The Netflix TechBlog

JULY 15, 2021

In this post, Kevin talks about his extensive experience in content analytics at Netflix since joining more than 10 years ago. Before joining Netflix, he worked at MySpace, helping implement page categorization, pathing analysis, sessionization, and more. Kevin, what drew you to data engineering? What drew you to Netflix?

Data Engineering 189

Data Engineering Engineering Entertainment Big Data 189

Dynatrace

OCTOBER 4, 2022

Generally, the storage technology categorizes data into landing, raw, and curated zones depending on its consumption readiness. The result is a framework that offers a single source of truth and enables companies to make the most of advanced analytics capabilities simultaneously. Support diverse analytics workloads.

Artificial Intelligence 201

Artificial Intelligence Analytics Storage Government 201

Scalegrid

FEBRUARY 8, 2024

Within Amazon S3’s offerings are features like metadata tagging, different classes of data movement and storage options, configuring control over access permissions, and ensuring safety against disasters through data replication mechanisms. In extensive large-scale data assessments, leveraging such technologies is common practice.

Storage 130

Storage Systems Big Data Azure 130

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. VLDB’19. Approximate query support.

Big Data 57

Big Data Analytics Latency Azure 57

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 278

Artificial Intelligence Cloud Innovation Strategy 278

DZone

MAY 5, 2021

Anytime, every time or sometime you would have heard someone going around with data analysis and saying maybe this could have happened because of this, maybe users did not like the feature or maybe we were wrong all the time. Any analysis and prediction in data analytics across industries experience what I call maybe syndrome.

Analytics 195

Analytics Testing Big Data Code 195

Dynatrace

OCTOBER 25, 2022

“AIOps platforms address IT leaders’ need for operations support by combining big data and machine learning functionality to analyze the ever-increasing volume, variety and velocity of data generated by IT in response to digital transformation.” – Gartner Market Guide for AIOps platforms. Evolution of modern AIOps.

DevOps 210

DevOps Big Data Cloud Innovation 210

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

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

AIOps brings an additional level of analysis to observability, as well as the ability to respond to events that warrant it. This requires significant data engineering efforts, as well as work to build machine-learning models. Big data automation tools. IT automation, DevOps, and DevSecOps go together.

Artificial Intelligence 182

Artificial Intelligence Tuning Strategy Big Data 182

Dynatrace

DECEMBER 15, 2022

AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. Collect raw data in virtual and nonvirtual environments from multiple feeds, normalize and structure the data, and aggregate it for alerts.

Artificial Intelligence 195

Artificial Intelligence DevOps Hardware Virtualization 195

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. The three challenges to optimizing Core Web Vitals is exactly why the Dynatrace Business Insights team have built the Insights Analytics Engine.

Traffic 261

Traffic Metrics Mobile Analytics 261

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

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. This second solution picks up at data collection, aggregation, and analysis, preparing it for execution. Deterministic AI.

DevOps 189

DevOps Artificial Intelligence Healthcare Innovation 189

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 207

Analytics C++ Innovation Engineering 207

The Netflix TechBlog

JUNE 7, 2021

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the cloud network infrastructure to address the identified problems. The data is also used by security and other partner teams for insight and incident analysis.

Network 325

Network Transportation AWS Cloud 325

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 189

Big Data Google Scalability Processing 189

DZone

MAY 30, 2019

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing.

Artificial Intelligence 100

Artificial Intelligence Social Media FinTech Storage 100

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

APRIL 11, 2023

Dynatrace Runtime Vulnerability Analysis now covers the entire application stack – blog Automatic vulnerability detection at runtime and AI-powered risk assessment further enable DevSecOps automation. Learn more.

Cloud 180

Cloud DevOps Open Source Retail 180

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 205

Artificial Intelligence DevOps Innovation Metrics 205

Uber Engineering

APRIL 5, 2018

Three years ago, Uber Engineering adopted Hadoop as the storage ( HDFS ) and compute ( YARN ) infrastructure for our organization’s big data analysis.

