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AWS Import/Export vs Google BigQuery Data Transfer Service: What are the differences?
Introduction
This Markdown code provides a comparison between AWS Import/Export and Google BigQuery Data Transfer Service.
1. Pricing Model: AWS Import/Export charges based on the number and size of physical devices used for data transfer, while Google BigQuery Data Transfer Service pricing is based on the amount of data transferred.
2. Transfer Methods: AWS Import/Export supports offline data transfer through physical devices like portable storage devices or hard drives, while Google BigQuery Data Transfer Service focuses on online data transfer using APIs.
3. Supported Data Sources: AWS Import/Export allows data transfer from various sources, including portable storage devices, file servers, and Amazon S3, while Google BigQuery Data Transfer Service enables data transfer from several cloud applications and databases.
4. Transformation Capabilities: AWS Import/Export does not offer transformation capabilities and requires data transformation before transfer, while Google BigQuery Data Transfer Service includes customizable transformation features during the transfer process.
5. Data Transfer Speed: AWS Import/Export has a slower data transfer speed when compared to Google BigQuery Data Transfer Service due to the offline nature of its transfer methods.
6. Transfer Monitoring: AWS Import/Export provides basic transfer monitoring through notifications and tracking, while Google BigQuery Data Transfer Service offers more advanced monitoring capabilities, including real-time status updates and detailed transfer logs.
In Summary, AWS Import/Export and Google BigQuery Data Transfer Service differ in their pricing models, transfer methods, supported data sources, transformation capabilities, data transfer speed, and transfer monitoring.
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Learn MoreWhat is AWS Import/Export?
Import/Export supports importing and exporting data into and out of Amazon S3 buckets. For significant data sets, AWS Import/Export is often faster than Internet transfer and more cost effective than upgrading your connectivity.
What is Google BigQuery Data Transfer Service?
BigQuery Data Transfer Service lets you focus your efforts on analyzing your data. You can setup a data transfer with a few clicks. Your analytics team can lay the foundation for a data warehouse without writing a single line of code.
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Requests
It is an elegant and simple HTTP library for Python, built for human beings. It allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data.
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.
NPOI
It is a .NET library that can read/write Office formats without Microsoft Office installed. No COM+, no interop.
HTTP/2
It's focus is on performance; specifically, end-user perceived latency, network and server resource usage.
Embulk
It is an open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.