Google Cloud Data Fusion vs Google Cloud Dataflow: What are the differences?

Google Cloud Data Fusion and Google Cloud Dataflow are two popular services offered by Google Cloud Platform for working with large-scale data processing and analytics. Here are the key differences between them:

1. Data Integration vs Data Processing: Google Cloud Data Fusion is primarily designed for data integration tasks, allowing users to easily ingest, transform, and integrate data from various sources into a unified and actionable format. It provides a visual interface and pre-built connectors for seamless data integration workflows. On the other hand, Google Cloud Dataflow is focused on large-scale data processing and analytics. It allows users to build and run data processing pipelines using Apache Beam, which is an open-source unified programming model for batch and stream processing. Dataflow provides a scalable and fully managed service for executing data processing jobs in parallel.

2. Managed vs Customizable: Google Cloud Data Fusion is a fully managed service where Google takes care of the infrastructure, maintenance, and scaling aspects. It provides a low-code development environment with drag-and-drop capabilities, making it easy for users to create data integration workflows without worrying about the underlying infrastructure. In contrast, Google Cloud Dataflow provides more flexibility and customization options for users. It allows users to write custom Apache Beam code to define their data processing pipelines and provides control over the execution environment. Users can choose to run Dataflow pipelines on managed infrastructure or on their own infrastructure using Dataflow SDKs.

3. Real-time vs Batch Processing: Google Cloud Data Fusion is well-suited for batch data integration tasks where data can be processed in bulk and transformed incrementally. It provides tools and capabilities for efficiently handling large volumes of data in a batch-oriented manner. Alternatively, Google Cloud Dataflow is designed for both batch and real-time data processing. It supports continuous streaming and allows users to process data in real-time as it arrives. Dataflow provides windowing and triggering capabilities for handling streaming data and enables users to perform real-time analytics and actions.

4. Pricing Model: Google Cloud Data Fusion follows a subscription-based pricing model, where users pay for the specific edition and the number of nodes used. The pricing is based on the specific requirements and usage needs of the users. On the other hand, Google Cloud Dataflow follows a pay-as-you-go model, where users are billed based on the actual usage of processing resources (CPU, memory, etc.) during the execution of data processing pipelines. The pricing is based on the amount of data processed and the duration of pipeline execution.

5. Pre-built Connectors vs Polyglot Support: Google Cloud Data Fusion provides a wide range of pre-built connectors for seamless integration with various data sources and platforms. These connectors are designed to work out-of-the-box and provide configuration options for easily accessing and transforming data from different systems. In contrast, Google Cloud Dataflow offers polyglot support, allowing users to write pipelines using multiple programming languages such as Java, Python, and Go. It provides a flexible and extensible programming model for building data processing pipelines using the language of choice.

In summary, Google Cloud Data Fusion is a managed service focused on data integration tasks, providing a visual interface and pre-built connectors, while Google Cloud Dataflow is a customizable service for large-scale data processing and analytics, offering support for both batch and real-time processing, custom code, and polyglot support.