Cloud Data Fusion
Fully managed, cloud-native data integration at any scale.Try it free
Visual point-and-click interface enabling code-free deployment of ETL/ELT data pipelines
Broad library of 150+ preconfigured connectors and transformations, at no additional cost
Natively integrated best-in-class Google Cloud services
End-to-end data lineage for root cause and impact analysis
Built with an open source core (CDAP) for pipeline portability
Avoid technical bottlenecks and lift productivity
Data Fusion’s intuitive drag-and-drop interface, pre-built connectors, and self-service model of code-free data integration remove technical expertise-based bottlenecks and accelerate time to insight.
Lower total cost of pipeline ownership
A serverless approach leveraging the scalability and reliability of Google services like Dataproc means Data Fusion offers the best of data integration capabilities with a lower total cost of ownership.
Build with a data governance foundation
With built-in features like end-to-end data lineage, integration metadata, and cloud-native security and data protection services, Data Fusion assists teams with root cause or impact analysis and compliance.
Open core, delivering hybrid and multi-cloud integration
Data Fusion is built using open source project CDAP, and this open core ensures data pipeline portability for users. CDAP’s broad integration with on-premises and public cloud platforms gives Cloud Data Fusion users the ability to break down silos and deliver insights that were previously inaccessible.
Integrated with Google’s industry-leading big data tools
Data Fusion’s integration with Google Cloud simplifies data security and ensures data is immediately available for analysis. Whether you’re curating a data lake with Cloud Storage and Dataproc, moving data into BigQuery for data warehousing, or transforming data to land it in a relational store like Cloud Spanner, Cloud Data Fusion’s integration makes development and iteration fast and easy.
Data integration through collaboration and standardization
Cloud Data Fusion offers pre-built transformations as well as the ability to create an internal library of custom connections and transformations that can be validated, shared, and reused across teams. It lays the foundation of collaborative data engineering and improves productivity. That means less waiting for ETL developers and data engineers and, importantly, less sweating about code quality.
Star Media Group transforms into an engagement business with Cloud Data Fusion.Read the story
Transformation as a result of technology-driven change
Personalized, relevant content delivered to consumers
Requirements for cost-effectiveness and scalability met
Enabling Cloud Data Fusion
Learn how to enable the Cloud Data Fusion API for your Google Cloud project.
Cloud Data Fusion concepts overview
Learn about Cloud Data Fusion concepts and features.
Exploring data lineage
This tutorial shows how to use Cloud Data Fusion to explore data lineage: the data's origins and its movement over time.
Using JDBC drivers with Cloud Data Fusion
Discover how to use Java Database Connectivity (JDBC) drivers with Cloud Data Fusion pipelines.
Data engineering on Google Cloud
Learn firsthand how to design and build data processing systems on Google Cloud with this four-day instructor-led class.
Cloud Data Fusion helps users build scalable, distributed data lakes on Google Cloud by integrating data from siloed on-premises platforms. Customers can leverage the scale of the cloud to centralize data and drive more value out of their data as a result. The self-service capabilities of Cloud Data Fusion increase process visibility and lower the overall cost of operational support.
Cloud Data Fusion can help organizations better understand their customers by breaking down data silos and enabling development of agile, cloud-based data warehouse solutions in BigQuery. A trusted, unified view of customer engagement and behavior unlocks the ability to drive a better customer experience, which leads to higher retention and higher revenue per customer.
Many users today want to establish a unified analytics environment across a myriad of expensive, on-premises data marts. Employing a wide range of disconnected tools and stop-gap measures creates data quality and security challenges. Cloud Data Fusion’s vast variety of connectors, visual interfaces, and abstractions centered around business logic helps in lowering TCO, promoting self-service and standardization, and reducing repetitive work.
|Code-free self-service||Remove bottlenecks by enabling nontechnical users through a code-free graphical interface that delivers point-and-click data integration.|
|Collaborative data engineering||Cloud Data Fusion offers the ability to create an internal library of custom connections and transformations that can be validated, shared, and reused across an organization.|
|Google Cloud-native||Fully managed Google Cloud-native architecture unlocks the scalability, reliability, security, and privacy features of Google Cloud.|
|Enterprise-grade security||Integration with Cloud Identity and Access Management (IAM), Private IP, VPC-SC and CMEK provides enterprise security and alleviates risks by ensuring compliance and data protection.|
|Integration metadata and lineage||Search integrated datasets by technical and business metadata. Track lineage for all integrated datasets at the dataset and field level.|
|Seamless operations||REST APIs, time-based schedules, pipeline state-based triggers, logs, metrics, and monitoring dashboards make it easy to operate in mission-critical environments.|
|Comprehensive integration toolkit||Built-in connectors to a variety of modern and legacy systems, code-free transformations, conditionals and pre/post processing, alerting and notifications, and error processing provide a comprehensive data integration experience.|
|Hybrid enablement||Open source provides the flexibility and portability required to build standardized data integration solutions across hybrid and multi-cloud environments.|
|Edition||Price per Cloud Data Fusion instance hour||Number of simultaneous pipelines supported||Number of users supported|