Dynatrace

SEPTEMBER 13, 2023

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 252

AWS Efficiency Azure Cloud 252

Dynatrace

JANUARY 31, 2024

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. RAG augments user prompts with relevant data retrieved from outside the LLM.

Cache 209

Cache Azure Infrastructure Monitoring 209

The Netflix TechBlog

MARCH 6, 2019

—?Mounting object storage in Netflix’s media processing platform By Barak Alon (on behalf of Netflix’s Media Cloud Engineering team) MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. Encoding is not a one-time process?—?large We have one file?—?the

Media 214

Media Storage Processing Cache 214

Scalegrid

MARCH 28, 2024

Redis Data Types and Structures The design of Redis’s data structures emphasizes versatility. Snapshots provide point-in-time captures of the dataset, which are efficient for recovery on startup. It is designed to cache plain text values, offering fast read and write access to frequently accessed data.

Cache 130

Cache Storage Scalability Architecture 130

The Netflix TechBlog

SEPTEMBER 24, 2021

By Xiaomei Liu , Rosanna Lee , Cyril Concolato Introduction Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Packaging has always been an important step in media processing. Uploading and downloading data always come with a penalty, namely latency.

Cloud 237

Cloud Media Storage Cache 237

Scalegrid

JANUARY 8, 2024

This article delves into the specifics of how AI optimizes cloud efficiency, ensures scalability, and reinforces security, providing a glimpse at its transformative role without giving away extensive details. Using AI for Enhanced Cloud Operations The integration of AI in cloud computing is enhancing operational efficiency in several ways.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Scalegrid

FEBRUARY 8, 2024

Their design emphasizes increasing availability by spreading out files among different nodes or servers — this approach significantly reduces risks associated with losing or corrupting data due to node failure. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

The Netflix TechBlog

NOVEMBER 17, 2022

A distinct, NN-based, video processing block can evolve independently, be used beyond video downscaling and be combined with different codecs. While conventional video codecs remain prevalent, NN-based video encoding tools are flourishing and closing the performance gap in terms of compression efficiency.

Network 292

Network Media Innovation Efficiency 292

Dynatrace

JUNE 7, 2023

Advanced AI applications using OpenAI services don’t just forward user input to OpenAI models; they also require client-side pre- and post-processing. A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt.

Monitoring 168

Monitoring Latency Metrics Azure 168

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. In our last blog post, we introduced “Data Mesh” — A Data Movement and Processing Platform.

Processing 265

Processing Engineering Infrastructure Latency 265

The Netflix TechBlog

MARCH 7, 2024

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. In other cases, it is more convenient to share the results via a low-latency API.

Systems 226

Systems Media Cache Open Source 226

The Netflix TechBlog

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Then deep dive into the merging use case of AutoOptimize and share some results and benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

MARCH 4, 2024

We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.

Tuning 210

Tuning Efficiency Big Data Engineering 210

Dynatrace

JULY 24, 2023

In the world of DevOps and SRE, DevOps automation answers the undeniable need for efficiency and scalability. This evolution in automation, referred to as answer-driven automation, empowers teams to address complex issues in real time, optimize workflows, and enhance overall operational efficiency.

DevOps 225

DevOps Traffic Efficiency Servers 225

The Netflix TechBlog

JUNE 13, 2023

Replay traffic testing gives us the initial foundation of validation, but as our migration process unfolds, we are met with the need for a carefully controlled migration process. A process that doesn’t just minimize risk, but also facilitates a continuous evaluation of the rollout’s impact.

Traffic 279

Traffic Metrics Systems Strategy 279

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Massively parallel processing. What is a data lakehouse? Data warehouses.

Artificial Intelligence 214

Artificial Intelligence Storage Analytics Government 214

Scalegrid

JANUARY 12, 2024

This article analyzes cloud workloads, delving into their forms, functions, and how they influence the cost and efficiency of your cloud infrastructure. In the realm of cloud-based business operations, there is an increasing dependence on complex information processing patterns.

Cloud 130

Cloud Virtualization Storage Efficiency 130

The Netflix TechBlog

MARCH 1, 2021

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. The subsystems all communicate with each other asynchronously via Timestone, a high-scale, low-latency priority queuing system.

Serverless 228

Serverless Media Latency Social Media 228

The Netflix TechBlog

OCTOBER 27, 2020

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 243

Latency Storage Big Data Tuning 243

Scalegrid

MARCH 14, 2024

Defining Hybrid Cloud Strategy The decision-making process about where to situate data and applications is vital to any hybrid cloud solution. The public cloud’s ability to scale efficiently enables ‘cloudbursting’ when demand spikes without requiring businesses to overprovision their own infrastructures.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Scalegrid

MARCH 22, 2024

Crafting Your Multi-Cloud Blueprint Formulating a comprehensive multi-cloud framework is an intricate process that requires a business’s specific requirements, choosing suitable cloud services, and optimizing for performance and affordability. They can also bolster uptime and limit latency issues or potential downtimes.

