Why GraphQL Is Perfect For Microservices - CityJS London 2022

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ApiDays Zurich Keynote 2017 Gareth Jones

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Neo4j is a high performance, open-source graph database written in Java and Scala. But of course, you can use and extend it from other JVM Languages, like Kotlin. I've been using Kotlin on and off since 2012, that's when I wrote my first article about this pragmatic and clean programming language. Today I want to demonstrate in 4 examples how you can use Neo4j with Kotlin. In preparation for KotlinConf 2017 we started to gather community activity of the Kotlin community in a Neo4j graph. You can see tweets, GitHub projects, StackOverflow questions and answers and meetup events in this database of developer activity. After showing some examples we will explore how to query that database using a Kotlin driver for Neo4j. Then we'll look at the Kotlin + Spring (Data) demo app, that shows community members who get a lot of praise on a Twitter wall. My main Kotlin project over the last year has been the GraphQL extension for Neo4j. So we'll have a look at how we can extend Neo4j with a custom HTTP API and how we integrate the GraphQL-Java library using Kotlin. This extension manages a GraphQL schema to translate GraphQL queries to Neo4js native query language Cypher. Another way to extend Neo4j is with user-defined procedures and functions which (with a small trick) are really easy to implement with Kotlin too. We'll take a look at the procedures and functions we expose as part of the neo4j-graphql extension.

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iMasters

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Why GraphQL is Perfect for Node.js Microservices - IJS London 2022

GraphQL and microservices architectures are perfectly compatible. You can use GraphQL as a data layer for these microservices and combine data from all these services into one universal API. This way, you can break the core of your services into different microservices while still providing one endpoint that your clients can interact with. In this talk, we dive into GraphQL, microservices in Node.js, and why they are the perfect combination. Find the code example here https://github.com/stepzen-dev/stepzen-datalayer-microservices

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Unleash office 365 with the power of cognitive services and microsoft graph api

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GraphQL and microservices architectures are perfectly compatible. You can use GraphQL as a data layer for these microservices and combine data from all these services into one universal API. This way, you can break the core of your services into different microservices while still providing one endpoint that your clients can interact with. In this talk, we dive into GraphQL, microservices in Node.js, and why they are the perfect combination. Find the code example here https://github.com/stepzen-dev/stepzen-datalayer-microservices

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Have you used GraphQL yet? If you haven't, you're likely to encounter the technology soon. If you have, you might have questions about how to scale your team and what best practices large and small companies are following. What principles should I be following when developing a new GraphQL API? What tooling is available to help me be more productive and spend less time writing boilerplate code? How can many teams with many apps across one company share one graph? GraphQL Conf 2019 just wrapped up in Berlin where I was fortunate to hear meet thoughtful leaders on these and other subjects related to GraphQL. So, whether you're brand new to GraphQL or have an active project using GraphQL, I'd like to share some of what I have learned so you can be productive with GraphQL and your teams - while keeping relationships at the center of our work.

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This document discusses using GraphQL to wrap and secure REST APIs. It describes problems with REST APIs like multiple endpoints, over-fetching and under-fetching data, and versioning issues. GraphQL solves these problems with a single endpoint that returns flexible data structures. The document then provides steps to implement GraphQL, including using schemas to define queries and mutations, retrieving data from existing services and APIs, adding authentication middleware, and building a GraphQL server with Node.js and Apollo.

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Apache Airflow is a platform to programmatically author, schedule and monitor workflows. Airflow is not a data streaming solution. Tasks do not move data from one to the other (though tasks can exchange metadata!). Airflow is not in the Spark Streaming or Storm space, it is more comparable to Oozie or Azkaban. It's primary goal is to solve problem nicely described in this XKCD comic (https://xkcd.com/2054/) What's unique about Airflow is that it brings "infrastructure as a code" concept to building scalable, manageable and elegant workflows. Workflows are defined as Python code - thus making dynamic workflow possible. It provides hundreds of out-of-the-box Operators that allow your pipeline to tap into pretty much any resource possible - starting from resources from multiple cloud providers as well as on-the-premises systems of yours. It's super-easy to write your own operators and leverage the power of data pipeline infrastructure provided by Airflow. This talk will be about general concepts behind Airflow - how you can author your workflow, write your own operators and run and monitor your pipelines. It will also explain how you can leverage Kubernetes (in recent release of Airflow) to make use of your on-premises or in-the-cloud infrastructure efficiently. You leave the talk armed with enough knowledge to evaluate if Airflow is good for you to solve your data pipeline problems and get some insight from Airflow contributors in case you are already an Airflow user.

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