The Netflix TechBlog

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy.

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Latency Storage Big Data Tuning 252

The Netflix TechBlog

NOVEMBER 3, 2023

Democratizing Stream Processing @ Netflix By Guil Pires , Mark Cho , Mingliang Liu , Sujay Jain Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale. The existing Data Mesh Processors have a lot of overlap with SQL.

Processing 271

Processing Engineering Infrastructure Latency 271

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

Analytics 246

Analytics Infrastructure Storage Architecture 246

Uber Engineering

OCTOBER 17, 2018

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109

Big Data Latency Transportation Traffic 109

Dynatrace

FEBRUARY 16, 2023

How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

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Analytics Innovation Metrics Database 246

Uber Engineering

AUGUST 3, 2018

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data.

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Big Data Transportation Engineering Storage 110

The Netflix TechBlog

JUNE 20, 2024

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.

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Data Engineering Engineering Entertainment Software Engineering 206

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.

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Java Scalability Traffic Architecture 211

The Netflix TechBlog

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 212

Storage Latency Efficiency Data Engineering 212

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.

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Software Software Analytics Big Data 246

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges. Performance.

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Big Data Storage Benchmarking Hardware 63

The Netflix TechBlog

JUNE 7, 2021

At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides flow data at scale for network insight. Flow Exporter The Flow Exporter is a sidecar that uses eBPF tracepoints to capture TCP flows at near real time on instances that power the Netflix microservices architecture. What is BPF?

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Network Transportation AWS Cloud 327

Dynatrace

OCTOBER 4, 2022

Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. Limited data availability constrains value creation. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes.

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Analytics Artificial Intelligence Storage Serverless 264

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

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Tuning Efficiency Big Data Engineering 217

Dynatrace

JUNE 29, 2022

To drive better outcomes using hybrid cloud architectures, it helps to understand their benefits—and how to orchestrate them seamlessly. What is hybrid cloud architecture? Hybrid cloud architecture is a computing environment that shares data and applications on a combination of public clouds and on-premises private clouds.

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Infrastructure Cloud Azure AWS 246

DZone

JANUARY 20, 2023

There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable. It has been a norm to perceive that distributed databases use the method of adding cheap PC(s) to achieve scalability (storage and computing) and attempt to store data once and for all on demand.

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Scalability Big Data Hardware Internet 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 295

Cloud Monitoring Best Practices Infrastructure 295

DZone

JULY 29, 2019

In today's world, data is generated in high volumes and to make something out of it, extracted data is needed to be transformed, stored, maintained, governed and analyzed. These processes are only possible with a distributed architecture and parallel processing mechanisms that Big Data tools are based on.

Big Data 162

Big Data Government Open Source Storage 162

Dynatrace

JANUARY 16, 2023

The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace Software Intelligence Platform to keep their Kubernetes clusters secure, healthy, and high performing. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

Open Source 358

Open Source Java Operating System Programming 358

Dynatrace

JULY 6, 2020

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. See the health of your big data resources at a glance. Azure HDInsight supports a broad range of use cases including data warehousing, machine learning, and IoT analytics.

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Azure Cloud Big Data Virtualization 227

The Netflix TechBlog

MAY 4, 2023

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal.

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Traffic Latency Tuning Systems 347

Dynatrace

JULY 6, 2022

Scripts and procedures usually focus on a particular task, such as deploying a new microservice to a Kubernetes cluster, implementing data retention policies on archived files in the cloud, or running a vulnerability scanner over code before it’s deployed. The range of use cases for automating IT is as broad as IT itself.

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Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

JUNE 1, 2021

Data Engineers of Netflix?—?Interview 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. For example?—?clinical

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Data Engineering Engineering Big Data Healthcare 130

The Netflix TechBlog

SEPTEMBER 10, 2019

After recreating the dataset, you can plot the raw numbers and perform custom analyses to understand the distribution of the data across test cells. However, the default reports only provide a summary view of the data with some powerful but limited filtering options.

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Metrics Architecture Infrastructure Innovation 221

Dynatrace

APRIL 11, 2023

Cloud application security remains challenging because organizations lack end-to-end visibility into cloud architecture. As organizations migrate applications to the cloud, they must balance the agility that microservices architecture brings with the complexity and lack of transparency that can also come with it.

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Cloud DevOps Open Source Retail 246

Dynatrace

DECEMBER 15, 2022

Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. Collect raw data in virtual and nonvirtual environments from multiple feeds, normalize and structure the data, and aggregate it for alerts.

Artificial Intelligence 246

Artificial Intelligence DevOps Hardware Virtualization 246

Dynatrace

APRIL 25, 2023

Drowning under endless data? Having access to large data sets can be helpful, but only if organizations are able to leverage insights from the information. These analytics can help teams understand the stories hidden within the data and share valuable insights. and only they have access.” and only they have access.”

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Analytics Big Data Media Operating System 246

Highly Scalable

MARCH 10, 2015

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods.

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Retail C++ Analytics Metrics 152

Dynatrace

MARCH 11, 2021

In the fourth part of the series, I’ll show you how I used Dynatrace’s raw problem and event data to find the best fit for optimized anomaly detection settings. I took a big-data-analysis approach, which started with another problem visualization. Statistically analyzing Dynatrace’s event and problem data.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

MARCH 24, 2023

Generally, container orchestration tools communicate with a user-created YAML or JSON file — formats that enable data exchange between applications and languages — that describes the configuration of the application or service. The post What is container orchestration? appeared first on Dynatrace news.

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Infrastructure Open Source Operating System Cloud 246

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. The four stages of data processing. There are four stages of data processing: Collect raw data. Analyze the data.

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DevOps Artificial Intelligence Healthcare Innovation 246

The Netflix TechBlog

MAY 26, 2020

By collecting, accessing and analyzing network data from a variety of sources like VPC Flow Logs, ELB Access Logs, Custom Exporter Agents, etc, we can provide Network Insight to users through multiple data visualization techniques like Lumen , Atlas , etc. At Netflix we publish the Flow Log data to Amazon S3.

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Network Tuning AWS Big Data 154

The Netflix TechBlog

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

Processing 219

Processing Big Data Efficiency Engineering 219

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.” training data) that the algorithm can then learn from. Traditional AIOps solutions are built for vendor-agnostic data ingestion.

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Artificial Intelligence DevOps Innovation Metrics 246

Uber Engineering

DECEMBER 17, 2019

Uber’s busy 2019 included our billionth delivery of an Uber Eats order, 24 million miles covered by bike and scooter riders on our platform, and trips to top destinations such as the Empire State Building, the Eiffel Tower, and the … The post Uber’s Data Platform in 2019: Transforming Information to Intelligence appeared first on Uber Engineering (..)

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Engineering Big Data Infrastructure Analytics 144

Dynatrace

MARCH 14, 2022

Over the past decade, the industry moved from paper-based to electronic health records (EHRs)—digitizing the backbone of patient data. They need automated approaches based on real-time, contextualized data. But the push toward digital experiences was thriving long before the advent of the pandemic. Emerging security threats.

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Healthcare Artificial Intelligence Innovation Strategy 246

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. Why do we need a new reference architecture?

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Cloud Big Data Latency Architecture 104