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AWS Step Functions

Stacks220

Followers363

+ 1

Votes24

Google Cloud Dataflow

Stacks209

Followers444

+ 1

Votes13

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AWS Step Functions vs Google Cloud Dataflow: What are the differences?

AWS Step Functions: Build Distributed Applications Using Visual Workflows. AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly; Google Cloud Dataflow: A fully-managed cloud service and programming model for batch and streaming big data processing. 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.

AWS Step Functions belongs to "Cloud Task Management" category of the tech stack, while Google Cloud Dataflow can be primarily classified under "Real-time Data Processing".

According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to AWS Step Functions, which is listed in 19 company stacks and 7 developer stacks.

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Pros of AWS Step Functions

Pros of Google Cloud Dataflow

6
Integration with other services
4
Easily Accessible via AWS Console
4
Complex workflows
4
Pricing
2
Scalability
2
High Availability
2
Workflow Processing

5
Unified batch and stream processing
4
Autoscaling
3
Fully managed
1
Throughput Transparency

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What companies use AWS Step Functions?

What companies use Google Cloud Dataflow?

See which teams inside your own company are using AWS Step Functions or Google Cloud Dataflow.

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What tools integrate with AWS Step Functions?

What tools integrate with Google Cloud Dataflow?

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What are some alternatives to AWS Step Functions and Google Cloud Dataflow?

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.

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

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.

AWS Data Pipeline

AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.

Batch

Yes, we’re really free. So, how do we keep the lights on? Instead of charging you a monthly fee, we sell ads on your behalf to the top 500 mobile advertisers in the world. With Batch, you earn money each month while accessing great engagement tools for free.

See all alternatives

AWS Step Functions vs Google Cloud Dataflow