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1.2 Databricks Marketplace & Data Sharing Basics

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Databricks Certified Data Analyst Associate Exam

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1 Platform Overview & Unity Catalog Foundations

1.1 Databricks Architecture & Compute Concepts 1.2 Databricks Marketplace & Data Sharing Basics 1.3 Unity Catalog Namespace & Object Hierarchy 1.4 Governed Asset Discovery & System Tables

2 Managing & Cleaning Governed Data

2.1 Dataset Discovery & Asset Tagging 2.2 Lineage Inspection & Data Traceability 2.3 SQL Data Cleaning & Transformation Techniques

3 Data Ingestion & Import Patterns

3.1 Workspace Uploads & Cloud File Ingestion 3.2 Incremental File Ingestion with Auto Loader 3.3 Ingesting Data via Delta Sharing & External Connections

4 Databricks SQL Warehouses & Core Querying

4.1 Databricks SQL Interface & SQL Assistant 4.2 SQL Warehouses Sizing & Management 4.3 Table Creation & Data Modification in Databricks SQL

5 Advanced SQL Authoring & Time Travel

5.1 Advanced Aggregations, Window Functions & Analytics 5.2 Complex Joins, CTEs & Set Operations 5.3 Views, Materialized Views & Delta Time Travel

6 Query Performance Analysis & Photon Engine

6.1 Photon Engine Architecture & Acceleration 6.2 Query History & Execution Profile Inspection 6.3 Delta Lake Audit History & Commit Logs

7 Liquid Clustering, Caching & Performance Troubleshooting

7.1 Caching Hierarchy in Databricks SQL 7.2 Data Layout Optimization: Liquid Clustering vs Partitioning & Z-Ordering 7.3 Troubleshooting Slow & Failing Queries

8 AI/BI Dashboards & Visualization Design

8.1 AI/BI Dashboards Overview & Canvas Layout 8.2 Chart Types & Visualization Selection 8.3 Notebook & SQL Editor Visualizations

9 Dashboard Interactivity, Publishing & Alerts

9.1 Dashboard Parameters & Interactive Filtering 9.2 Publishing, Sharing & Access Control 9.3 Scheduled Refresh, Email Subscriptions & Alerts

10 Developing & Maintaining AI/BI Genie Spaces

10.1 AI/BI Genie Architecture & Space Setup 10.2 Curation via Instructions, Descriptions & Sample Questions 10.3 Trusted Assets & Parameterized Functions 10.4 Monitoring, Benchmarks, Feedback & Maintenance

11 Data Modeling & Medallion Architecture

11.1 Dimensional Modeling Fundamentals 11.2 Medallion Architecture for Analytics 11.3 Constraint Enforcement & Relationships in Unity Catalog

12 Securing Data & Unity Catalog Governance

12.1 Unity Catalog Privilege Model & Grants 12.2 Ownership, Inheritance & Access Control Patterns 12.3 Fine-Grained Security: Row Filters & Column Masks

1.2 Databricks Marketplace & Data Sharing Basics

Key Takeaways Explain This Topic AI

Delta Sharing is an open, REST-based protocol introduced by Databricks in May 2021 that enables secure cross-cloud and cross-platform data sharing without data replication.

Databricks Marketplace provides an open forum for data providers to distribute datasets, ML models, notebooks, and AI/BI Genie spaces to over 10,000 global consumer organizations without requiring ETL pipelines.

Share objects within Delta Sharing encapsulate tables, views, volumes, and notebooks, granting read-only access to external recipients using secure token-based authentication or native Unity Catalog bindings.

Databricks Clean Rooms allow multiple organizations to conduct joint privacy-safe analytics on sensitive datasets using differential privacy without revealing raw underlying data.

Last updated: July 2026

Databricks Marketplace & Data Sharing Basics

In modern enterprise data analytics, data isolation creates severe business friction. Traditionally, sharing data between organizations, business units, or different cloud platforms required complex Extract, Transform, Load (ETL) pipelines, File Transfer Protocol (FTP) servers, or custom API endpoints. These legacy methods introduce data duplication, stale datasets, security vulnerabilities, and significant engineering maintenance costs. The Databricks Data Intelligence Platform addresses these challenges through Delta Sharing and the Databricks Marketplace .

The Evolution of Open Data Sharing

Modern data architectures require zero-copy data sharing. Instead of copying gigabytes or terabytes of data across cloud storage accounts, modern architectures share direct access to underlying data files governed by strict security policies. +-----------------------------------------------------------------------+

  • | DELTA SHARING ARCHITECTURE |
  • | |
  • | +-------------------+ REST Protocol +---------------------+ |
  • | | Data Provider | =================> | Data Recipient | |
  • | | (Unity Catalog) | Short-Lived SAS | (Databricks, Spark, | |
  • | | | Presigned URLs | Pandas, Power BI) | |
  • | +-------------------+ +---------------------+ |
  • | | | |
  • | v v |
  • | +-------------------+ +---------------------+ |
  • | | Cloud Storage S3 | <------------------ | Read Direct Parquet/ | |
  • | | (Provider Account)| Direct Data Fetch | Delta Data Files | |
  • | +-------------------+ +---------------------+ |
  • +-----------------------------------------------------------------------+
  • Open vs. Proprietary Sharing Protocols

Proprietary Sharing: Many traditional cloud data warehouses allow data sharing only if both the data provider and data recipient use the exact same vendor product in the exact same cloud region.

Open Sharing (Delta Sharing): Databricks created Delta Sharing as an open-source REST protocol. Recipients do not need to use Databricks, run on the same cloud platform, or subscribe to any vendor lock-in. Recipients can query shared data natively using open tools like Apache Spark, Python Pandas, Microsoft Power BI, Tableau, Excel, or Rust.

