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Seamless Data Integration with Fivetran Google Cloud Storage Connector
Effortlessly manage raw and normalized data pipelines for your ML teams.
Supports hybrid deployment models for enhanced integration flexibility.Sync multiple tables and files with advanced pattern handling.Automatically convert data to open formats like Apache Iceberg and Delta Lake.
Tags
IntegrationsStorageGoogle Cloud Storage
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overview
Overview of Fivetran Google Cloud Storage Connector
The Fivetran Google Cloud Storage Connector simplifies the process of ingesting and organizing data for analytics and machine learning teams. With managed pipelines dedicated to raw and normalized data, your teams can focus on insights rather than data management.
- Streamline data ingestion from 700+ sources.
- Support for structured and unstructured data.
- Centralize your data storage on Google Cloud.
features
Key Features
Our connector is designed to enhance your data workflows with features that improve performance and governance. From built-in data catalog integrations to auto format conversions, experience a new standard in data management.
- Hybrid deployment models for on-premises and SaaS.
- Multi-table and file syncing for comprehensive ingestion.
- Native integrations with Google BigQuery Metastore.
use_cases
Use Cases
Ideal for data engineers and analytics teams, the Fivetran Google Cloud Storage Connector is perfect for enterprises pursuing large-scale data lake modernization. Maximize your data capabilities for analytics and AI initiatives while ensuring compliance and governance.
- Efficiently manage data lakes for AI and machine learning.
- Ensure compliance with GDPR and other standards.
- Enable data-driven insights with modern tools and technologies.
Frequently Asked Questions
Who is the Fivetran Google Cloud Storage Connector for?
It is designed for data engineers, analytics teams, and enterprises that need to centralize large-scale data from diverse sources for advanced analytics and AI initiatives.
What are the benefits of hybrid deployment models?
Hybrid deployment models offer flexibility to adapt to various security and infrastructure requirements, making it easy to integrate both on-premises and cloud environments.
How does auto conversion to open table formats work?
The connector automatically transforms ingested data into open formats like Apache Iceberg and Delta Lake, ensuring data is ready for querying and AI applications, while minimizing vendor lock-in risks.