflix TechBlog

FEBRUARY 16, 2021

A Platform Based on Rules Our initial use case analysis highlighted that most of the change requests were related to enhancing, configuring, or tweaking existing SKU entities to enable business teams to carry out plans or offer related A/B experiments across various geo-locations.

Mobile 209

Mobile Engineering Infrastructure Scalability 209

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 166

AWS Cloud Artificial Intelligence IoT 166

Abhishek Tiwari

JULY 7, 2023

In the era of big data and complex data processing, data pipelines have emerged as a popular solution for managing and manipulating data. They provide a systematic approach to extract, transform, and load (ETL) data from various sources, enabling organizations to derive valuable insights.

Logistics 52

Logistics Transportation Scalability Data Engineering 52