GraphQL
gRPC
Next.js
React
Prisma
SQLAlchemy
Hibernate
Nest.js
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
GraphQL Amazon EMRBased on our record, GraphQL seems to be a lot more popular than Amazon EMR. While we know about 229 links to GraphQL, we've tracked only 10 mentions of Amazon EMR. 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.
A headless CMS delivers content using APIs, often RESTful or GraphQL, allowing front-end developers to fetch material dynamically. This strategy makes it easier to integrate content into apps and helps developers structure their data retrieval more efficiently. - Source: dev.to / about 15 hours ago
In recent years, GraphQL has emerged as a powerful alternative to REST for API development. Created by Facebook in 2012 and open-sourced in 2015, GraphQL offers a more flexible and efficient approach to data querying and manipulation. If you're considering migrating your existing REST APIs to GraphQL, this step-by-step guide will help you navigate the transition smoothly. - Source: dev.to / 13 days ago
Feel free to check out the official documentation of Apollo Server and GraphQL for more detailed information on advanced topics and best practices. Happy coding! - Source: dev.to / 15 days ago
Before starting the tutorial on developing a personal target tracking application with Flutter, Riverpod, Strapi, and GraphQL, ensure you meet the following requirements:. - Source: dev.to / 22 days ago
On the other hand, GraphQL is a query language for APIs that was developed by Facebook. It allows clients to specify exactly what data they need, and the server responds with only that data. - Source: dev.to / about 1 month ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: over 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
gRPC - Application and Data, Languages & Frameworks, Remote Procedure Call (RPC), and Service Discovery
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Next.js - A small framework for server-rendered universal JavaScript apps
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
React - A JavaScript library for building user interfaces
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost