Introduction to Amazon Nimble Studio and its use cases

In this recipe, we will learn about Amazon Nimble Studio. We will also learn about the use cases of Amazon Nimble Studio.

Recipe Objective - Introduction to Amazon Nimble Studio and its use cases?

The Amazon Nimble Studio is a widely used service and is defined as a fully managed service which enables creative studios to produce visual effects, animation, and interactive content entirely in the cloud. With access to virtual workstations, high-speed storage, and scalable rendering across AWS's global infrastructure, you can quickly onboard and collaborate with artists around the world and create content faster. Amazon Nimble Studio combines virtual workstations powered by Amazon EC2 G4dn instances, NVIDIA GPUs, and Amazon FSx high-speed storage into a single package. It works with Windows and Linux and allows artists to use Amazon Machine Images to work with third-party creative applications and custom software applications (AMIs). AWS also stated that studios can use custom software applications and bring them into Nimble Studio via AMIs. Customers can start with G4dn.xlarge (4 vCPUs, 16GB memory, and an NVIDIA Tesla T4 GPU with RTX) for simple tasks and scale up to 64 vCPUs and 256GB memory for larger data sets and simulation workflows. Build on the world's most secure infrastructure, knowing that users will always have control over their data, including the ability to encrypt, move, and manage it. Before it leaves the secure facilities, AWS automatically encrypts all data flowing across the AWS global network at the physical layer and it builds with the highest standard for data security. Granting user permissions, sharing project data, and adding new team members are all made easier with the Nimble Studio portal. Stream pixels instead of data using the NICE DCV remote display protocol to keep the users project data in the cloud and streamline artist collaboration and provides seamless collaboration.

Learn to Build ETL Data Pipelines on AWS

Benefits of Amazon Nimble Studio

  • Instead of weeks, users get their content production pipeline up and running in hours. Nimble Studio's automation and pre-built Amazon Machine Images (AMIs) make setting up virtual workstations, storage, and a render farm a breeze, all while maintaining an artist-friendly user interface (UI) and thus accelerate the cloud transition. Nimble Studio scales users studio to meet business needs across single or multiple locations by automatically configuring AWS services. With virtual workstations, users can add more artists to graphics-intensive projects, use high-speed storage with Amazon FSx, and orchestrate compute resources on an integrated cloud-based render farm with EC2 Spot Instances and thus scale with the project demand. In a matter of minutes, users will be able to bring in remote artists. Make use of the most up-to-date software and hardware to give users their artists and studio the best possible performance. Users can look for and hire the best talent in major content creation markets because of the availability and thus access the global talent users need. There are several costs to consider when setting up virtual streaming workstations. To take the guesswork out of the total cost of ownership, Nimble Studio offers a simplified pricing structure that includes the instance, Elastic Block Store (EBS), and egress charges (TCO) and thus provide simplified workstation pricing.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Nimble Studio and Use cases of Amazon Nimble Studio.

Use cases of Amazon Nimble Studio

    • It has a use case of Visual Effects(VFX)

Nimble Studio provides the robust infrastructure you need for even the most complex VFX work, whether users are creating lifelike creatures, immersive environments, or complex simulations.

    • It has a use case of Animation

Nimble Studio offers a content creation pipeline that scales with users needs, from animated shorts and commercials to full-length feature films.

    • It has a use case of Game development

Nimble Studio, in collaboration with AWS Partners Epic Games, Incredibuild, and Perforce, can help users scale your game production pipeline and connect remote teams.

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Azure Stream Analytics for Real-Time Cab Service Monitoring
Build an end-to-end stream processing pipeline using Azure Stream Analytics for real time cab service monitoring

Build Serverless Pipeline using AWS CDK and Lambda in Python
In this AWS Data Engineering Project, you will learn to build a serverless pipeline using AWS CDK and other AWS serverless technologies like AWS Lambda and Glue.

Orchestrate Redshift ETL using AWS Glue and Step Functions
ETL Orchestration on AWS - Use AWS Glue and Step Functions to fetch source data and glean faster analytical insights on Amazon Redshift Cluster

Learn to Create Delta Live Tables in Azure Databricks
In this Microsoft Azure Project, you will learn how to create delta live tables in Azure Databricks.

SQL Project for Data Analysis using Oracle Database-Part 2
In this SQL Project for Data Analysis, you will learn to efficiently analyse data using JOINS and various other operations accessible through SQL in Oracle Database.

Implementing Slow Changing Dimensions in a Data Warehouse using Hive and Spark
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.

Build an Incremental ETL Pipeline with AWS CDK
Learn how to build an Incremental ETL Pipeline with AWS CDK using Cryptocurrency data

GCP Project to Learn using BigQuery for Exploring Data
Learn using GCP BigQuery for exploring and preparing data for analysis and transformation of your datasets.

GCP Project-Build Pipeline using Dataflow Apache Beam Python
In this GCP Project, you will learn to build a data pipeline using Apache Beam Python on Google Dataflow.

Log Analytics Project with Spark Streaming and Kafka
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.