Data Sharing Rules and Dataset Publishing – GCP Professional Data Engineer Guide
Introduction
Data sharing and dataset publishing are critical capabilities in any modern cloud data platform. In the context of Google Cloud Platform (GCP) and the Professional Data Engineer certification, understanding how to securely share data, enforce access controls, and publish datasets for consumption by internal and external stakeholders is essential. This guide covers the importance, mechanisms, and exam strategies related to Data Sharing Rules and Dataset Publishing.
Why Is This Important?
Organizations rarely operate in data silos. Teams, departments, partners, and even external customers need controlled access to datasets. Improper data sharing can lead to:
• Data breaches – Sensitive information exposed to unauthorized users.
• Compliance violations – Breaking regulations like GDPR, HIPAA, or CCPA.
• Data quality issues – Consumers using stale or incorrect versions of data.
• Cost overruns – Uncontrolled access leading to excessive query costs.
Proper data sharing rules and publishing practices ensure that the right people have the right access to the right data at the right time — a core responsibility of any data engineer.
What Are Data Sharing Rules?
Data sharing rules define who can access what data, how they can access it, and under what conditions. In GCP, these rules are implemented through a combination of:
• IAM (Identity and Access Management) – Controls who can access BigQuery datasets, Cloud Storage buckets, Pub/Sub topics, and other resources at the project, dataset, or resource level.
• Dataset-level permissions – In BigQuery, you can grant roles such as BigQuery Data Viewer, BigQuery Data Editor, or BigQuery Data Owner at the dataset level to specific users, groups, or service accounts.
• Authorized Views – Allow you to share query results from a dataset without giving users direct access to the underlying tables. This is a powerful mechanism for enforcing row-level or column-level security.
• Authorized Datasets – Allow an entire dataset to be authorized to query another dataset, simplifying management when many views need cross-dataset access.
• Column-level security – Using policy tags in BigQuery with Data Catalog, you can restrict access to specific columns containing sensitive data.
• Row-level security – BigQuery supports row-level access policies that filter rows based on the identity of the user querying the data.
• VPC Service Controls – Define security perimeters around GCP resources to prevent data exfiltration across project boundaries.
• Data Loss Prevention (DLP) API – Automatically scan and redact or mask sensitive data before sharing.
What Is Dataset Publishing?
Dataset publishing is the act of making a curated dataset available for consumption. This can be internal (within an organization) or external (to partners or the public). Key GCP mechanisms include:
• BigQuery Analytics Hub – A data exchange platform that allows organizations to publish and subscribe to shared datasets. Publishers list datasets (called listings) in an exchange, and subscribers can access them without data duplication. This is the primary GCP service for governed dataset publishing.
• BigQuery public datasets – Google hosts a variety of public datasets that demonstrate the concept of dataset publishing at scale.
• Authorized Views and Shared Datasets – Traditional approach where you grant cross-project access to specific BigQuery datasets or views.
• Cloud Storage signed URLs or public buckets – For file-based dataset sharing (CSV, Parquet, Avro), though this approach requires more manual governance.
• Data Catalog – Provides metadata management and discovery, making published datasets searchable and well-documented with tags, descriptions, and schema information.
• Dataplex – Enables data mesh architectures where domains can publish and govern their own datasets while maintaining organizational-level policies.
How Data Sharing Works in GCP – Step by Step
Scenario: Sharing a BigQuery dataset with another team in a different project
1. Identify the dataset – Determine which tables or views need to be shared.
2. Define access requirements – Decide whether the consumer needs full table access or a filtered/restricted view.
3. Create authorized views (if needed) – If you need to restrict rows or columns, create a view that applies the necessary filters, then authorize it in the source dataset.
4. Grant IAM permissions – Assign the BigQuery Data Viewer role on the dataset (or specific tables) to the consumer's Google group or service account.
5. Apply column-level security (if needed) – Use policy tags from Data Catalog to restrict sensitive columns.
6. Apply row-level security (if needed) – Create row access policies using CREATE ROW ACCESS POLICY DDL statements.
7. Document in Data Catalog – Add descriptions, tags, and ownership information so consumers can discover and understand the data.
8. Monitor access – Use Cloud Audit Logs and BigQuery INFORMATION_SCHEMA views to track who is accessing the data.
Scenario: Publishing a dataset via Analytics Hub
1. Create a data exchange – Set up an exchange in Analytics Hub (private or public).
2. