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Based on our record, JsonAPI should be more popular than Google Cloud Dataflow. It has been mentiond 50 times since March 2021. 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.
For context, the subject-predicate-object pattern is known as a semantic triple or Resource Description Framework (RDF) triple: https://en.wikipedia.org/wiki/Semantic_triple They're useful for storing social network graph data, for example, and can be expressed using standards like Open Graph and JSONAPI: https://ogp.me https://jsonapi.org I've stored RDF triples in database tables and experimented with query... - Source: Hacker News / about 1 month ago
Built on JSON API standards, the OSF API is intuitive for anyone familiar with REST conventions. Once you learn its core patterns, you can quickly expand into project creation, user collaboration, and more—without constantly referencing documentation. The official OSF API docs provide everything needed to get started. - Source: dev.to / about 2 months ago
Following established patterns reduces the learning curve for your API. Adopt conventions from JSON:API or Microsoft API Guidelines to provide consistent experiences. - Source: dev.to / 3 months ago
I’ve used both GraphQL and REST in the past. From json:api to Relay, each approach for building APIs has its pros and cons. However, a constant challenge is choosing between code-first and schema-first approaches. - Source: dev.to / 8 months ago
There is a group of people who set out to standardize JSON responses into a single response style, either for returning single or multiple resources. You can take their style as a reference when designing their API to ensure uniformity of responses. - Source: dev.to / about 1 year ago
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
ReqRes - A hosted REST-API ready to respond to your AJAX requests.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
graphql.js - A reference implementation of GraphQL for JavaScript - graphql/graphql-js
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Prisma GraphQL API - Prisma helps modern applications access and manipulate data through a unified data layer
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.