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. A truly modern AIOps solution also serves the entire software development lifecycle to address the volume, velocity, and complexity of multicloud environments.

Artificial Intelligence 291

Artificial Intelligence Cloud Innovation Strategy 291

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., There are multiple sources of queueing in both hardware and software, and Seer works best when using deep instrumentation to capture these. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

Dynatrace

JULY 6, 2022

Vulnerability management is one example of a DevSecOps workflow that teams should automate to ensure vulnerability scans run regularly. This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools.

Artificial Intelligence 194

Artificial Intelligence Tuning Strategy Big Data 194

Dynatrace

APRIL 11, 2023

Application vulnerabilities remain a key concern Application vulnerabilities—weaknesses or flaws in software applications that malicious attackers can use to exploit IT systems—exist in any type of software, including web and mobile applications. But organizations face barriers to this convergence.

Cloud 192

Cloud DevOps Open Source Retail 192

The Netflix TechBlog

NOVEMBER 20, 2023

To compensate for that, ETL workflows often use a lookback window, based on which they reprocess the data in that certain time window. 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.

Processing 210

Processing Big Data Efficiency Engineering 210

Dynatrace

JANUARY 11, 2023

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. With agent monitoring, third-party software collects data and reports from the component that’s attached to the agent.

Cloud 227

Cloud Monitoring Best Practices Infrastructure 227

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. Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. Deterministic AI.

DevOps 202

DevOps Artificial Intelligence Healthcare Innovation 202

Dynatrace

APRIL 20, 2023

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 based financial services group, discussed how the bank uses log monitoring on the Dynatrace platform with an emphasis on observability and security data.

Analytics 193

Analytics Infrastructure Storage Efficiency 193

Dynatrace

OCTOBER 4, 2022

Stop worrying about log data ingest and storage — start creating value instead. Dynatrace® Grail , an additional core technology for the Dynatrace® Software Intelligence platform , is the world’s first data lakehouse with massively parallel processing (MPP) for context-rich observability, business, and security analytics.

Analytics 240

Analytics Artificial Intelligence Storage Serverless 240

Dynatrace

AUGUST 14, 2023

How behavior analytics works User behavior analytics works by first collecting, then analyzing user behavior data. Collect user behavior data Organizations typically use analytics software to collect a large volume of data on user behavior from relevant sources.

Analytics 233

Analytics Social Media Website IoT 233

ScaleOut Software

JULY 19, 2021

This architecture does not apply computing resources to track the myriad data sources sending telemetry and continuously look for issues and opportunities that need immediate responses. The post The Need for Real-Time Device Tracking appeared first on ScaleOut Software.

IoT 78

IoT Analytics Big Data Architecture 78

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.” But what is AIOps, exactly? And how can it support your organization? What is AIOps? Why is AIOps needed?

Artificial Intelligence 218

Artificial Intelligence DevOps Innovation Metrics 218

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 202

Java Scalability Traffic Architecture 202

Dynatrace

JUNE 29, 2022

For example, workflows from both public and private cloud resources can support an application. 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 cloud architecture vs. multicloud architecture.

Infrastructure 187

Infrastructure Cloud Azure AWS 187

The Netflix TechBlog

JUNE 1, 2021

Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Samuel Setegne is a Senior Software Engineer on the Core Data Science and Engineering team. For example?—?clinical

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 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. Logs on Grail Log data is foundational for any IT analytics.

Analytics 191

Analytics Innovation Metrics Database 191

The Netflix TechBlog

MARCH 25, 2019

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

The Morning Paper

OCTOBER 26, 2020

Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. What follows is a discussion of where big data systems might be heading, heavily inspired by the remarks in this paper, but with several of my own thoughts mixed in.

Cloud 104

Cloud Big Data Latency Architecture 104

The Netflix TechBlog

MAY 4, 2023

Utilizing cloned real traffic, we can exercise the diversity of inputs from a wide range of devices and device application software versions in production. For example, if some fields in the responses are timestamps, those will differ. This is particularly important for complex APIs that have many high cardinality inputs.

Traffic 339

Traffic Latency Tuning Systems 339

Dynatrace

SEPTEMBER 15, 2020

During the Performance Clinic episode, I asked Stefano to tell us more about this changing world and how we can leverage automation, AI and machine learning to optimize modern software stacks despite the increased complexity. For example, the jvm_gcType parameter already contains the list of GC types that are allowed in OpenJDK 11.

