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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Cloud Task Management
  5. Amazon SWF vs Google Cloud Dataflow

Amazon SWF vs Google Cloud Dataflow

OverviewComparisonAlternatives

Overview

Amazon SWF
Amazon SWF
Stacks35
Followers79
Votes0
Google Cloud Dataflow
Google Cloud Dataflow
Stacks219
Followers497
Votes19

Amazon SWF vs Google Cloud Dataflow: What are the differences?

Introduction

In this article, we will explore the key differences between Amazon SWF (Simple Workflow Service) and Google Cloud Dataflow. These two platforms offer workflow management and data processing capabilities, but they have notable distinctions that make them suitable for different use cases.

  1. Deployment Model: Amazon SWF is a fully managed service offered by Amazon Web Services (AWS). It provides a dedicated infrastructure for running and managing workflows. On the other hand, Google Cloud Dataflow is a serverless platform that abstracts away the infrastructure management, allowing users to focus solely on writing the data processing logic. It leverages the power of Apache Beam programming model for efficient data processing.

  2. Availability: Amazon SWF is available in multiple AWS regions, making it accessible to users worldwide. It also offers fault tolerance through the replication of workflows and workflow history. In contrast, Google Cloud Dataflow is currently only available in select regions, which may limit its usage for global deployments. However, it provides managed autoscaling and fault tolerance features out of the box.

  3. Workflow Complexity: Amazon SWF is well-suited for complex workflows with long-running steps, human intervention, and decider logic. It allows fine-grained control over workflow execution and supports retries, timeouts, and conditional branching. On the other hand, Google Cloud Dataflow is more focused on data processing pipelines. It simplifies the creation of parallel data transformations and optimizes resource utilization, making it a better choice for batch and stream processing scenarios.

  4. Integration Ecosystem: Amazon SWF integrates tightly with other AWS services, such as Amazon S3, Amazon EC2, and AWS Lambda, allowing seamless interaction with a wide range of cloud services. It also supports external integration via the use of Java, .NET, PHP, and other SDKs. In comparison, Google Cloud Dataflow integrates with various Google Cloud Platform services, including Google BigQuery, Google Cloud Pub/Sub, and Google Cloud Storage, offering a similar ecosystem for data handling and processing.

  5. Pricing Model: Amazon SWF pricing is based on the number of workflow executions, activity tasks, and data transfers. It provides a detailed breakdown of costs, allowing users to estimate their expenses accurately. On the other hand, Google Cloud Dataflow pricing is based on the number of Dataflow job steps, active CPUs, and data processed. It offers a more straightforward pricing structure that simplifies cost estimation and billing.

  6. Development Experience: Amazon SWF offers a visual console for managing workflows, monitoring their progress, and troubleshooting any issues. It provides a graphical representation of workflow states and supports additional features like task prioritization. In contrast, Google Cloud Dataflow utilizes the user interface provided by the Google Cloud Console, which offers a similar set of monitoring and management capabilities.

In Summary, Amazon SWF and Google Cloud Dataflow differ in deployment model, availability, workflow complexity, integration ecosystem, pricing model, and development experience. These distinctions make each platform suitable for specific use cases, allowing users to choose the workflow management and data processing solution that best fits their requirements.

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Detailed Comparison

Amazon SWF
Amazon SWF
Google Cloud Dataflow
Google Cloud Dataflow

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.

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.

Maintaining application state;Tracking workflow executions and logging their progress;Holding and dispatching tasks;Controlling which tasks each of your application hosts will be assigned to execute
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks
35
Stacks
219
Followers
79
Followers
497
Votes
0
Votes
19
Pros & Cons
No community feedback yet
Pros
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency

What are some alternatives to Amazon SWF, Google Cloud Dataflow?

AWS Step Functions

AWS Step Functions

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.

Amazon Kinesis

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.

Google Keep

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.

Earnings Feed API

Earnings Feed API

REST API for real-time SEC filings data. Access 10-K, 10-Q, 8-K filings and Form 4 insider transactions as they hit EDGAR. Filter by ticker, form type, or date range. Build alerts, power dashboards, or integrate into trading systems. Free tier available.

ZoomRadar

ZoomRadar

Offers live, customizable weather radar maps with real-time AI tornado detection and storm tracking powered by Level 2 Doppler data.

Amazon Kinesis Firehose

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.

Workfront

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

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

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.

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