Dynatrace

JULY 6, 2022

AIOps brings an additional level of analysis to observability, as well as the ability to respond to events that warrant it. This requires significant data engineering efforts, as well as work to build machine-learning models. Big data automation tools. IT automation, DevOps, and DevSecOps go together.

Artificial Intelligence 193

Artificial Intelligence Tuning Strategy Big Data 193

Dynatrace

DECEMBER 15, 2022

AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. Continuously gather high-fidelity data in context without manual configuration or scripting. ITOps vs. AIOps.

Artificial Intelligence 207

Artificial Intelligence DevOps Hardware Virtualization 207

Dynatrace

JANUARY 16, 2023

Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch. The need for runtime security observability is growing to automate vulnerability impact analysis. This report reflects Kubernetes adoption statistics based on the analysis of 4.1 Kubernetes survey methodology.

Open Source 346

Open Source Java Operating System Programming 346

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., ASPLOS’19. This is because many QoS violations are caused by very short, bursty events that do not have an impact on queue lengths until a few milliseconds before the violation occurs.

Big Data 45

Big Data Cloud Performance Hardware 45

Uber Engineering

APRIL 5, 2018

Three years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis.

Systems 109

Systems Big Data Storage Infrastructure 109

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

DZone

DECEMBER 21, 2020

Applications used in the field of Big Data process huge amounts of information, and this often happens in real time. Naturally, such applications must be highly reliable so that no error in the code can interfere with data processing. It is an open-source framework for distributed processing of large amounts of data.

Code 150

Code Java Big Data Open Source 150

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

DZone

MAY 30, 2019

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing.

Artificial Intelligence 100

Artificial Intelligence Social Media FinTech Storage 100

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. Traditional log analysis evaluates logs and enables organizations to mitigate myriad risks and meet compliance regulations. ” Watch session now!

Analytics 192

Analytics Infrastructure Storage Efficiency 192

Scalegrid

NOVEMBER 25, 2019

Google Cloud does offer their own wide column store and big data database called Bigtable which is actually ranked #111, one under ScyllaDB at #110 on DB-Engines. ScyllaDB slow query analysis tied ScyllaDB backups and recoveries for second place at 14% each for the most time-consuming management task. of all cloud deployments.

Big Data 187

Big Data Database Open Source Azure 187

The Netflix TechBlog

MARCH 2, 2021

I started working at a local payment processing company after graduation, where I built survival models to calculate lifetime value and experimented with them on our brand new big data stack. I was doing data science without realizing it. One of the most common analyses that I do is a look-back analysis on the explore-data.

Analytics 207

Analytics Innovation C++ Engineering 207

All Things Distributed

NOVEMBER 5, 2015

The new region, coupled with the existing AWS regions in Dublin and Frankfurt, will provide customers with quick, low-latency access to websites, mobile applications, games, SaaS applications, big data analysis, Internet of Things (IoT) applications, and more.

AWS 167

AWS Retail Entertainment IoT 167

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

SEPTEMBER 15, 2020

Supported technologies include cloud services, big data, databases, OS, containers, and application runtimes like the JVM. Akamas also enables you to automate the analysis of the experiment metrics in powerful ways. For example, our smart windowing feature can automatically identify the time window during the experiment (e.g.

Performance 189

Performance Java Metrics Cloud 189

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.

Cloud 191

Cloud DevOps Open Source Retail 191

Dynatrace

OCTOBER 4, 2022

Still, it is critical to collect, store, and make easily accessible these massive amounts of log data for analysis. Full access to all relevant observability, security, and business data is essential to address unforeseen issues and enable proactive efforts to prevent service degradation and outages.

Analytics 239

Analytics Artificial Intelligence Storage Serverless 239

All Things Distributed

JUNE 26, 2016

Advanced problem solving that connects big data with machine learning. At-scale computing and visual analysis. deploys its customers’ genomic pipelines on Amazon EC2 for highly complex and sensitive DNA research activities. We are at the cusp of a dramatic age of technology. We want you to start using it today.

AWS 90

AWS Cloud Healthcare Blockchain 90

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. The list goes on.

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. The list goes on.

Analytics 52

Analytics IoT Lambda Big Data 52

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. This type of analysis is greatly eased by open source tools such RStudio, Jupyter, Zeppelin along with scripting languages R and Python.

Big Data 40

Big Data Retail Storage Google 40

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

ScaleOut Software

APRIL 3, 2020

This software can simultaneously perform these actions for hundreds of thousands of ventilators to keep this vital logistical information instantly available for real-time analysis. These questions can be answered using the latest data as it streams in from the field. How many ventilators currently are in repair by make?”.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

This software can simultaneously perform these actions for hundreds of thousands of ventilators to keep this vital logistical information instantly available for real-time analysis. These questions can be answered using the latest data as it streams in from the field. How many ventilators currently are in repair by make?”.

Logistics 52

Logistics Analytics Scalability Cloud 52

All Things Distributed

JUNE 20, 2017

The new AWS Asia Pacific (Hong Kong) Region, coupled with the existing AWS Regions in Singapore, Tokyo, Sydney, Beijing, Seoul, and Mumbai, and a future one in Ningxia, will provide customers with quick, low-latency access to websites, mobile applications, games, SaaS applications, big data analysis, Internet of Things (IoT) applications, and more.

AWS 149

AWS Logistics Cloud Social Media 149

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

All Things Distributed

SEPTEMBER 29, 2016

The new European region, coupled with the existing AWS Regions in Dublin and Frankfurt, and a future one in London, will provide customers with quick, low-latency access to websites, mobile applications, games, SaaS applications, Big Data analysis, Internet of Things applications, and more.

AWS 166

AWS IoT Internet Internet 166

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

Scalegrid

FEBRUARY 8, 2024

Within Amazon S3’s offerings are features like metadata tagging, different classes of data movement and storage options, configuring control over access permissions, and ensuring safety against disasters through data replication mechanisms. In extensive large-scale data assessments, leveraging such technologies is common practice.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

MAY 1, 2023

Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information. Here are the six steps of a typical ITOA process : Define the data infrastructure strategy. Why use a data lakehouse for causal AI? Why is ITOA important? Apache Spark.

Analytics 193

Analytics Artificial Intelligence Big Data Open Source 193

All Things Distributed

DECEMBER 20, 2017

By knowing this, Kärcher can generate new top-line revenue in the form of subscription models for its analysis portal. Marketers use big data and artificial intelligence to find out more about the future needs of their customers. More than mere support. Customers provide feedback online immediately after their purchase.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

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 290

Artificial Intelligence Cloud Innovation Strategy 290

The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 179

Big Data Cache Engineering Data Engineering 179

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

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 201

DevOps Artificial Intelligence Healthcare Innovation 201

O'Reilly

OCTOBER 21, 2019

With new cryptographic techniques that can enable analysis on the data without actually “seeing” it, data may one day be accessible outside of the corporate silos in which it currently resides. But the story extends beyond the individual.

Blockchain 52

Blockchain Social Media Innovation Internet 52

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.” This second solution picks up at data collection, aggregation and analysis, and prepares it for execution (grey arc).

Artificial Intelligence 217

Artificial Intelligence DevOps Innovation Metrics 217