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 343

Traffic Latency Tuning Systems 343

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 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 193

Data Engineering Engineering Entertainment Big Data 193

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 246

Software Software Analytics Big Data 246

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 246

DevOps Big Data Cloud Innovation 246

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 246

Artificial Intelligence Tuning Strategy Big Data 246

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 196

Big Data Google Scalability Processing 196

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 358

Open Source Java Operating System Programming 358

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 184

Big Data Cache Engineering Data Engineering 184

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 246

DevOps Artificial Intelligence Healthcare Innovation 246

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 264

Tuning Architecture Monitoring Big Data 264

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 246

Artificial Intelligence Storage Analytics Government 246

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 246

Artificial Intelligence DevOps Hardware Virtualization 246

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 295

Cloud Monitoring Best Practices Infrastructure 295

DZone

FEBRUARY 6, 2024

Grafana is used widely these days to monitor and visualize the metrics for 100s or 1000s of servers, Kubernetes Platforms, Virtual Machines, Big Data Platforms, etc. Various platforms that are supported by Grafana today:

Big Data 173

Big Data Open Source Virtualization Metrics 173

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 246

Artificial Intelligence DevOps Innovation Metrics 246

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 246

Analytics Innovation Metrics Database 246

The Netflix TechBlog

MARCH 25, 2019

Netflix’s diverse data landscape made it challenging to capture all the right data and conforming it to a common data model. Spark is the primary big-data compute engine at Netflix and with pretty much every upgrade in Spark, the spark plan changed as well springing continuous and unexpected surprises for us.

Infrastructure 222

Infrastructure Big Data Transportation Architecture 222

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 264

Traffic Mobile Metrics Analytics 264

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

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., Microsoft’s big data clusters have 10s of thousands of machines, and are used by thousands of users to run some pretty complex queries. Analysis suggests that the change in TPC-H answer quality is insignificant.

Big Data 57

Big Data Analytics Latency Azure 57

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 260

Performance Java Metrics Cloud 260

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 326

Network Transportation AWS Cloud 326

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 246

Infrastructure Cloud Azure AWS 246

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 255

Big Data Government Processing Analytics 255

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 159

Code Java Big Data Open Source 159

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 212

Analytics C++ Innovation Engineering 212

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 246

Analytics Social Media Website IoT 246

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 155

AWS Cloud Artificial Intelligence IoT 155

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

All Things Distributed

OCTOBER 7, 2015

We live in a world where massive volumes of data are generated from websites, connected devices and mobile apps. In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data.

Cloud 114

Cloud Big Data AWS Analytics 114

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

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 214

Mobile Engineering Infrastructure Scalability 214

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

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 246

Cloud DevOps Open Source Retail 246

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