Strategy 130

Strategy Cloud Government Innovation 130

Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. Fault-tolerance.

Big Data 154

Big Data Processing Lambda Database 154

Dynatrace

APRIL 5, 2021

The 2014 launch of AWS Lambda marked a milestone in how organizations use cloud services to deliver their applications more efficiently, by running functions at the edge of the cloud without the cost and operational overhead of on-premises servers. Real-time file processing, for quickly indexing files, processing logs, and validating content.

Lambda 184

Lambda AWS Serverless Hardware 184

Percona

APRIL 17, 2023

The CFQ works well for many general use cases but lacks latency guarantees. The deadline excels at latency-sensitive use cases ( like databases ), and noop is closer to no schedule at all. 25.84 - Total 21.04 zone_reclaim_mode In some OS versions, the vm.zone_reclaim_mode is enabled. 25.84 - Total 21.04

Best Practices 52

Best Practices Design Tuning Database 52

Dynatrace

DECEMBER 15, 2022

This transition to public, private, and hybrid cloud is driving organizations to automate and virtualize IT operations to lower costs and optimize cloud processes and systems. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. So, what is ITOps? How does ITOps create business value?

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

Dynatrace

FEBRUARY 24, 2021

DevOps is focused on optimizing software development and delivery, and SRE is focused on operations processes. DevOps is not a specific process, but rather a general collection of flexible software creation and delivery practices that looks to close the gap between software development and IT operations. Reduced latency.

DevOps 191

DevOps Software Engineering Speed Google 191

Dynatrace

JANUARY 18, 2023

However, getting reliable answers from observability data so teams can automate more processes to ensure speed, quality, and reliability can be challenging. SRE applies software engineering principles to operations and infrastructure processes. Indeed, the pressure to digitally transform is increasing. What is DevOps?

DevOps 203

DevOps Best Practices Innovation Strategy 203

The Netflix TechBlog

APRIL 26, 2022

We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it. The motivation has not changed since then; the design has. Design evolution Axion fact store has four components?—?fact

Storage 187

Storage Design Scalability Latency 187

The Netflix TechBlog

NOVEMBER 2, 2021

For example, when we design a new version of VMAF, we need to effectively roll it out throughout the entire Netflix catalog of movies and TV shows. This article explains how we designed microservices and workflows on top of the Cosmos platform to bolster such video quality innovations. A simplified trace graph from Nirvana.

Media 171

Media Innovation Metrics Latency 171

Dynatrace

FEBRUARY 2, 2022

service availability with <50ms latency for an application with no revenue impact. A broken SLO with no owner can take longer to remediate and is more likely to recur compared to an SLO with an owner and a well-defined remediation process. To avoid this, start the SLO discussion early in the design process.

DevOps 202

DevOps Metrics Best Practices Latency 202

The Netflix TechBlog

JUNE 4, 2019

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. Its goal is to assign running processes to time slices of the CPU in a “fair” way. So why mess with it?

Cache 251

Cache Latency Airlines Logistics 251

Percona

MARCH 29, 2023

As the amount of data grows, the need for efficient data compression becomes increasingly important to save storage space, reduce I/O overhead, and improve query performance. Snappy compression is designed to be fast and efficient regarding memory usage, making it a good fit for MongoDB workloads.

Storage 105

Storage Network Open Source Latency 105

The Netflix TechBlog

SEPTEMBER 8, 2020

For each route we migrated, we wanted to make sure we were not introducing any regressions: either in the form of missing (or worse, wrong) data, or by increasing the latency of each endpoint. Being able to canary a new route let us verify latency and error rates were within acceptable limits. This meant that data that was static (e.g.

Latency 233

Latency Cache Java Traffic 233

The Netflix TechBlog

OCTOBER 19, 2020

Now let’s look at how we designed the tracing infrastructure that powers Edgar. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices.

Infrastructure 233

Infrastructure Transportation Storage Open Source 233

The Morning Paper

NOVEMBER 10, 2019

It’s been clear for a while that software designed explicitly for the data center environment will increasingly want/need to make different design trade-offs to e.g. general-purpose systems software that you might install on your own machines. The desire for CPU efficiency and lower latencies is easy to understand.

Network 92

Network Transportation Latency Entertainment 92

The Netflix TechBlog

MARCH 13, 2020

The teams have been working closely on SVT-AV1 development, discussing architectural decisions, implementing new tools, and improving compression efficiency. The SVT-AV1 encoder supports all AV1 tools which contribute to compression efficiency. As seen below, SVT-AV1 demonstrates 16.5%

Open Source 154

Open Source Efficiency C++ Speed 154

Dynatrace

JANUARY 13, 2022

Real user monitoring (RUM) is a performance monitoring process that collects detailed data about a user’s interaction with an application. Analyzing a clinician’s clickstream when using an electronic medical record system to better improve the efficiency of data entry. What is real user monitoring?

Monitoring 184

Monitoring Latency Mobile Best Practices 184