What is Deployd and what are its top alternatives?
Top Alternatives to Deployd
DreamFactory is an open source REST API backend for mobile, web, and IoT applications. It provides RESTful web services with pre-built connectors to SQL, NoSQL, file storage systems, and web services. It's secure, reusable, and offers live API documentation. ...
Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...
A highly-extensible, open-source Node.js framework that enables you to create dynamic end-to-end REST APIs with little or no coding. Connect to multiple data sources, write business logic in Node.js, glue on top of your existing services and data, connect using JS, iOS & Android SDKs. ...
We want to enable you to build complete web apps in days, without having to worry about backends, databases or servers, all with an open source library that's as simple to use as jQuery. ...
It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...
Amazon API Gateway
Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. ...
Insomnia REST Client
Insomnia is a powerful REST API Client with cookie management, environment variables, code generation, and authentication for Mac, Window, and Linux. ...
Deployd alternatives & related posts
related DreamFactory posts
related Firebase posts
This is my stack in Application & Data
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Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
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We are starting to work on a web-based platform aiming to connect artists (clients) and professional freelancers (service providers). In-app, timeline-based, real-time communication between users (& storing it), file transfers, and push notifications are essential core features. We are considering using Node.js, ExpressJS, React, MongoDB stack with Socket.IO & Apollo, or maybe using Real-Time Database and functionalities of Firebase.
related MongoDB posts
Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.
We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient
Based on the above criteria, we selected the following tools to perform the end to end data replication:
We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.
We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.
In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.
Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.
In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
related LoopBack posts
I use LoopBack because it is: * It is truly and Unbelievably Extensible * it is default integrated with OpenAPI (Swagger) Spec Driven REST API * I write lesser codes, because most of the user stories have been covered using the code generation * It's documentation is more compact and well detailed than ExpressJS * It is very easy to learn, hence you can build a basic Rest API App in minutes * It has built in NPM packages required to build my Rest API which saves me time on installation and configuration * The Datasource/Service/Controller concept is just Brilliant (that's mostly all you need to get your app speaking with an External API services) * The support for SOAP and Rest API services is amazing!
We inherited this project and the backend is using LoopBack v3. I haven't taken a look at Loopback.io v4, but I'm planning to replace it. The reason being is that Loopback v3 documentation is a bit confusing and we are having trouble packaging the build using Webpack. Not to mention, integrating unit tests (latest Jest).
I still think Loopback is a great tool, but their documentation is really "messy" and hard to navigate through. There's also a constraint of time from our side. So what's the best option out there?
Should I try upgrading to Loopback v4, or trying other stuff? (i.e. NestJS)
related Hoodie posts
related Postman posts
We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.
Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like
workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.
Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.
This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.
Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct
Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.
Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.
Our whole Node.js backend stack consists of the following tools:
- Lerna as a tool for multi package and multi repository management
- npm as package manager
- NestJS as Node.js framework
- TypeScript as programming language
- ExpressJS as web server
- Swagger UI for visualizing and interacting with the API’s resources
- Postman as a tool for API development
- TypeORM as object relational mapping layer
- JSON Web Token for access token management
The main reason we have chosen Node.js over PHP is related to the following artifacts:
- Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
related Amazon API Gateway posts
related Insomnia REST Client posts
We've tried a couple REST clients over the years, and Insomnia REST Client has won us over the most. Here's what we like about it compared to other contenders in this category:
- Uncluttered UI. Things are only in your face when you need them, and the app is visually organized in an intuitive manner.
- Native Mac app. We wanted the look and feel to be on par with other apps in our OS rather than a web app / Electron app (cough Postman).
- Easy team sync. Other apps have this too, but Insomnia's model best sets the "set and forget" mentality. Syncs are near instant and I'm always assured that I'm working on the latest version of API endpoints. Apps like Paw use a git-based approach to revision history, but I think this actually over-complicates the sync feature. For ensuring I'm always working on the latest version of something, I'd rather have the sync model be closer to Dropbox's than git's, and Insomnia is closer to Dropbox in that regard.
Some features like automatic public-facing documentation aren't supported, but we currently don't have any public APIs, so this didn't matter to us.