Overview

Google Cloud Dataflow

Stacks223

Followers497

Votes19

AWS Step Functions

Stacks240

Followers391

Votes31

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

AWS Step Functions vs. Google Cloud Dataflow

AWS Step Functions and Google Cloud Dataflow are cloud-based services that enable developers to build and execute data processing workflows. While both services allow for scalable data processing, there are several key differences between the two.

Data Processing Model: AWS Step Functions is a serverless workflow orchestrator that allows developers to coordinate multiple AWS Lambda functions and other services. It provides a visual representation of the workflow using state machines and allows for easy tracking, logging, and error handling. On the other hand, Google Cloud Dataflow is a fully managed service based on Apache Beam that focuses on parallel data processing. It allows developers to define data pipelines using a programming model that supports both batch and stream processing.
Language Support: AWS Step Functions provides native support for AWS Lambda functions and other AWS services, allowing developers to build workflows using various programming languages supported by AWS Lambda. In contrast, Google Cloud Dataflow supports multiple programming languages, including Java, Python, and Go, allowing developers to choose language based on their preference and existing codebase.
Integration with Ecosystem: AWS Step Functions integrates seamlessly with various AWS services such as AWS Lambda, Amazon SNS, Amazon SQS, and more. It leverages existing AWS authentication and authorization mechanisms, making it easy to interact with other AWS services. Google Cloud Dataflow, on the other hand, integrates well with other Google Cloud services such as BigQuery, Cloud Pub/Sub, and Cloud Storage. It leverages Google Cloud IAM for authentication and authorization.
Cost Model: AWS Step Functions bills based on the number of state transitions and the time taken to execute a state machine. It also charges for AWS Lambda invocations and other services used within the workflows. Google Cloud Dataflow, on the other hand, bills based on the actual data processed and the number of workers utilized during the data processing. Depending on the specific workload, the cost model of each service can vary.
Managed Service Offering: AWS Step Functions is a fully managed service where AWS handles infrastructure provisioning, scaling, and maintenance. Developers can focus on building and deploying workflows without worrying about the underlying infrastructure. Google Cloud Dataflow is also a fully managed service, abstracting away the complexities of managing and scaling data processing infrastructure. Developers can take advantage of the managed service offerings of both platforms.
Community and Ecosystem: AWS Step Functions benefits from the large AWS community and marketplace, providing access to a broad range of pre-built integrations and extensions. Google Cloud Dataflow also benefits from the vibrant Google Cloud community and ecosystem, with support from Google and various third-party libraries and tools.

In summary, while both AWS Step Functions and Google Cloud Dataflow provide scalable and managed solutions for data processing workflows, AWS Step Functions focus on orchestrating serverless functions and AWS services, whereas Google Cloud Dataflow emphasizes parallel data processing using a variety of programming languages. The choice between the two services depends on the specific requirements and preferences of the development team.

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Detailed Comparison

Google Cloud Dataflow	AWS Step Functions
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 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.
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;	-
Statistics
Stacks 223	Stacks 240
Followers 497	Followers 391
Votes 19	Votes 31
Pros & Cons
Pros 7 Unified batch and stream processing 5 Autoscaling 4 Fully managed 3 Throughput Transparency	Pros 7 Integration with other services 5 Complex workflows 5 Pricing 5 Easily Accessible via AWS Console 3 High Availability

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

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.

Amazon Kinesis Firehose

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

Google Keep

It is a note-taking service developed by Google. It is available on the web, and has mobile apps for the Android and iOS mobile operating systems. Keep offers a variety of tools for taking notes, including text, lists, images, and audio.

Amazon SWF

Amazon Simple Workflow allows you to structure the various processing steps in an application that runs across one or more machines as a set of “tasks.” Amazon SWF manages dependencies between the tasks, schedules the tasks for execution, and runs any logic that needs to be executed in parallel. The service also stores the tasks, reliably dispatches them to application components, tracks their progress, and keeps their latest state.

Workfront

It allows user to manage projects in one place. It helps marketing, IT, & enterprise teams conquer chaos by improving productivity, collaboration, and visibility.

Taskworld

It is designed to facilitate project and task management, collaboration, delegation, communication, knowledge management, measure progress and provide performance metrics for evidence-based evaluations within teams.

Twister2

It is a high-performance data processing framework with capabilities to handle streaming and batch data. It can leverage high-performance clusters as well we cloud services to efficiently process data.