Why is persistent file storage important for modern application development?
Development teams are modernizing their applications by adopting containers, serverless, and microservices-based architectures. Because containers are transient in nature, long-running applications can benefit from keeping state in durable storage. Distributed applications like machine learning training, and web serving, benefit from a shared storage layer. Amazon Elastic File System (Amazon EFS) is a simple, serverless, set-and-forget, cloud native file system, enabling you to build modern applications, persist and share data from your AWS containers and serverless applications, with zero management required.
Data persistence for your serverless applications
Serverless computing enables you to be more agile while spending less time dealing with the security, scalability and availability of your applications. At the same time, modern data-intensive applications require fast access to large volumes of shared data. AWS Lambda lets you run large-scale, and mission-critical serverless applications. Amazon EFS provides highly available and durable serverless storage for those applications, and simplifies the sharing of data that needs to persist beyond and between executions of Lambda functions and AWS Fargate tasks. This powerful combination is ideal for building machine learning applications, loading large models, libraries, and other reference data, processing and backing up large amounts of data, hosting web content, and developing internal build systems.
How it works
Web serving and content management
Web serving and content management systems require shared data access across multiple containerized application instances, data persistence, and data durability. Examples include applications like WordPress and Drupal, that benefit from scaling out to multiple instances for performance and redundancy, and need to share uploads, plugins, and templates.
A stateful microservice is part of a loosely coupled application that needs to remember some things about its state each time it runs, as in the way a weather application remembers your home city. For these modern applications, Amazon EFS is a data foundation, operating in concert with containers and serverless technologies to reliably and consistently deploy to AWS, allowing data to persist application state.
ML and AI
Containers are a way to deploy machine learning (ML) training jobs, inference endpoints, and tools that run consistently in multiple environments. Using containers can solve problems such as consistency, portability, and dependency management that complicate matters for data scientists and developers whose focus should really be on their code, datasets, and training models, not on infrastructure. ML tools like Amazon SageMaker Notebooks, as well as open source tools like Jupyter use Amazon EFS to manage data scientist home directories. Amazon FSx for Lustre can be used for your most compute-intensive ML training jobs where the highest throughput is required.
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