Datastream and Data Fusion are both powerful services provided by Google Cloud to handle data integration tasks, but they have some important differences:
Datastream is a serverless, real-time change data capture and replication service that provides access to streaming, low-latency data from databases like MySQL, PostgreSQL, AlloyDB, and Oracle. It allows for near real-time analytics in BigQuery, offers a simple setup with secure connectivity, and automatically scales with no infrastructure to manage. Datastream reads and delivers every change from your databases (insert, update, delete) to load data into BigQuery, CloudSQL, GCS, and Spanner. It also normalizes data types across sources for easier downstream processing and handles schema drift resolution. In terms of security, it supports multiple secure, private connectivity methods, and data is encrypted in transit and at rest.
See https://cloud.google.com/datastream
Data Fusion is a fully managed, cloud-native data integration service that offers a visual point-and-click interface for code-free deployment of ETL/ELT data pipelines. It includes a broad library of preconfigured connectors and transformations and integrates natively with Google Cloud services. It's built with an open-source core (CDAP) for pipeline portability, allowing for data pipeline portability across on-premises and public cloud platforms. It also has built-in features for data governance, including end-to-end data lineage, integration metadata, and cloud-native security and data protection services. Additionally, it allows for data integration through collaboration and standardization, offering pre-built transformations for both batch and real-time processing and the ability to create an internal library of custom connections and transformations.
See https://cloud.google.com/data-fusion/
In a nutshell, while both services offer data integration capabilities, Datastream focuses on real-time change data capture and replication from databases, while Data Fusion provides a more extensive toolset for building and managing ETL/ELT pipelines with a visual interface, built-in connectors, and transformations. Datastream would be a better choice if your main requirement is real-time, low-latency data replication with automatic schema handling. On the other hand, Data Fusion would be more suitable if you need to build complex data pipelines with a visual interface, need a broad set of connectors and transformations, or require pipeline portability across different environments
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