AWS Import/Export vs Google BigQuery Data Transfer Service

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AWS Import/Export

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Google BigQuery Data Transfer Service

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AWS Import/Export vs Google BigQuery Data Transfer Service: What are the differences?

AWS Import/Export: Transfer your data directly onto and off of storage devices using Amazon’s internal network and bypassing the Internet. 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; Google BigQuery Data Transfer Service: Automate data movement from SaaS applications to Google BigQuery on a scheduled, managed basis. 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.

AWS Import/Export and Google BigQuery Data Transfer Service can be primarily classified as "Data Transfer" tools.

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What 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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What companies use AWS Import/Export?
What companies use Google BigQuery Data Transfer Service?
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What tools integrate with AWS Import/Export?
What tools integrate with Google BigQuery Data Transfer Service?
What are some alternatives to AWS Import/Export and Google BigQuery Data Transfer Service?
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.
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.
NPOI
It is a .NET library that can read/write Office formats without Microsoft Office installed. No COM+, no interop.
Embulk
It is an open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.
HTTP/2
It's focus is on performance; specifically, end-user perceived latency, network and server resource usage.
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