The Netflix TechBlog

MARCH 10, 2023

This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case.

Media 237

Media Traffic Processing Design 237

Dynatrace

APRIL 8, 2024

Maximizing a strong foundation of IT infrastructure performance and security means effectively utilizing all the data that these systems produce to drive risk management processes and decision-making. Risk functions realize unprecedented visibility and control through the following: Automated aggregation of data from disparate sources.

Analytics 206

Analytics Infrastructure Efficiency Technology 206

The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Anchored in the primary use case of supporting Google’s YouTube business, what we’re looking at here could well be the future of data processing at Google. Because they had too many data processing systems! ;). VLDB’19. are divided.

Analytics 75

Analytics Latency Cache Google 75

The Netflix TechBlog

JANUARY 25, 2023

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. But there are more interesting cases where users want to store temporal (time-based) data or spatial data. Movie Entity with id 1234 has violence.

Scalability 283

Scalability Latency Media Architecture 283

Dynatrace

DECEMBER 13, 2023

By tracking these KPIs and similar, organizations can gain valuable insights into the performance of their mobile apps and make data-driven decisions to improve the user experience and drive growth. Here are some ways observability data is important to mobile app performance monitoring. Load time and network latency metrics.

Best Practices 203

Best Practices Mobile Metrics Performance 203

Scalegrid

DECEMBER 21, 2023

In today’s data-driven world, the ability to effectively monitor and manage data is of paramount importance. Redis®, a powerful in-memory data store, is no exception. Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring.

Strategy 130

Strategy Monitoring Latency DevOps 130

High Scalability

FEBRUARY 17, 2021

This is guest post by Sachin Sinha who is passionate about data, analytics and machine learning at scale. Load stage is to load the data and then run stage we run the test. Load is consistent for all dbs for all tests as expected as this phase is to load the data. Again Yugabyte latency is quite high.

Benchmarking 308

Benchmarking Latency C++ Database 308

Dynatrace

DECEMBER 4, 2023

Data quality and drift: Monitoring the quality and characteristics of training and runtime data to detect significant changes that might impact model accuracy. OpenLLMetry, an open source SDK built on OpenTelemetry, offers standardized data collection for AI Model observability. Maintained under the Apache 2.0

Open Source 262

Open Source Latency Metrics Java 262

Dynatrace

MAY 17, 2023

OpenTelemetry , the open source observability tool, has become the go-to standard for instrumenting custom applications to collect observability telemetry data. For this third and final part of our series, we saved the best for last: How you can enhance telemetry data even more and with less effort on your end with Dynatrace OneAgent.

Metrics 176

Metrics Monitoring Database Network 176

Dynatrace

DECEMBER 27, 2022

SLOs can be a great way for DevOps and infrastructure teams to use data and performance expectations to make decisions, such as whether to release and where engineers should focus their time. This telemetry data serves as the basis for establishing meaningful SLOs. SLOs aid decision making. SLOs promote automation. Reliability.

Software 269

Software Software Benchmarking Latency 269

Dynatrace

APRIL 7, 2023

Customers can use AWS Lambda Response Streaming to improve performance for latency-sensitive applications and return larger payload sizes. Customers can use response streaming to achieve the following: Improve Time to First Byte (TTFB) performance for latency-sensitive applications. Return larger payload sizes.

Lambda 221

Lambda AWS Serverless Latency 221

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

The Netflix TechBlog

JANUARY 10, 2024

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. divide the input video into small chunks 2.

Processing 229

Processing Media Latency Innovation 229

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

Scalegrid

JANUARY 8, 2024

The partnership between AI and cloud computing brings about transformative trends like enhanced security through intelligent threat detection, real-time analytics, personalization, and the implementation of edge computing for quicker on-site decision-making.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Dynatrace

OCTOBER 23, 2023

Hyper-V plays a vital role in ensuring the reliable operations of data centers that are based on Microsoft platforms. As such, the observability platform used to monitor Hyper-V should ideally fulfill requirements for holistic visibility, correlation and causation analysis, AI-powered analytics, scalability, and security.

Efficiency 220

Efficiency Virtualization Hardware Performance 220

Dynatrace

JANUARY 31, 2024

This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. It also shows how data observability relates to business outcomes as organizations embrace generative AI. GenAI is prone to erratic behavior due to unforeseen data scenarios or underlying system issues.

Cache 209

Cache Azure Infrastructure Monitoring 209

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . In-Memory Data Grids for Fast-Changing Data. For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache.