Performance 189

Performance Java Metrics Cloud 189

The Netflix TechBlog

APRIL 29, 2019

Various software systems are needed to design, build, and operate this CDN infrastructure, and a significant number of them are written in Python. One example is the Spectator Python client library, a library for instrumenting code to record dimensional time series metrics.

Open Source 279

Open Source Network Infrastructure Big Data 279

All Things Distributed

DECEMBER 13, 2016

Take Peterborough City Council as an example. The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes. Fraud.net is a good example of this.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

Percona

OCTOBER 18, 2023

release , we added support for physical backups and restores to significantly reduce Recovery Time Objective ( RTO ), especially for big data sets. However, the problem of losing data between backups – in other words, Recovery Point Objective (RPO) – for physical backups was not solved. serverSideEncryption section.

Best Practices 89

Best Practices Storage AWS Big Data 89

ScaleOut Software

APRIL 3, 2020

What’s missing is a flexible, fast, and easy-to-use software system that can be quickly adapted to track these assets in real time and provide immediate answers for logistics managers. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

What’s missing is a flexible, fast, and easy-to-use software system that can be quickly adapted to track these assets in real time and provide immediate answers for logistics managers. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

All Things Distributed

DECEMBER 20, 2017

In today's era of global digitalization there are many examples that show that IT does matter. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster. Value creation through data. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

Percona

JANUARY 19, 2023

However, there are cases where the same column is defined on multiple indexes in order to serve different query patterns, and sometimes some of the indexes created for the same column are redundant, leading to more overhead when inserting or deleting data (as indexes are updated) and increased disk space for storing the indexes for the table.

Open Source 85

Open Source Storage Database Big Data 85

Testsigma

MAY 26, 2021

The test results are a huge set of data and they need to be matched against the expected results, which are again stored in files. . Let us see a few scenarios where data-driven testing is useful in providing a quality product. Scenario 1: Tabular data . Scenario 2: Data Arrays. Example: E-commerce applications.

Testing 70

Testing Healthcare Performance Testing Website 70

All Things Distributed

JANUARY 6, 2016

For example, Samsung Electronic Printing used AWS to deploy its Printing Apps Center in a way that didn’t require them to invest up-front capital and kept total costs quite low. We’ve also been hearing many requests from Korean companies, including large enterprises like Samsung and Mirae Asset.

AWS 110

AWS Cloud Games Latency 110

Testsigma

MAY 14, 2021

The traditional testing that was done on the software installed on local servers is now slowly fading away. However, the primary goal of traditional testing and cloud-based testing remains the same i.e., to deliver high-quality and efficient software. Every project/ software/organization is different and have different requirements.

Cloud 67

Cloud Testing Testing Tools Internet 67

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

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. This new, object-oriented software technique provides a memory-based orchestration framework for tracking and analyzing telemetry from each data source.

Analytics 52

Analytics Architecture Scalability Software Architecture 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

OCTOBER 7, 2015

Today, I am excited to share with you a brand new service called Amazon QuickSight that aims to simplify the process of deriving insights from a wide variety of data sources in a fast and affordable manner. Big data challenges. We believe this is one of the critical parts of our big data offerings.

Cloud 137

Cloud Big Data AWS Analytics 137

ACM Sigarch

APRIL 8, 2019

big-data processing, machine learning, quantum computing, and so on). For example, the existence and support of open-source frameworks such as LLVM or Tensorflow/Pytorch are an attractive element to many newcomers. Her current work focuses on hardware/software co-design for extremely large-scale deep learning training.

Architecture 74

Architecture Open Source Hardware Software Engineering 74

All Things Distributed

SEPTEMBER 6, 2013

Europe is a continent with much diversity and for each country there are great AWS customer examples to tell. Here are some great examples from different industries each with unique use cases. Shell leverages AWS for big data analytics to help achieve these goals.

Cloud 129

Cloud Energy AWS Healthcare 129

All Things Distributed

MAY 21, 2018

We already have an idea of how digitalization, and above all new technologies like machine learning, big-data analytics or IoT, will change companies' business models — and are already changing them on a wide scale. The workplace of the future. At the same time there are many grounds for optimism.

Artificial Intelligence 76

Artificial Intelligence Technology Technology IoT 76