Delta Sharing Protocol & Core Architecture

Delta Sharing operates natively within Unity Catalog. It establishes a secure channel between a Data Provider and a Data Recipient using short-lived pre-signed URLs.

Key Architectural Entities

Share: A logical container created by a Data Provider inside Unity Catalog. A Share groups together data assets intended for distribution. A single Share can contain:

  • Delta Lake Tables (read-only)
  • Table Partitions & Dynamic Views
  • Volumes (non-tabular files like PDFs or CSVs)
  • AI/ML Models and Notebooks

Recipient: A object in Unity Catalog representing an external user, team, or organization that receives data. Unity Catalog tracks recipient authentication credentials.

Provider: An object representing the entity sharing the data.

How Delta Sharing Executes Under the Hood

When a Data Recipient queries a shared Delta table:

The Recipient client sends an authentication request to the Provider's Delta Sharing server (managed by Unity Catalog).

Unity Catalog verifies privileges, parses the Delta table commit log, and generates short-lived, pre-signed cloud storage URLs (e.g., AWS S3 pre-signed URLs valid for 1 hour).

The Recipient client reads the underlying Parquet/Delta data files directly from the Provider's cloud object storage using those pre-signed URLs.

No data is copied , and compute costs for executing the query are borne entirely by the Recipient.

Databricks Marketplace: Discovery & Monetization

The Databricks Marketplace is an open commercial and public data exchange built directly on top of Delta Sharing. It allows data providers to showcase, distribute, and monetize data products, while providing data analysts with immediate access to third-party datasets.

Types of Marketplace Assets

Unlike traditional data exchanges that only offer static tabular CSV files, Databricks Marketplace supports a rich variety of intelligence assets:

Tabular Datasets: Financial market feeds, geographic demography, point-of-sale retail data, and weather telemetry.

AI / ML Models: Pre-trained machine learning models and large language model (LLM) weights.

Databricks Notebooks & Solution Accelerators: Ready-to-run analytical code, visualization templates, and industry workflow reference guides.

AI/BI Genie Spaces: Pre-curated conversational natural language analytics environments ready for instant querying.

Marketplace Exchange Listings

Listings on Databricks Marketplace fall into two categories:

Standard (Instant) Listings: Free or public datasets that consumers can instantly request and mount into their Unity Catalog metastore with a single click.

Personalized Listings: Commercial datasets requiring provider approval, contract negotiation, or customization before access is granted.

Provider vs. Recipient Experience

Sharing data in Databricks requires zero ETL setup. Administrators use standard Data Control Language (DCL) SQL commands to govern shares.

Databricks-to-Databricks Sharing vs. Databricks-to-Open Sharing

Databricks supports two recipient authentication models:

Databricks-to-Databricks (D2D): If the recipient also uses Databricks with Unity Catalog, sharing is seamless. The provider shares data using the recipient's unique Unity Catalog Metastore Identifier. The shared catalog appears natively in the recipient's Catalog Explorer without token management.

Databricks-to-Open (D2O): If the recipient uses an external non-Databricks platform (such as Python scripts or Power BI), Unity Catalog generates a secure one-time activation link. The recipient downloads a secure credential file ( .share credential file containing a bearer token) used to authenticate REST API calls.

Managing Shares with SQL

Data Analysts and Data Governance Leads inspect and manage shares using standard Databricks SQL commands:

sql\n-- Create a share object in Unity Catalog\nCREATE SHARE regional_sales_share\nCOMMENT 'Contains quarterly sales metrics for external distributors';\n\n-- Add Delta tables to the share\nALTER SHARE regional_sales_share ADD TABLE main.sales_schema.quarterly_summary;\n\n-- Create a recipient entity for an external distributor\nCREATE RECIPIENT distributor_acme\nUSING ID 'aws:us-west-2:a1b2c3d4-e5f6-7890-1234-56789abcdef0';\n\n-- Grant read access on the share to the recipient\nGRANT SELECT ON SHARE regional_sales_share TO RECIPIENT distributor_acme;\n\n-- Inspect active shares and detailed metadata\nSHOW SHARES;\nDESCRIBE SHARE regional_sales_share;\n

Data Clean Rooms: Secure Multi-Party Collaboration

For privacy-sensitive industries (such as healthcare, banking, and retail), sharing raw datasets is legally prohibited due to regulations like GDPR and HIPAA. Databricks Clean Rooms provide a secure, privacy-preserving environment where multiple parties collaborate without sharing raw data.

Clean Room Key Principles

Zero Raw Data Exposure: Participants can run joint analytical queries (e.g., matching retail purchase records against advertising exposure logs), but neither party can see the other party's raw records.

Privacy Overlap Queries: Clean rooms enforce differential privacy algorithms and minimum aggregate threshold restrictions to prevent reverse-engineering of individual identity records.

No Cloud Co-location Required: Built on Delta Sharing, Clean Room participants can reside in different cloud providers (e.g., AWS vs. Azure) and different metastores seamlessly.

Delta Sharing & Marketplace Comparison Table

Feature / Attribute Delta Sharing (Open) Databricks Marketplace Databricks Clean Rooms

Primary Purpose Direct point-to-point data sharing Open discovery & commercial distribution Multi-party privacy-safe joint analytics

Supported Recipient Databricks & Non-Databricks clients Databricks consumers globally Databricks workspace participants

Data Copy Required No (Zero-copy pre-signed URLs) No (Zero-copy data mounting) No (Privacy-sandboxed queries)

Asset Types Tables, Views, Volumes, Models Tables, Models, Notebooks, Genie Spaces Joined SQL Tables & Aggregations

Monetization Support Custom infrastructure setups Native commercial marketplace listings Multi-party commercial agreements