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AWS Data Pipeline vs Google BigQuery Data Transfer Service: What are the differences?
- Supported platforms: AWS Data Pipeline is a fully managed service that allows you to define data processing workflows, while Google BigQuery Data Transfer Service enables you to schedule and automate data imports into BigQuery from external sources such as Google Cloud Storage or JDBC databases.
- Integration capabilities: AWS Data Pipeline integrates with various AWS services like Amazon S3, RDS, and Redshift, allowing you to easily move and process data across different AWS tools. On the other hand, Google BigQuery Data Transfer Service mainly focuses on importing data into BigQuery, limiting its integration capabilities compared to AWS Data Pipeline.
- Pricing model: AWS Data Pipeline follows a pay-as-you-go model where you only pay for the resources you use, including the number of pipelines and the duration they run. In contrast, Google BigQuery Data Transfer Service offers free transfers for certain sources, but charges for data transfer and storage, potentially leading to different cost structures for users.
- Data transformation options: AWS Data Pipeline offers data transformation capabilities through activities like data copy, SQL transformation, and EMR cluster execution, allowing for more complex data processing tasks within the pipeline. Google BigQuery Data Transfer Service primarily focuses on transferring and loading data into BigQuery, with limited support for data transformation operations within the service.
- Data source support: AWS Data Pipeline supports a wide range of data sources and destinations, including on-premises systems, cloud storage, and various AWS services, providing flexibility in managing data workflows across different environments. Comparatively, Google BigQuery Data Transfer Service is optimized for importing data into BigQuery from specific sources like Google Cloud Storage, limiting its compatibility with a diverse set of data platforms.
- Real-time processing: AWS Data Pipeline supports near real-time data processing through features like event triggers and on-demand pipeline activation, facilitating quicker processing of data as soon as it becomes available. In contrast, Google BigQuery Data Transfer Service does not offer real-time processing capabilities and focuses more on scheduled batch data transfers to BigQuery.
In Summary, AWS Data Pipeline and Google BigQuery Data Transfer Service differ in their supported platforms, integration capabilities, pricing models, data transformation options, data source support, and real-time processing capabilities.
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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 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 tools integrate with AWS Data Pipeline?
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What are some alternatives to AWS Data Pipeline and Google BigQuery Data Transfer Service?
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.
Airflow
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.