The Netflix TechBlog

JANUARY 13, 2022

By Vikram Srivastava and Marcelo Mayworm Netflix has one of the most complex data platforms in the cloud on which our data scientists and engineers run batch and streaming workloads. Pensive collects logs for the failed jobs launched by the step from the relevant data platform components and then extracts the stack traces.

Big Data 243

Big Data Infrastructure Metrics Games 243

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. What drew you to Netflix?

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

The Netflix TechBlog

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Dhevi Rajendran Dhevi Rajendran 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.

Data Engineering 140

Data Engineering Engineering Software Engineering Big Data 140

The Netflix TechBlog

MARCH 25, 2019

Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted.

Infrastructure 226

Infrastructure Big Data Transportation Architecture 226

Dynatrace

MAY 1, 2023

IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. ITOA automates repetitive cloud operations tasks and streamlines the flow of analytics into decision-making processes.

Analytics 201

Analytics Artificial Intelligence Big Data Open Source 201

The Netflix TechBlog

OCTOBER 27, 2020

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. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 254

Latency Storage Big Data Tuning 254

All Things Distributed

AUGUST 18, 2011

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. However, this cannot be done without efficient, scalable data analytics.

Big Data 94

Big Data Analytics AWS Cloud 94

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.

Big Data 63

Big Data Storage Benchmarking Hardware 63

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

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. This engine is written in Java and Scala. So, what is Apache Flink?

Code 159

Code Java Big Data Open Source 159

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 190

Big Data Cache Engineering Data Engineering 190

Dynatrace

JULY 6, 2022

At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.

Artificial Intelligence 202

Artificial Intelligence Tuning Strategy Big Data 202

Dynatrace

JUNE 26, 2023

This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes. This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. Automate DevSecOps processes at scale.

Software 200

Software Software Analytics Big Data 200

The Morning Paper

DECEMBER 13, 2020

An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. The processing mode – traditional batch (with or without budget constraints), or incremental. Block processing.

Big Data 83

Big Data Open Source Processing Analytics 83

Dynatrace

OCTOBER 25, 2022

NoOps is a concept in software development that seeks to automate processes and eliminate the need for an extensive IT operations team. But it might also result in the entire software development process falling apart. Can organizations really function without an operations team? What is NoOps? Evolution of NoOps.

DevOps 229

DevOps Big Data Cloud Innovation 229

DZone

DECEMBER 27, 2023

Welcome to the first post in our exciting series on mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. Working together, they form the backbone of many modern data engineering solutions.

Best Practices 140

Best Practices Data Engineering Big Data Games 140

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. In this way, no human intervention is required in the remediation process. Multi-objective optimizations.

Tuning 219

Tuning Efficiency Big Data Engineering 219

Highly Scalable

MARCH 1, 2012

The following figure depicts imaginary “evolution” of the major NoSQL system families, namely, Key-Value stores, BigTable-style databases, Document databases, Full Text Search Engines, and Graph databases: NoSQL Data Models. Data duplication and denormalization are first-class citizens. Conceptual Techniques.

Database 279

Database Ecommerce Efficiency Engineering 279

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

AUGUST 10, 2021

Vikash Chhaganlal , GM of Engineering and Infrastructure at Kiwibank said it. Dynatrace CMO Mike Maciag talked about the dangers of “status quo” and failing to get where you need to go because of loyalty to legacy APM software, or hanging onto outdated processes. Investing in data is easy but using it is really hard”.

DevOps 246

DevOps Innovation Big Data Cloud 246

Dynatrace

OCTOBER 4, 2022

Stop worrying about log data ingest and storage — start creating value instead. Dynatrace® Grail , an additional core technology for the Dynatrace® Software Intelligence platform , is the world’s first data lakehouse with massively parallel processing (MPP) for context-rich observability, business, and security analytics.

Analytics 246

Analytics Artificial Intelligence Storage Serverless 246

Dynatrace

APRIL 21, 2021

To do this effectively, you need a big data processing approach. The three challenges to optimizing Core Web Vitals is exactly why the Dynatrace Business Insights team have built the Insights Analytics Engine. How do you know where to focus first with failing pages? How Dynatrace Business Insights can help.

Traffic 264

Traffic Metrics Mobile Analytics 264

Dynatrace

JANUARY 16, 2023

Most Kubernetes clusters in the cloud (73%) are built on top of managed distributions from the hyperscalers like AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE). Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

Open Source 351

Open Source Java Operating System Programming 351

The Netflix TechBlog

FEBRUARY 16, 2021

Membership Engineering at Netflix is responsible for the plan and pricing configurations for every market worldwide. To solve the challenges mentioned above and meet our rapidly evolving business needs, we re-architected the legacy SKU catalog from the ground up and partnered with the Growth Engineering team to build a scalable SKU platform.

Mobile 220

Mobile Engineering Infrastructure Scalability 220

Dynatrace

APRIL 20, 2023

Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams. “The weakness of a data lake is they fail when you need to access them fast,” Pawlowski said.

Analytics 200

Analytics Infrastructure Storage Architecture 200

DZone

JULY 17, 2023

Apache Spark is a leading platform in the field of big data processing, known for its speed, versatility, and ease of use. Understanding Apache Spark Apache Spark is a unified computing engine designed for large-scale data processing.

Big Data 130

Big Data Performance Open Source Tuning 130

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 214

Java Scalability Traffic Architecture 214

Dynatrace

MARCH 24, 2023

Container orchestration is a process that automates the deployment and management of containerized applications and services at scale. Container orchestration enables organizations to manage and automate the many processes and services that comprise workflows.

Infrastructure 198

Infrastructure Open Source Operating System Cloud 198

The Netflix TechBlog

JUNE 7, 2021

Berkeley Packet Filter (BPF) is an in-kernel execution engine that processes a virtual instruction set, and has been extended as eBPF for providing a safe way to extend kernel functionality. What is BPF? In some ways, eBPF does to the kernel what JavaScript does to websites: it allows all sorts of new applications to be created.

Network 327

Network Transportation AWS Cloud 327

Dynatrace

DECEMBER 15, 2022

In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. 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. So, what is ITOps? What is ITOps? ITOps vs. AIOps.

Artificial Intelligence 215

Artificial Intelligence DevOps Hardware Virtualization 215

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.” But what is AIOps, exactly? And how can it support your organization? What is AIOps? Two approaches to AIOps.

Artificial Intelligence 224

Artificial Intelligence DevOps Innovation Metrics 224

Dynatrace

JUNE 1, 2020

However, with today’s highly connected digital world, monitoring use cases expand to the services, processes, hosts, logs, networks, and of course, end-users that access these applications – including your customers and employees. With our AI engine, Davis, at the core Dynatrace provides precise answers in real-time. AI-Assistance.

Artificial Intelligence 185

Artificial Intelligence Social Media Monitoring IoT 185

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. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 214

Storage Latency Efficiency Data Engineering 214

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. CloudOps includes processes such as incident management and event management. The four stages of data processing.

DevOps 209

DevOps Artificial Intelligence Healthcare Innovation 209

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 153

Azure Cloud Big Data Virtualization 153

The Netflix TechBlog

MARCH 2, 2021

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 219

Analytics C++ Innovation Engineering 219

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 116

Cloud Big Data AWS Analytics 116