Advanced Cloud Data Fusion Features Improve Integration

 

Cloud Data Fusion

Fully regulated, cloud-native data integration for any size. Cloud Data Fusion helps users create distributed, scalable data lakes on Google Cloud by integrating data from disparate on-premises platforms.

Benefits

Avoid technological hiccups and boost productivity

Google Cloud Data Fusion's self-service model of code-free data integration, pre-built connectors, and an easy-to-use drag-and-drop interface which removes barriers based on technical skills accelerates time to insight.

Architecture of Cloud Data Fusion

Overall lower pipeline ownership costs

With a server-less architecture that leverages the scalability and dependability of Google services like Dataproc, Data Fusion offers the best data integration capabilities at the lowest total cost of ownership.

Build utilizing a data governance basis

With built-in capabilities including 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.

Crucial attributes

Open core offering hybrid and multi-cloud integration

User mobility of the data pipeline is ensured by the open core of the CDAP project, which is utilized in the building of Data Fusion. By means of comprehensive engagement with both public and on-premises cloud systems, CDAP facilitates the dismantling of silos and offers insights that were not before accessible to Cloud Data Fusion customers.

When coupled with Google's leading big data tools for the sector

The integration between Data Fusion and Google Cloud facilitates data protection and ensures that data is always available for analysis. Whether you're putting together a data lake with Cloud Storage and Dataproc, moving data into BigQuery for data warehousing, or transforming data to end up in a relational store like Spanner, the integration of Cloud Data Fusion makes creation and iteration rapid and easy.

Standards and collaboration to make data integration possible

In Cloud Data Fusion, pre-built transformations are accessible for both batch and real-time processing. It provides the ability to create an internal library of distinct connections and transformations that other teams can use, verify, and exchange. Both productivity and the foundation for collaborative data engineering are raised. For data engineers and ETL developers, this means reduced waiting times and, more importantly, less stress about the caliber of the code.

Use cases

Google Cloud data lakes are safer and more modern

Cloud Data Fusion helps users create distributed, scalable data lakes on Google Cloud by integrating data from disparate on-premises platforms. Customers can leverage the capacity of the cloud to centralize data and get more value out of it. The self-service capabilities of Cloud Data Fusion provide process visibility while lowering the overall cost of operational assistance.

Aggressive BigQuery data warehouses

Through the dismantling of data silos and the facilitation of the development of adaptable, cloud-based data warehousing solutions in BigQuery, Cloud Data Fusion can help companies gain a more thorough consumer knowledge. A dependable, cohesive picture of client involvement and behavior unlocks the potential to deliver an exceptional customer experience, increasing retention and revenue per customer.

unified analytics environment

Many users these days want to combine multiple costly on-premises data marts into a single analytics environment. When inconsistent tools and band-aid solutions are employed, problems arise with data security and quality. With its extensive set of connectors, graphical user interfaces, and business logic-based abstractions, Cloud Data Fusion reduces total cost of ownership (TCO), promotes standardization and self-service, and gets rid of tedious work.

Pricing for Cloud Data Fusion

There are two categories for Cloud Data Fusion pricing:

The number of hours that each instance operates determines the design cost, not the number of pipes that are made and used. The first 120 hours per month per account are free with the Basic edition.

Processing cost: The cost of the Dataproc clusters used in the pipelines.

Edition	Price per Cloud Data Fusion instance hour	Number of simultaneous pipelines supported	Number of users supported
Developer	US$0.35	2 (Recommended)	2 (Recommended)
Basic	US$1.80	Unlimited	Unlimited
Enterprise	US$4.20	Unlimited	Unlimited