AWS Step Functions vs Amazon Kinesis: What are the differences?

Introduction

In this article, we will discuss the key differences between AWS Step Functions and Amazon Kinesis. Both services are offered by Amazon Web Services (AWS) and serve different purposes in managing and processing data.

1. Data Processing Technique: AWS Step Functions is a serverless visual workflow service that coordinates distributed applications and microservices using various AWS services. It allows developers to build, monitor, and scale applications that are composed of multiple steps, also known as workflows. On the other hand, Amazon Kinesis is a real-time streaming data platform that collects, processes, and analyzes large amounts of data in real-time, making it suitable for use cases such as log data analysis, IoT data ingestion, and real-time analytics.

2. Data Source: AWS Step Functions can handle workflows that involve processing data from various sources such as AWS Lambda functions, Amazon SNS, and Amazon DynamoDB. It integrates well with other AWS services and allows developers to orchestrate complex workflows seamlessly. In contrast, Amazon Kinesis is primarily focused on handling streaming data from sources like clickstreams, social media feeds, IoT devices, and log files. It provides a scalable infrastructure for collecting and processing real-time data streams.

3. Data Processing Patterns: AWS Step Functions employ workflows to model different states and transitions between steps. It provides a visual representation to define the sequence of tasks and conditions for each step. This enables developers to create complex and conditional workflows that can execute a series of tasks in parallel or sequentially. On the other hand, Amazon Kinesis allows for real-time streaming of data with the ability to process and transform data using Kinesis Data Analytics or custom applications. It provides capabilities for real-time aggregations, filtering, and transformations on data streams.

4. Latency and Durability: AWS Step Functions do not provide guarantees on real-time data ingestion or processing since it is focused on coordinating workflows rather than real-time streaming. However, it provides durability and ensures that the steps in a workflow are executed reliably. In contrast, Amazon Kinesis is designed for low-latency streaming of data, making it suitable for real-time processing and analytics use cases. It guarantees data durability by automatically replicating data across multiple availability zones.

5. Pricing Model: AWS Step Functions follow a pay-per-use pricing model based on the number of state transitions, allowing users to pay only for the resources consumed during workflow execution. In contrast, Amazon Kinesis follows a pricing model based on the amount of data ingested, data egress, and the number of shards used. The cost is calculated based on the volume of data processed by the Kinesis streams and the number of transactions performed.

6. Data Retention: AWS Step Functions do not store or retain data between state transitions. It allows passing data between steps as JSON objects, but the data is not stored persistently within the service. On the other hand, Amazon Kinesis provides the ability to retain data for up to 7 days by default, allowing users to have access to historical data and perform further analysis or processing on the stored streams.

In summary, AWS Step Functions and Amazon Kinesis are both valuable services offered by AWS, but they have significant differences in terms of their data processing techniques, data sources, data processing patterns, latency and durability, pricing models, and data retention capabilities.