The Netflix TechBlog

OCTOBER 28, 2021

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. What is your favorite project?

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

Scalegrid

FEBRUARY 8, 2024

These elements work together to spread data over several locations physically distributed, possibly extending across different data centers while optimizing available storage resources. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

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. By Werner Vogels on 18 August 2011 04:00 PM. Comments ().

Big Data 111

Big Data Analytics AWS Cloud 111

The Netflix TechBlog

JANUARY 13, 2022

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. In the future, we are looking to automate this process. The issue may occur in the source Kafka stream, the main Flink job, or the sinks to which the Flink job is writing data.

Big Data 238

Big Data Infrastructure Metrics Hardware 238

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. A small example might help bring this to life.

Big Data 57

Big Data Analytics Latency Azure 57

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 183

Analytics Infrastructure Storage Efficiency 183

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

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 185

Artificial Intelligence Tuning Strategy Big Data 185

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 281

Artificial Intelligence Cloud Innovation Strategy 281

Dynatrace

APRIL 21, 2021

On the Dynatrace Business Insights team, we have developed analytical views and an approach to help you get started. To do this effectively, you need a big data processing approach. What challenges will organizations face to optimize Core Web Vitals? These challenges center around scale, focus, and communication.

Traffic 262

Traffic Metrics Mobile Analytics 262

Dynatrace

APRIL 11, 2023

For example, the open source Java library at the heart of the Log4Shell crisis in 2021 was patched within days given the pervasiveness of the code. For example, in a recent study , 55% of security teams say they don’t trust developers, and 49% of developers perceive security teams as a blocker to innovation.

Cloud 183

Cloud DevOps Open Source Retail 183

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. Analyze the data.

DevOps 192

DevOps Artificial Intelligence Healthcare Innovation 192

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

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. Let’s look at the Azure DB for MariaDB overview as an example. See the health of your big data resources at a glance. Azure Virtual Network Gateways.

Azure 146

Azure Cloud Big Data Virtualization 146

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.” A comprehensive, modern approach to AIOps is a unified platform that encompasses observability, AI, and analytics.

Artificial Intelligence 208

Artificial Intelligence DevOps Innovation Metrics 208

ScaleOut Software

NOVEMBER 24, 2020

The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. At the same time, telemetry snapshots are stored in a data lake, such as HDFS , for offline batch analysis and visualization using big data tools like Spark.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

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

All Things Distributed

DECEMBER 20, 2017

In today's era of global digitalization there are many examples that show that IT does matter. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

ScaleOut Software

APRIL 3, 2020

For example, the parameters for a ventilator could include its identifier, make and model, current location, status (in use, in storage, broken), time in use, technical issues and repairs, and contact information. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

For example, the parameters for a ventilator could include its identifier, make and model, current location, status (in use, in storage, broken), time in use, technical issues and repairs, and contact information. Within seconds, the software performs aggregate analysis of this data for all real-time digital twins.

Logistics 52

Logistics Analytics Scalability Cloud 52

All Things Distributed

AUGUST 16, 2011

For example a number of our European customers are subject to data residency requirements when it comes to PII data and they use the EU Region to meet to those requirements. Government and Big Data. One particular early use case for AWS GovCloud (US) will be massive data processing and analytics.

AWS 111

AWS Government Big Data Cloud 111

All Things Distributed

SEPTEMBER 6, 2013

Europe is a continent with much diversity and for each country there are great AWS customer examples to tell. Here are some great examples from different industries each with unique use cases. Shell leverages AWS for big data analytics to help achieve these goals.

Cloud 129

Cloud Energy AWS Healthcare 129

Testsigma

SEPTEMBER 8, 2021

This is just a small example that we all can relate to and understand the cloud. If I consider current times, it has become a core concept in development, management, analytics, and also – automation testing. In IT, however, the cloud means a lot more than just streaming media on the system. Signup now. AppPerfect.

Cloud 76

Cloud Testing AWS Testing Tools 76

Percona

JANUARY 14, 2019

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. This information can be a mix of analytical (OLAP) queries (i.e. Analytical databases are optimized for a low number of slow queries.

Database 40

Database Analytics Blockchain Healthcare 40

The Netflix TechBlog

JULY 26, 2021

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 253

Big Data Government Analytics Processing 253

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. The test data management for the company had become a big problem and had to be solved.

Testing 60

Testing Storage Database Processing 60

Highly Scalable

MARCH 10, 2015

Although there are many books on data mining in general and its applications to marketing and customer relationship management in particular [BE11, AS14, PR13 etc.], The rest of the article is organized as follows: We first introduce a simple framework that ties together a retailer’s actions, profits and data.

Retail 152

Retail C++ Analytics Metrics 152

All Things Distributed

AUGUST 17, 2011

A good example is the comments section on this blog; a few lines of JavaScript and these pages have a dynamic nature with comments, trackbacks and social media discussion showing up as they happen. Although there are some good examples that come with Cactus is still early days and there is not much of a community using it.

Servers 126

Servers Social Media AWS Website 126

Highly Scalable

JUNE 5, 2012

At GridDynamics, we recently worked on a custom database for realtime web analytics where fast intersection of very large lists of IDs was a must for good performance. From a functional point of view, we needed mainly a standard boolean query processing, so it was possible to use Solr/Lucene as a platform. Vectorized Intersection.

C++ 102

C++ Java Performance Testing Efficiency 102

All Things Distributed

DECEMBER 3, 2009

But customers have also asked us for more flexibility and control in the way that Amazon EC2 instances are booted such that they have finer grained control over for example what software configurations and data sets are available to the instance at boot time. Driving down the cost of Big-Data analytics.

AWS 60

AWS Storage Operating System Cloud 60

All Things Distributed

JULY 13, 2010

Customers with complex computational workloads such as tightly coupled, parallel processes, or with applications that are very sensitive to network performance, can now achieve the same high compute and networking performance provided by custom-built infrastructure while benefiting from the elasticity, flexibility and cost advantages of Amazon EC2.

Cloud 118

Cloud AWS Automotive Latency 118

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. An Example of Schema Mapping.

Latency 243

Latency Storage Big Data Tuning 243

KeyCDN

OCTOBER 2, 2018

That’s why we’ve compiled an exhaustive list of web development blogs and newsletters to make this process easier. It’s awesome for discovering how grid systems, CSS animation, Big Data, etc all play roles in real-world web design. Be sure to check it out if your dev process needs a creative kick in the pants.

Development 75

Development Website Design Code 75

All Things Distributed

DECEMBER 13, 2016

Take Peterborough City Council as an example. The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes.

AWS 166

AWS Cloud Artificial Intelligence IoT 166

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 137

Cloud Big Data AWS Analytics 137