The Netflix TechBlog

JULY 15, 2021

Before joining Netflix, he worked at MySpace, helping implement page categorization, pathing analysis, sessionization, and more. The combination of overcoming technical hurdles and creating new opportunities for analysis was rewarding. Kevin, what drew you to data engineering? What drew you to Netflix?

Data Engineering 189

Data Engineering Engineering Entertainment Big Data 189

Dynatrace

JUNE 26, 2023

Software analytics offers the ability to gain and share insights from data emitted by software systems and related operational processes to develop higher-quality software faster while operating it efficiently and securely. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI.

Software 190

Software Software Analytics Big Data 190

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 288

Artificial Intelligence Cloud Innovation Strategy 288

The Netflix TechBlog

MAY 4, 2023

This blog post will provide a detailed analysis of replay traffic testing, a versatile technique we have applied in the preliminary validation phase for multiple migration initiatives. After replaying the requests, this dedicated service also records the responses from the production and replay paths for offline analysis.

Traffic 339

Traffic Latency Tuning Systems 339

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

OCTOBER 25, 2022

“AIOps platforms address IT leaders’ need for operations support by combining big data and machine learning functionality to analyze the ever-increasing volume, variety and velocity of data generated by IT in response to digital transformation.” – Gartner Market Guide for AIOps platforms. Evolution of modern AIOps.

DevOps 219

DevOps Big Data Cloud Innovation 219

DZone

DECEMBER 21, 2020

Setting up a data warehouse is the first step towards fully utilizing big data analysis. Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation.

Big Data 189

Big Data Google Scalability Processing 189

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 191

Artificial Intelligence Tuning Strategy Big Data 191

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.

Artificial Intelligence 211

Artificial Intelligence Analytics Storage Government 211

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 198

DevOps Artificial Intelligence Healthcare Innovation 198

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 205

Artificial Intelligence DevOps Hardware Virtualization 205

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 345

Open Source Java Operating System Programming 345

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

Dynatrace

APRIL 25, 2023

Exploratory analytics with collaborative analytics capabilities can be a lifeline for CloudOps, ITOps, site reliability engineering, and other teams struggling to access, analyze, and conquer the never-ending deluge of big data. These analytics can help teams understand the stories hidden within the data and share valuable insights.

Analytics 203

Analytics Big Data Media Operating System 203

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

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. 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. The raw event and problem data from Dynatrace for analysis stored in InfluxDB.

Tuning 263

Tuning Architecture Monitoring Big Data 263

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.

Analytics 188

Analytics Innovation Metrics Database 188

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. What is cloud monitoring? predict and prevent security breaches and outages.

Cloud 224

Cloud Monitoring Best Practices Infrastructure 224

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 215

Artificial Intelligence DevOps Innovation Metrics 215

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

AUGUST 14, 2023

Dynatrace enables organizations to understand user behavior with big data analytics based on gap-free data, eliminating the guesswork involved in understanding the user experience. The right analytics solution can provide the precise insight needed to understand what is driving user behavior and, from there, what to do about it.

Analytics 231

Analytics Social Media Website IoT 231

Dynatrace

APRIL 21, 2021

To do this effectively, you need a big data processing approach. Core Web Vitals metrics introduced in additional standard Business Insights views to enhance our reporting and analysis. How do you know where to focus first with failing pages? Not all pages are equally important, and development resources are top priority.

Traffic 263

Traffic Metrics Mobile Analytics 263

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

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 C++ Innovation Engineering 207

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 184

Infrastructure Cloud Azure AWS 184

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 187

Performance Java Metrics Cloud 187

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 189

Cloud DevOps Open Source Retail 189

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

Testsigma

SEPTEMBER 8, 2021

AppPerfect is one among the tools list that is a versatile tool – it is of great use for not only testers but developers and big data operations. The report is then gathered at a single place with a detailed analysis for the team. Testsigma is giving a 30-day free trial – review all features before you decide.

Cloud 76

Cloud Testing AWS Testing Tools 76