nodejs-storage VS amplify-cli

Cloud Storage: blog storage for static assets and media files

Preevy includes built-in support for saving profiles on AWS S3 and Google Cloud Storage. You can also store the profile on the local filesystem and copy it manually before running Preevy - we won't show this method here.

Google Cloud Storage{:target="_blank"} is a globally distributed object storage service offered by Google Cloud Platform. They provide trustworthy and scalable databases for storing large amounts of blob data. They also provide a way to optimize cost and performance with different storage classes and pricing options.

Google Cloud Storage - https://cloud.google.com/storage/

Also, in terms of packing a pre-trained model you will probably want to puts weights, biases etc into S3 or similar object storage (https://cloud.google.com/storage etc) and load it on application start

An object storage system (e.g. Amazon S3, Azure Blob Storage, Google Cloud Platform Cloud Storage, etc.) makes it easy and simple to save large amounts of historical data and retrieve it for future use.

ToolJet allows you to build applications that use relational and non-relational databases, REST APIs, and cloud storage like Google Cloud Storage, AWS S3, and Minio. It is an excellent development tool helping individuals, developers, and businesses create and ship products faster.

Google Cloud Storage is a flexible, scalable, and cost-effective option for hosting a static website. It offers features like custom domains and SSL certificates, and it's easy to use. However, it can be more expensive than some other options for high-traffic websites.

There are a few ways to handle this depending on your client's preferences. If your client still wants to own the data, we support exporting to Object Storage systems like S3, GCS, R2, etc. This will keep the data out of their production systems but allow them to query it on demand.

Of course, the storage layer is the place where the data is stored. Because the amount of data to be stored is huge, we can not use HDD or SSD data storages, we need something cheaper. In this case we will be talking about AWS S3 because we are working with Amazon Web Services. For Azure, you could use Azure Data Lake Storage Gen2. If you are working with Google Cloud, you could use Google Cloud Storage. It does not matter what storage you use as long as it is cheap and can store a huge amount of data.

Bug: Before continuing, we need to add some manual changes to the generated output since there is a bug in the 10.8.1 version of the Amplify CLI. To fix the issue, open the amplify/backend/predictions/identifyText<>/parameters.json file and add the following three key-value pairs to it:

AWS Amplify CLI - GitHub

If this is the first time using Amplify you'll need to install the Amplify CLI. This tool will help us setup and add Amplify's services.

This disconnection between the initial business cases of DynamoDB and Amplify can even be seen within the AWS teams themselves. [4] We don't believe any of them are to blame. The solo Front End Engineer bootstrapping a quick Amplify app for a PoC feels like a way different use case than a team of highly trained data engineers working on their Single Table Design for their micro-service. Amplify rightfully tries to offer an easy way to deal with storing data. And so it follows a standard SQL design with DynamoDB. This though leads to bad performance (original selling point of DynamoDB) or other limitations hard to anticipate.

Overall it is pretty clear and fine that Amplify focuses on PoC projects rather than production ones (with features like Geo-tagging [5] but no way to migrate data). However, when starting to get traction, it is a shame we need to completely eject instead of being able to extend because of lack of (boring but necessary) fundamentals.

[1] https://github.com/aws-amplify/amplify-cli/issues/10164

Unfortunately, and this is a bad look for AWS, Amplify still hasn't migrated to CDK v2, meaning they're still using the maintenance-only CDK v1, which is incredibly easy to break due to incompatible packages, and isn't getting any new features. Further, Amplify's implementation of CDK (I think it's the StackSynthesizer subclass) doesn't even support some of the most productive features of CDK like the ability to create a Lambda image from a Dockerfile. You have to define and push the image separately which misses the point of having the entire app defined within Amplify.

Hi u/recursivebob , can you please submit an issue to the Amplify CLI on Github using this link

Hi all, for a new project I need to create a web app and mobile app and would like to hear your opinion on what would be the right architecture. The architecture has two peculiarities, it needs: - a service for crawling/scraping and writing into a (document) DB - full search text support for the crawled/scraped data Since the budget for running is low and time is tight, I was looking into serverless and BaaS architectures. I like the idea of using a BaaS architecture so I was I thinking if Firebase or AWS Amplify are a good fit. Regarding the full search text, Firestore does not support full text search out of the box, but it is possible to use Elastic, Algolia, Typesense (https://firebase.google.com/docs/firestore/solutions/search). With AWS Amplify it's apparently even more easier - it appears that it can be directly connected to AWS ElasticSearch. Regarding the crawling/scraping, I could use e.g. EC2 instances. Now from the time perspective looks good, but not from the budget perspective. As I found here: https://github.com/aws-amplify/amplify-cli/issues/3860 an ElasticSearch Instance on AWS can cost 70$ for 2 hours!!! This is around the budget for a whole month... So what you guys would advice me? Are there any alternatives? How can I move fast with a minimal cost?

AWS Amplify code is open-sourced and could be found on GitHub at https://github.com/aws-amplify/amplify-cli. We could make use of the NPM packages deployed independently to recreate the functionality offered by the AWS Amplify CLI regarding the GraphQL transformers. The entry point for generating the Appsync resolvers could be found here and the class we are interested in is GraphQLTransform which takes as a parameter all the individual transformers and iterates over them to generate the GraphQL resolvers and the associated CloudFormation stacks for deploying those resolvers.

Can you check that issue: https://github.com/aws-amplify/amplify-cli/issues/6338

AWS Amplify - The AWS Amplify CLI is a toolchain which includes a robust feature set for simplifying mobile and web application development. The CLI uses AWS CloudFormation and nested stacks to allow you to add or modify configurations locally before you push them for execution in your account.