๐ Maximize the Potential of a Well-Planned GraphQL Schema: Elevate Your Project! ๐
Looking to elevate your project? Discover the game-changing benefits of a well-planned GraphQL schema. ๐
In modern API development, GraphQL has revolutionized flexibility, efficiency, and scalability. A meticulously crafted schema lies at the core of every successful GraphQL implementation, enabling seamless data querying and manipulation. ๐ก
Explore the key advantages of a well-planned GraphQL schema for your project:
โค๏ธโ๐ฅ Precisely define data requirements for each API call. GraphQL's query language empowers clients to request specific data, reducing over-fetching and network traffic This control ensures lightning-fast responses and a superior user experience.
โค๏ธโ๐ฅ Act as a contract between frontend and backend teams, providing clear guidelines for data exchange. Developers can work independently on components, without waiting for API modifications. This decoupling accelerates development and project delivery.
โค๏ธโ๐ฅ Anticipate future data requirements by easily adding, modifying, and deprecating with a well-designed schema. This saves development time and prevents disruptive changes down the line, making your project adaptable and future-proof.
โค๏ธโ๐ฅ GraphQL's self-documenting nature serves as a comprehensive source of truth, eliminating ambiguity. Developers can effortlessly explore and understand data and relationships, boosting productivity and code quality.
โค๏ธโ๐ฅ GraphQL's ability to batch and aggregate data from multiple sources optimizes backend operations By intelligently combining and caching data, you can enhance application performance, delivering lightning-fast experiences to users.
Embrace the power of a well-planned GraphQL schema to transform your project and unlock endless possibilities. Optimize data fetching, simplify development workflows, future-proof your application, enhance developer experience, and improve performance. ๐ช
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Datadog is a monitoring and analytics platform for cloud-scale application infrastructure. Combining metrics from servers, databases, and applications, Datadog delivers sophisticated, actionable alerts, and provides real-time visibility of your entire infrastructure. Datadog includes 100+ vendor-supported, prebuilt integrations and monitors hundreds of thousands of hosts.
GraphQl Editor might be a bit more popular than Datadog. We know about 6 links to it since March 2021 and only 5 links to Datadog. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Aside from the ones mentioned graphql editor has a bunch of features that are helpful for testing like a click-out creator and a built-in mock backend for testing queries. Source: over 1 year ago
I may be wrong, but something like graphqleditor is geared more towards setting up GraphQL API/server, in Supabase case, it's database - Postgres, is the server/API. Source: about 2 years ago
I've tried graphqleditor.com but I can't get my my supabase API url to connect [mysupabaseurl].supabase.co/graphql/v1. Source: about 2 years ago
Https://graphqleditor.com/ New version is available here. Source: over 2 years ago
Make your schema and code to that. Here's a tool to help visualize. I've personally never found it useful, but maybe that's just me. Https://graphqleditor.com/. Source: over 2 years ago
Ideally, if we had access to the underlying infrastructure, we could probably install the Datadog Agent and configure it to send our logs directly to Datadog, or even use AWS Lambda functions or Azure Event Hub + Azure Functions in case we were facing some specific cloud scenarios. - Source: dev.to / 9 months ago
Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 1 year ago
Datadog is a powerful monitoring and security platform that gives you visibility into end-to-end traces, application metrics, logs, and infrastructure. While Datadog has great documentation on their Kubernetes integration, we've observed that there's some missed nuance that leads to common pitfalls. - Source: dev.to / almost 3 years ago
.. Is to see you email address being silently distributed to every single company that I've watched a talk from. And now suddenly get several promotional spam emails per day from some 4-5 different domains like instana.com, datadoghq.com, snyk.io, cockroachlabs.com (some of them send even multiple emails per day!). Source: about 3 years ago
We're commonly doing this with logging, using services such as Loggly or DataDog. We're using managed databases, be it on AWS, Heroku or database-vendor-specific solutions. We're storing binaries on S3. Externalising user authentication and authorization might be a good candidate as well. - Source: dev.to / over 3 years ago
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