Key Features of Delta Sharing

Oct 13, 2025 by pinkandtrash.com 108 views

What is Delta Sharing?

Delta Sharing is an open-source protocol developed by Databricks that enables secure and real-time data sharing across different organizations, platforms, and cloud environments without replication. This innovative technology allows you to share live data from Delta Lake tables stored in Databricks, AWS, Azure, or any cloud storage with external clients without moving or copying data. Clients can access shared data using Python, SQL, Apache Spark, or BI tools like Power BI and Tableau.

Key Features of Delta Sharing

Cross-Platform Data Sharing

Delta Sharing enables you to share data between different cloud providers, such as AWS, GCP, Azure, and even on-prem systems. This feature allows for seamless data sharing across various platforms, making it an ideal solution for organizations with multi-cloud or hybrid environments.

No Data Duplication

Unlike traditional file-based sharing (CSV, Parquet), data is not copied. Consumers query live data directly, reducing the risk of data duplication and ensuring that only the most up-to-date information is shared.

Secure and Controlled Access

Delta Sharing supports access control with tokens and policies, ensuring that only authorized users can see the data. This feature provides an additional layer of security, making it an ideal solution for organizations with sensitive data.

Supports Multiple Clients

Delta Sharing works with BigQuery, Spark, Python, Pandas, SQL, Power BI, and Tableau, making it a versatile solution for various data analysis and visualization tools.

Efficient Data Delivery

Delta Sharing uses Parquet and Delta Lake formats, enabling fast and efficient data access. This feature ensures that data is delivered quickly and efficiently, reducing the risk of data latency and improving overall data sharing performance.

How Delta Sharing Works

Data Provider (Owner)

The data provider defines tables in Delta Lake and shares data via the Delta Sharing Server. The data provider grants access using tokens, ensuring that only authorized users can access the shared data.

Data Consumer

The data consumer uses the Delta Sharing client (Python, SQL, BI tool) to retrieve data via a secure REST API. The data consumer can read real-time data without duplication, ensuring that they have access to the most up-to-date information.

Comparison: Delta Sharing vs Traditional Data Sharing

Feature	Delta Sharing	CSV/Parquet Files	API-based Sharing
Data Movement	No	Yes	Yes
Live Updates	Yes	No	Limited
Cross-Cloud Sharing	Yes	No	Limited
Access Control	Yes	No	Yes
Performance	High (Parquet/Delta format)	Medium	High

As the comparison table shows, Delta Sharing offers several advantages over traditional data sharing methods, including no data duplication, live updates, and cross-cloud sharing.

Use Cases

Delta Sharing is ideal for various use cases, including:

Sharing financial transaction data with external partners
Providing real-time analytics data to vendors or business units
Cross-company collaboration without complex ETL pipelines
Regulated data sharing where access must be controlled but not duplicated

Conclusion

Delta Sharing is a powerful way to share live data securely and efficiently without duplication. It allows BigQuery, Spark, and other clients to directly access shared data using the Delta Sharing protocol. By leveraging Delta Sharing, organizations can unlock efficient and secure data sharing, improving collaboration and reducing the risk of data duplication.

Would you like me to explain how it fits into your Google Cloud and BigQuery setup in more detail?

Setting up Delta Sharing with GCP

To set up Delta Sharing with GCP, you will need to:

Create a Delta Sharing Server instance in your GCP project
Configure the Delta Sharing Server to use your GCP storage bucket
Share your Delta Lake tables with external clients using the Delta Sharing protocol

I can provide a more detailed guide on setting up Delta Sharing with GCP, including step-by-step instructions and code examples.

Integrating Delta Sharing with BigQuery

To integrate Delta Sharing with BigQuery, you will need to:

Create a BigQuery dataset and table to store your shared data
Configure the Delta Sharing Server to use your BigQuery dataset
Share your Delta Lake tables with BigQuery using the Delta Sharing protocol

I can provide a more detailed guide on integrating Delta Sharing with BigQuery, including step-by-step instructions and code examples.

Conclusion

Frequently Asked Questions

Q: What is Delta Sharing?

A: Delta Sharing is an open-source protocol developed by Databricks that enables secure and real-time data sharing across different organizations, platforms, and cloud environments without replication.

Q: What are the key features of Delta Sharing?

A: The key features of Delta Sharing include:

Cross-platform data sharing
No data duplication
Secure and controlled access
Supports multiple clients
Efficient data delivery

Q: How does Delta Sharing work?

A: Delta Sharing works by allowing data providers to share live data from Delta Lake tables stored in Databricks, AWS, Azure, or any cloud storage with external clients without moving or copying data. Clients can access shared data using Python, SQL, Apache Spark, or BI tools like Power BI and Tableau.

Q: What are the benefits of using Delta Sharing?

A: The benefits of using Delta Sharing include:

Efficient and secure data sharing
Reduced risk of data duplication
Improved collaboration
Faster data access

Q: Can I use Delta Sharing with BigQuery?

A: Yes, you can use Delta Sharing with BigQuery. Delta Sharing works with BigQuery, Spark, Python, Pandas, SQL, Power BI, and Tableau, making it a versatile solution for various data analysis and visualization tools.

Q: How do I set up Delta Sharing with GCP?

A: To set up Delta Sharing with GCP, you will need to:

Create a Delta Sharing Server instance in your GCP project
Configure the Delta Sharing Server to use your GCP storage bucket
Share your Delta Lake tables with external clients using the Delta Sharing protocol

Q: Can I integrate Delta Sharing with other data platforms?

A: Yes, you can integrate Delta Sharing with other data platforms, including AWS, Azure, and on-prem systems.

Q: What are the use cases for Delta Sharing?

A: The use cases for Delta Sharing include:

Sharing financial transaction data with external partners
Providing real-time analytics data to vendors or business units
Cross-company collaboration without complex ETL pipelines
Regulated data sharing where access must be controlled but not duplicated

Q: Is Delta Sharing secure?

A: Yes, Delta Sharing is secure. It supports access control with tokens and policies, ensuring that only authorized users can see the data.

Q: Can I use Delta Sharing with my existing data infrastructure?

A: Yes, you can use Delta Sharing with your existing data infrastructure. Delta Sharing is designed to work with various data platforms and tools, making it easy to integrate with your existing infrastructure.

Q: What are the system requirements for Delta Sharing?

A: The system requirements for Delta Sharing include:

A Delta Sharing Server instance
A GCP project with a storage bucket
A Delta Lake table with shared data
A client application (e.g., Python, SQL, BI tool) to access the shared data

Q: How do I get started with Delta Sharing?

A: To get started with Delta Sharing, you will need to:

Create a Delta Sharing Server instance in your GCP project
Configure the Delta Sharing Server to use your GCP storage bucket
Share your Delta Lake tables with external clients using the Delta Sharing protocol

I hope this Q&A article has provided you with a comprehensive understanding of Delta Sharing and its benefits. If you have any further questions or would like to learn more about setting up Delta Sharing with GCP or integrating it with BigQuery, please don't hesitate to ask.