Cloud Data Fusion Advanced Features Boosts Data Integration

Cloud Data Fusion

Cloud-native data integration that is fully controlled at any size. Through the integration of data from fragmented on-premises platforms, Cloud Data Fusion assists users in creating distributed, scalable data lakes on Google Cloud.

Advantages

Prevent technical snags and increase output

Time to insight is accelerated by Google Cloud Data Fusion‘s self-service paradigm of code-free data integration, pre-built connectors, and intuitive drag-and-drop interface, which eliminates bottlenecks based on technical skills.

Cloud Data Fusion Architecture

Reduced ownership costs overall for pipelines

Data Fusion provides the greatest data integration capabilities at a reduced total cost of ownership its to a server less architecture that takes advantage of the scalability and dependability of Google services like Dataproc.

Construct with a foundation of data governance

Data Fusion helps teams with root cause or impact analysis and compliance with built-in capabilities including end-to-end data lineage, integration metadata, and cloud-native security and data protection services.

Important characteristics

Open core providing multi-cloud and hybrid integration

The open core of the CDAP project, which is used in the construction of Data Fusion, guarantees user mobility of the data pipeline. Through extensive interaction with public and on-premises cloud platforms, CDAP enables Cloud Data Fusion users to dismantle silos and provide insights that were previously unavailable.

Combined with Google’s premier big data tools for the industry

Data security is made easier and data is always ready for analysis thanks to Data Fusion’s interface with Google Cloud. The integration of Cloud Data Fusion makes development and iteration quick and simple, whether you’re assembling a data lake with Cloud Storage and Dataproc, transferring data into BigQuery for data warehousing, or converting data to end up in a relational store like Spanner.

Cooperation and standards to enable data integration

Pre-built transformations are available for batch and real-time processing in Cloud Data Fusion. It offers the capacity to build an internal library of unique transformations and connections that can be shared, validated, and utilized by other teams. Productivity is increased and the groundwork for collaborative data engineering is laid. This translates to less waiting for data engineers and ETL developers as well as, crucially, less anxiety over the quality of the code.

Use cases

Google Cloud data lakes are more contemporary and safe

Through the integration of data from fragmented on-premises platforms, Cloud Data Fusion assists users in creating distributed, scalable data lakes on Google Cloud. Clients can centralize data and extract more value from it by taking use of the cloud’s size. The self-service features of Cloud Data Fusion reduce the total cost of operational support while improving process visibility.

BigQuery data warehouses that are agile

By destroying data silos and facilitating the creation of flexible, cloud-based data warehousing solutions in BigQuery, Cloud Data Fusion can assist businesses in having a deeper understanding of their clientele. The capacity to provide a superior customer experience, which raises retention and revenue per customer, is unlocked by a reliable, unified view of customer engagement and behavior.

Unified environment for analytics

These days, a lot of users wish to create a single analytics environment that spans several pricey on-premises data marts. There are issues with data security and quality when disparate tools and temporary fixes are used. With its wide range of connectors, visual interfaces, and abstractions based on business logic, Cloud Data Fusion lowers total cost of ownership (TCO), encourages self-service and standardization, and eliminates repetitious work.

Cloud Data Fusion Pricing

The pricing of Cloud Data Fusion is divided into:

1. Design cost: determined by how many hours each instance runs, not by how many pipes are created and used. With the Basic edition, the first 120 hours per month per account are free.
2. Processing cost: The price of the pipelines’ Dataproc clusters.