AWS Data Pipeline vs AWS Snowball Edge

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AWS Data Pipeline vs AWS Snowball Edge: What are the differences?


In this analysis, we will compare the key differences between AWS Data Pipeline and AWS Snowball Edge.

  1. Integration Capabilities: AWS Data Pipeline is a web service that helps orchestrate and automate the movement and transformation of data between different AWS services and on-premises data sources. It provides integration with a wide range of AWS services, allowing seamless data movement across various components of a data pipeline. On the other hand, AWS Snowball Edge is a physical device used for offline data transfer. It offers robust storage and compute capabilities and is specifically designed for data migration, edge computing, and data collection in remote or disconnected environments.

  2. Data Transfer Method: AWS Data Pipeline transfers data using a network connection between different AWS services and data sources. It leverages APIs and network connectivity for data movement. In contrast, AWS Snowball Edge utilizes physical devices that are shipped to the customer's location. Customers can load their data onto the Snowball Edge device using a local network and then ship the device back to AWS for data transfer, ensuring fast and secure data movement.

  3. Use Cases: AWS Data Pipeline is ideal for building complex data workflows and coordinating the execution of data-oriented tasks, including data transformation, scheduling, and monitoring. It enables users to create and schedule data-driven workflows using a graphical interface or API. On the other hand, AWS Snowball Edge is primarily designed for scenarios where internet connectivity is limited or unreliable. It is commonly used for large-scale data transfers, data migration, and edge computing in remote locations or environments with restricted network access.

  4. Data Processing Capabilities: With AWS Data Pipeline, users can easily perform data transformations and manipulations using various AWS services, such as Amazon EMR (Elastic MapReduce), AWS Glue, or custom scripts. It provides the flexibility to process data in parallel and apply custom logic to transform the data within the pipeline. In contrast, AWS Snowball Edge focuses more on data storage and transportation. It provides large on-board storage capacity and the ability to run compute-heavy tasks on the device itself, using AWS Lambda functions.

  5. Scalability: AWS Data Pipeline is a fully managed service that automatically scales resources based on the workload, ensuring optimal performance and resource utilization. It can handle large-scale data processing and storage requirements efficiently. On the other hand, AWS Snowball Edge offers scalability through physical devices, allowing customers to transfer massive volumes of data by shipping multiple Snowball Edge devices. The footprint of Snowball Edge deployments can be increased as per the data transfer needs.

  6. Cost Structure: AWS Data Pipeline pricing is based on the number of pipeline activities and data processing hours. Users pay for the resources consumed during data transformation and processing stages. In contrast, AWS Snowball Edge pricing is based on the cost of the physical device and the data transfer job. Users pay for the Snowball Edge device rental, data transfer fees, and any additional compute or storage charges, if applicable.

In summary, AWS Data Pipeline is a service focused on orchestrating and automating data workflows within AWS services, while AWS Snowball Edge is a physical device designed for offline data transfer and storage. They differ in integration capabilities, data transfer methods, use cases, data processing capabilities, scalability, and cost structure.

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Pros of AWS Data Pipeline
Pros of AWS Snowball Edge
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What is 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.

What is AWS Snowball Edge?

AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.

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What tools integrate with AWS Data Pipeline?
What tools integrate with AWS Snowball Edge?
What are some alternatives to AWS Data Pipeline and AWS Snowball Edge?
AWS Glue
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
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 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.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
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.
See all alternatives