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AWS Data Pipeline vs Google Cloud Dataflow: What are the differences?
AWS Data Pipeline: Process and move data between different AWS compute and storage services. 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; Google Cloud Dataflow: A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
AWS Data Pipeline can be classified as a tool in the "Data Transfer" category, while Google Cloud Dataflow is grouped under "Real-time Data Processing".
Some of the features offered by AWS Data Pipeline are:
- You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
- Hourly analysis of Amazon S3‐based log data
- Daily replication of AmazonDynamoDB data to Amazon S3
On the other hand, Google Cloud Dataflow provides the following key features:
- Fully managed
- Combines batch and streaming with a single API
- High performance with automatic workload rebalancing Open source SDK
Pros of AWS Data Pipeline
- Easy to create DAG and execute it1
Pros of Google Cloud Dataflow
- Unified batch and stream processing5
- Fully managed3
- Throughput Transparency1