Systems 109

Systems Big Data Storage Infrastructure 109

ScaleOut Software

NOVEMBER 24, 2020

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. The results of batch analysis are typically produced after an hour’s delay or more. A second key limitation is the lack of real-time aggregate analysis.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

ScaleOut Software

APRIL 3, 2020

This software can simultaneously perform these actions for hundreds of thousands of ventilators to keep this vital logistical information instantly available for real-time analysis. These questions can be answered using the latest data as it streams in from the field.

Logistics 52

Logistics Analytics Scalability Cloud 52

ScaleOut Software

APRIL 3, 2020

This software can simultaneously perform these actions for hundreds of thousands of ventilators to keep this vital logistical information instantly available for real-time analysis. These questions can be answered using the latest data as it streams in from the field.

Logistics 52

Logistics Analytics Scalability Cloud 52

Testsigma

SEPTEMBER 8, 2021

If I consider current times, it has become a core concept in development, management, analytics, and also – automation testing. In-depth reporting : Testsigma’s reports are of extremely high quality and have been appreciated for the analytical information displayed in them. Cloud-based automation testing is a simple concept.

Cloud 76

Cloud Testing AWS Testing Tools 76

All Things Distributed

DECEMBER 20, 2017

By knowing this, Kärcher can generate new top-line revenue in the form of subscription models for its analysis portal. Marketers use big data and artificial intelligence to find out more about the future needs of their customers. More than mere support. Customers provide feedback online immediately after their purchase.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

The Morning Paper

SEPTEMBER 19, 2019

Hyper Dimension Shuffle describes how Microsoft improved the cost of data shuffling, one of the most costly operations, in their petabyte-scale internal big data analytics platform, SCOPE.

Blockchain 46

Blockchain Hardware Google Analytics 46

Rigor

JANUARY 13, 2020

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. Complete Web Monitoring. Speed Up Your Site. Still good. There's a time bomb on the web: user patience.

Performance 75

Performance Social Media Website Website Performance 75

Highly Scalable

MARCH 10, 2015

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods. In that case, the equation (1.2)

Retail 152

Retail C++ Analytics Metrics 152

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

DZone

JUNE 29, 2020

Today data is an important factor for business success. In every business, it has been observed that data is playing a game-changing moment to improve business performance. Data is important and necessary in this increasingly competitive world.

Games 363

Games Performance Big Data Analytics 363

All Things Distributed

JULY 11, 2011

Spot Instances are ideal for use cases like web and data crawling, financial analysis, grid computing, media transcoding, scientific research, and batch processing. Driving down the cost of Big-Data analytics. Introducing the AWS South America (Sao Paulo) Region. No Server Required - Jekyll & Amazon S3.

AWS 85

AWS Storage Cloud Big Data 85

The Netflix TechBlog

APRIL 29, 2019

The service that orchestrates failover uses numpy and scipy to perform numerical analysis, boto3 to make changes to our AWS infrastructure, rq to run asynchronous workloads and we wrap it all up in a thin layer of Flask APIs. CORE The CORE team uses Python in our alerting and statistical analytical work.

Open Source 279

Open Source Network Infrastructure Big Data 279

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 other two approaches are usually avoided due to the cost involved and data sensitivity.

Testing 60

Testing Storage Database Processing 60

The Netflix TechBlog

JULY 26, 2021

Such a paradigm aspires to assist front-line operations personnel and stakeholders in “running the business”²; performing their tasks through means such as ad hoc analysis, decision-support, and tracking (of tasks, assets, schedules, etc). Data Mesh leverages Iceberg tables as data warehouse sinks for downstream analytics use cases.

Big Data 253

Big Data Government Analytics Processing 253

All Things Distributed

OCTOBER 7, 2015

We live in a world where massive volumes of data are generated from websites, connected devices and mobile apps. 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.

Cloud 137

Cloud Big Data AWS Analytics 137