Secure Data Exchange Platforms

🚀 Delta Sharing: The Open Protocol for Secure Data Exchange Traditionally, data sharing involved providing static CSV/Parquet file dumps based on ad-hoc requests, requiring data engineers to create extracts or build complex ETL pipelines. By the time data reached recipients, it was often outdated. Additionally, moving data across organizational boundaries increased security risks and required manual auditing as well. Delta Sharing, an open protocol, solves these challenges by enabling direct, real-time data exchange while ensuring security and governance. 🔍 What is Delta Sharing? Delta Sharing is an open-source protocol that allows data providers to securely share live data from their data lake or lakehouse with any recipient, regardless of the computing platform they use. It is designed to work with Delta Lake, but it also supports other formats like Apache Parquet. 🔧 What Problems Does Delta Sharing Solve? ✅ Eliminates Data Copies – Consumers can query shared data without duplicating or exporting it into another system. ✅ Interoperability – Enables cross-platform sharing across different cloud and analytics services, including Databricks, Apache Spark, Pandas, and others. ✅ Real-time & Secure Access – Uses fine-grained access control to ensure only authorized users can access the latest version of shared data. ✅ Simplified Data Collaboration – Reduces the need for custom APIs, FTP transfers, or complex ETL workflows when sharing data with external partners. 🛠 Key Components in a Delta Sharing Scenario - Provider (Data Owner) – The entity sharing the data. - Delta Sharing Server – Handles authentication and access control. - Recipient (Data Consumer) – The entity accessing the shared data, which can be a data warehouse, a machine learning model, or a BI tool. - Storage Backend – Typically an object store (AWS S3, Azure Blob, Google Cloud Storage, MinIO) where the data resides. 📌 Common Use Cases for Delta Sharing 💡 Inter-company Data Exchange – Share supply chain, financial, or operational data with partners securely. 📊 Federated Analytics – Analysts can query live shared datasets without moving them into their own data warehouse. 🤖 Machine Learning & AI – Data scientists can directly access fresh, live data for model training without worrying about outdated extracts. ⚡ Data Monetization – Organizations can offer secure access to valuable datasets as a service without needing data pipelines. Delta Sharing + Unity Catalog Delta Sharing and Unity Catalog work together to enable secure, scalable, and governed data sharing across organizations. While Delta Sharing provides the protocol for sharing live data with external consumers, Unity Catalog acts as the central governance layer, ensuring fine-grained access control, auditing, and security compliance. I will write about this integration in the future. #deltasharing #datagovernance #datasharing

Zalando just dropped a fantastic blog on how they’re using Delta Sharing to power secure, real-time data exchange — and it’s a must-read for anyone still stuck in the swamp of FTP, CSV exports, or vendor lock-in. Why does this matter? Because Delta Sharing is changing the game in Retail: 🔓 Zero data copying – Share live data without duplicating or moving it ☁️ Cross-cloud and cross-platform – AWS, Azure, GCP, Snowflake, Power BI? No problem ⚡ Real-time and secure – Deliver governed access without delays 💸 Major cost savings – No need for expensive, proprietary license models Zalando’s implementation is elegant, open, and future-proof — exactly what modern data collaboration should look like. It's also why we're seeing a surge of interest from Retailers looking to both reduce their costs while drive stronger business results through real-time collaboration. Read the full post: https://lnkd.in/edTJiRgT And if your data team is still “sharing” via email attachments… it might be time to catch up. #DataSharing #OpenStandards #DeltaSharing #Databricks #ModernDataStack #DataEngineering #Zalando

Databricks is becoming a wrecking ball for data silos. About time I upgraded this diagram, because last month Databricks Clean Rooms became Generally Available. This introduces yet another way to share data and to collaborate on data projects with other data practitioners. The best part: you can bring in data from non Databricks (and even other public cloud) sources (like Synapse, Snowflake, Redshift or BigQuery) as long as these data sources are governed by a Unity Catalog. The whole idea is that enterprises can now share and work together with other enterprises in a secure and controlled environment. This makes it possible to collaborate on sensitive data projects without any risk of exposing sensitive information. Clean Rooms runs on Delta Sharing, which is become quite the standard I must say. Note: There are loads of loose ends in the diagram, but the idea is that we now have a way of collaborating on data cross-organization, rather than just sharing or integrating data via Spark Connect, Unity Catalog or Delta Sharing.