The Netflix TechBlog

OCTOBER 28, 2021

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer on the Product Data Science and Engineering team.

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

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. Performance.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Uber Engineering

OCTOBER 17, 2018

To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks … The post Uber’s Big Data Platform: 100+ Petabytes with Minute Latency appeared first on Uber Engineering Blog.

Big Data 109

Big Data Latency Transportation Traffic 109

The Netflix TechBlog

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. 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.

Latency 243

Latency Storage Big Data Tuning 243

Dynatrace

APRIL 20, 2023

Modern organizations ingest petabytes of data daily, but legacy approaches to log analysis and management cannot accommodate this volume of data. based financial services group, discussed how the bank uses log monitoring on the Dynatrace platform with an emphasis on observability and security data.

Analytics 195

Analytics Infrastructure Storage Efficiency 195

Uber Engineering

AUGUST 3, 2018

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data.

Big Data 110

Big Data Transportation Engineering Storage 110

DZone

FEBRUARY 23, 2023

Introduction With big data streaming platform and event ingestion service Azure Event Hubs , millions of events can be received and processed in a single second. Any real-time analytics provider or batching/storage adaptor can transform and store data supplied to an event hub.

Azure 294

Azure Big Data Analytics Storage 294

Dynatrace

FEBRUARY 16, 2023

How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 194

Analytics Innovation Metrics Database 194

DZone

JUNE 4, 2019

NVMe Storage Use Cases. NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI/ML infrastructures of any size. There are several AI/ML focused use cases to highlight.

Storage 100

Storage FinTech Artificial Intelligence Performance 100

Dynatrace

OCTOBER 4, 2022

Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. Limited data availability constrains value creation. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes.

Analytics 242

Analytics Artificial Intelligence Storage Serverless 242

DZone

JANUARY 20, 2023

There is a countless number of enterprises, particularly Internet giants, that have explored ways to make graph data processing scalable. It has been a norm to perceive that distributed databases use the method of adding cheap PC(s) to achieve scalability (storage and computing) and attempt to store data once and for all on demand.

Scalability 201

Scalability Big Data Hardware Internet 201

DZone

JUNE 3, 2019

Using local SSDs inside of the GPU node delivers fast access to data during training, but introduces challenges that impact the overall solution in terms of scalability, data access, and data protection.

Storage 100

Storage Performance Network Scalability 100

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

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 112

Big Data Analytics AWS Scalability 112

The Netflix TechBlog

OCTOBER 15, 2019

Part I: Overview Andreas Andreakis , Falguni Jhaveri , Ioannis Papapanagiotou , Mark Cho , Poorna Reddy , Tongliang Liu Overview It is a commonly observed pattern for applications to utilize multiple datastores where each is used to serve a specific need such as storing the canonical form of data (MySQL etc.), caching (Memcached etc.),

Transportation 192

Transportation Architecture Processing Storage 192

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 157

Big Data Government Open Source Storage 157

Dynatrace

JANUARY 11, 2023

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. With agent monitoring, third-party software collects data and reports from the component that’s attached to the agent.

Cloud 229

Cloud Monitoring Best Practices Infrastructure 229

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. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications.

Artificial Intelligence 100

Artificial Intelligence Social Media FinTech Storage 100

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. We will examine each of these elements in more detail.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Dynatrace

MARCH 24, 2023

Problems include provisioning and deployment; load balancing; securing interactions between containers; configuration and allocation of resources such as networking and storage; and deprovisioning containers that are no longer needed. How does container orchestration work?

Infrastructure 194

Infrastructure Open Source Operating System Cloud 194

Dynatrace

JANUARY 16, 2023

The study analyzes factual Kubernetes production data from thousands of organizations worldwide that are using the Dynatrace Software Intelligence Platform to keep their Kubernetes clusters secure, healthy, and high performing. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

Open Source 348

Open Source Java Operating System Programming 348

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. Collect raw data in virtual and nonvirtual environments from multiple feeds, normalize and structure the data, and aggregate it for alerts.

Artificial Intelligence 211

Artificial Intelligence DevOps Hardware Virtualization 211

Uber Engineering

AUGUST 14, 2019

Maintaining Uber’s large-scale data warehouse comes with an operational cost in terms of ETL functions and storage. Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog.

Efficiency 110

Efficiency Engineering Design Storage 110

Percona

OCTOBER 18, 2023

release , we added support for physical backups and restores to significantly reduce Recovery Time Objective ( RTO ), especially for big data sets. However, the problem of losing data between backups – in other words, Recovery Point Objective (RPO) – for physical backups was not solved. spec: backup: enabled: true.

Best Practices 93

Best Practices Storage AWS Big Data 93

All Things Distributed

AUGUST 20, 2012

Managing Cold Storage with Amazon Glacier. With the introduction of Amazon Glacier , IT organizations now have a solution that removes the headaches of digital archiving and provides extremely low cost storage. All Things Distributed. Werner Vogels weblog on building scalable and robust distributed systems. Expanding the Cloud â??

Storage 122

Storage Cloud AWS Media 122

ScaleOut Software

JULY 19, 2021

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. The following diagram illustrates a typical workflow. What’s missing in this picture?

IoT 78

IoT Analytics Big Data Architecture 78

Highly Scalable

MARCH 1, 2012

At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases. In this article I provide a short comparison of NoSQL system families from the data modeling point of view and digest several common modeling techniques.

Database 279

Database Ecommerce Efficiency Engineering 279

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

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

DZone

MAY 21, 2019

The demand for more IT resource-intensive applications has significantly increased today, whether it is to process quicker transactions, gain real-time insight, crunch big data sets, or to meet customer expectations. That’s because NVMe provides 6x higher bandwidth and IOPS advantage compared to SAS/SATA SSD.

Performance 100

Performance Big Data Storage Processing 100

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. PVLDB’20. Emphasis mine ).

Cloud 104

Cloud Big Data Latency Architecture 104

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

MAY 4, 2023

It can happen on an edge API system servicing customer devices, between the edge and mid-tier services, or from mid-tiers to data stores. Given the scale of the data being generated using replay traffic, we record the responses from the two sides to a cost-effective cold storage facility using technology like Apache Iceberg.

Traffic 339

Traffic Latency Tuning Systems 339

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

Testsigma

JULY 14, 2020

According to Wikipedia, Data-Driven Testing(DDT) is a software testing methodology that is used in the testing of computer software to describe testing done using a table of conditions directly as test inputs and verifiable outputs as well as the process where test environment settings and control are not hard-coded. Database tables.

Testing 63

Testing Artificial Intelligence DevOps Big Data 63

Percona

JANUARY 19, 2023

With disks being faster nowadays and CPU and memory resources being cheaper, we could easily say MySQL can handle TBs of data with good performance. For instance, in Percona Managed Services , we have many clients with TBs worth of data that are well performant. There are many compression tools and algorithms for data out there.

Open Source 90

Open Source Storage Database Big Data 90

All Things Distributed

MAY 18, 2010

Expanding the Cloud - Amazon S3 Reduced Redundancy Storage. Today a new storage option for Amazon S3 has been launched: Amazon S3 Reduced Redundancy Storage (RRS). This new storage option enables customers to reduce their costs by storing non-critical, reproducible data at lower levels of redundancy. Comments ().

Storage 72

Storage Cloud AWS Scalability 72