The Netflix TechBlog

JULY 26, 2021

This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 253

Big Data Government Analytics Processing 253

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).

Tuning 210

Tuning Efficiency Big Data Engineering 210

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 192

Analytics Infrastructure Storage Efficiency 192

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.

Big Data 63

Big Data Storage Benchmarking Hardware 63

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.

Big Data 110

Big Data Transportation Engineering Storage 110

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

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.

Analytics 191

Analytics Innovation Metrics Database 191

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.

Latency 243

Latency Storage Big Data Tuning 243

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

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 192

Software Software Analytics Big Data 192

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

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

Scalegrid

FEBRUARY 8, 2024

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130

Storage Systems Big Data Azure 130

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.

Scalability 201

Scalability Big Data Hardware Internet 201

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 203

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

OCTOBER 15, 2019

Part I: Overview Andreas Andreakis , Falguni Jhaveri , Ioannis Papapanagiotou , Mark Cho , Poorna Reddy , Tongliang Liu Overview It is a commonly observed pattern for applications to utilize multiple datastores where each is used to serve a specific need such as storing the canonical form of data (MySQL etc.), caching (Memcached etc.),

Transportation 192

Transportation Architecture Processing Storage 192

InfoQ

APRIL 15, 2024

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz

Artificial Intelligence 103

Artificial Intelligence Big Data Data Engineering Latency 103

Scalegrid

MARCH 28, 2024

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Redis is better suited for complex data models, and Memcached is better suited for high-throughput, string-based caching scenarios.

Cache 130

Cache Storage Scalability Architecture 130

Scalegrid

MARCH 14, 2024

Implementing a hybrid cloud solution involves careful decision-making regarding application and data placement, migration strategies, and choosing compatible cloud service providers while ensuring seamless integration and addressing security and compliance challenges. We will examine each of these elements in more detail.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

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.

Analytics 239

Analytics Artificial Intelligence Storage Serverless 239

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.

Infrastructure 187

Infrastructure Cloud Azure AWS 187

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., We’re not told how Seer figures out that a major architectural change has happened. ASPLOS’19. Seer in action. Seer also works best when making prediction 10-100ms into the future.

Big Data 45

Big Data Cloud Performance Hardware 45

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 157

Big Data Government Open Source Storage 157

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 210

Processing Big Data Efficiency Engineering 210

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.

Traffic 339

Traffic Latency Tuning Systems 339

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.

Artificial Intelligence 194

Artificial Intelligence Tuning Strategy Big Data 194

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

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?

Network 325

Network Transportation AWS Cloud 325

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.

Cloud 192

Cloud DevOps Open Source Retail 192

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.”

Analytics 205

Analytics Big Data Media Operating System 205

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 346

Open Source Java Operating System Programming 346

ACM Sigarch

APRIL 8, 2019

Computer architecture is an important and exciting field of computer science, which enables many other fields (eg. big-data processing, machine learning, quantum computing, and so on). For those of us who pursued computer architecture as a career, this is well understood. Why is that? Should we be alarmed as a community?

Architecture 74

Architecture Open Source Hardware Software Engineering 74

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?

Cloud 104

Cloud Big Data Latency Architecture 104

ScaleOut Software

JULY 19, 2021

Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives. 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.

IoT 78

IoT Analytics Big Data Architecture 78

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 208

Artificial Intelligence DevOps Hardware Virtualization 208

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.

Network 150

Network Tuning AWS Big Data 150

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

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.

Azure 150

Azure Cloud Big Data Virtualization 150