Analytics 52

Analytics Airlines Cache Scalability 52

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . In-Memory Data Grids for Fast-Changing Data. For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache.

Analytics 52

Analytics Airlines Cache Scalability 52

High Scalability

JANUARY 11, 2022

from a client it performs two parallel operations: i) persisting the action in the data store ii) publish the action in a streaming data store for a pub-sub model. User Feed Service, Media Counter Service) read the actions from the streaming data store and performs their specific tasks. Data Models. Graph Data Models.

Design 334

Design Media Storage Logistics 334

All Things Distributed

OCTOBER 12, 2016

Fast Data is an emerging industry term for information that is arriving at high volume and incredible rates, faster than traditional databases can manage. Three years ago, as part of our AWS Fast Data journey we introduced Amazon ElastiCache for Redis , a fully managed in-memory data store that operates at sub-millisecond latency.

Scalability 130

Scalability Analytics Cache AWS 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

Dynatrace

APRIL 25, 2023

Higher latency and cold start issues due to the initialization time of the functions. Observability is typically achieved by collecting three types of data from a system, metrics, logs and traces. Collecting these data types from serverless applications is not straightforward due to their ephemeral nature.

Serverless 213

Serverless Lambda Azure AWS 213

Uber Engineering

JANUARY 16, 2023

Uber’s interactive analytics team shares how they integrated Alluxio’s data caching into Presto, the SQL query engine powering thousands of daily active users on petabyte scale at Uber, to dramatically reduce data scan latencies through leveraging Presto on local disks.

Cache 79

Cache Speed Latency Analytics 79

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

Dynatrace

SEPTEMBER 13, 2023

This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem. Storing frequently accessed data in faster storage, usually in-memory caching, improves data retrieval speed and overall system performance. Beyond

AWS 252

AWS Efficiency Azure Cloud 252

Dynatrace

APRIL 6, 2022

Full-stack observability is the ability to determine the state of every endpoint in a distributed IT environment based on its telemetry data. A full-stack observability solution uses telemetry data such as logs, metrics, and traces to give IT teams insight into application, infrastructure, and UX performance.

DevOps 235

DevOps Innovation Infrastructure Analytics 235

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.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Dynatrace

APRIL 26, 2022

Digital experience monitoring enables companies to respond to issues more efficiently in real time, and, through enrichment with the right business data, understand how end-user experience of their digital products significantly affects business key performance indicators (KPIs).

Monitoring 209

Monitoring Social Media IoT Metrics 209

All Things Distributed

JUNE 7, 2017

Unlocking the value of data is a primary goal that AWS helps our customers to pursue. In recent years, an explosion of intelligent devices have created oceans of new data across many industries. This is because data gets more valuable when it can be processed together with other data. Law of Economics. Law of the Land.

AWS 118

AWS IoT Lambda Storage 118

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. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. Why is IT operations important?

Artificial Intelligence 208

Artificial Intelligence DevOps Hardware Virtualization 208

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

Scalegrid

DECEMBER 14, 2023

For example, your payment history might be on one database cluster and your analytics records on another cluster. If your analytics server is down, then each operation will wait for a default of 30 seconds before failing (which may or may not be what you want).

Java 130

Java Network Servers Database 130

Abhishek Tiwari

JULY 12, 2018

Building data pipelines can offer strategic advantages to the business. It can be used to power new analytics, insight, and product features. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines. Data pipeline initiatives are generally unfinished projects.

Latency 63

Latency Analytics Scalability Engineering 63

The Netflix TechBlog

SEPTEMBER 2, 2020

With request tracing and additional data from logs, events, metadata, and analysis, Edgar is able to show the flow of a request through our distributed system?—?what But, there are a few substantial differences in how Edgar approaches its data and its users. The downside is that we have so many dashboards.

Latency 296

Latency Transportation Engineering Traffic 296

High Scalability

NOVEMBER 23, 2018

Delay is Not an Option: Low Latency Routing in Space , Murat ). Then please recommend my well reviewed (30 reviews on Amazon and 72 on Goodreads!) @KevinBankston : Ford's CEO just said on NPR that the future of profitability for the company is all the data from its 100 million vehicles (and the people in them) they'll be able to monetize.

Internet 174

Internet Internet Scalability Analytics 174

Dynatrace

OCTOBER 4, 2021

API monitoring is the process of collecting and analyzing data about the performance of an API in order to identify problems that impact users. When it comes to APIs, some important metrics include the number of calls to API functions, the time to respond to API function calls, and the amount of data returned. What is API Monitoring?

Monitoring 183

Monitoring Latency Metrics Availability 183