Google Cloud Dataflow vs Google Cloud Functions vs Google Cloud Run
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization. | Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running | A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management. |
Fully managed;
Combines batch and streaming with a single API;
High performance with automatic workload rebalancing
Open source SDK; | - | Simple developer experience;
Fast autoscaling;
Managed;
Any language, any library, any binary;
Leverage container workflows and standards;
Redundancy;
Integrated logging and monitoring;
Built on Knative;
Custom domains |
Statistics | ||
Stacks 219 | Stacks 479 | Stacks 292 |
Followers 497 | Followers 479 | Followers 243 |
Votes 19 | Votes 25 | Votes 62 |
Pros & Cons | ||
Pros
| Pros
Cons
| Pros
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Integrations | ||
| No integrations available | ||
What are some alternatives to Google Cloud Dataflow, Google Cloud Functions, Google Cloud Run?
AWS Lambda
AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.
Azure Functions
Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
Serverless
Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
Knative
Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center
OpenFaaS
Serverless Functions Made Simple for Docker and Kubernetes
Nuclio
nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.
Amazon Kinesis
Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
Apache OpenWhisk
OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.
Cloud Functions for Firebase
Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.
AWS Batch
It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
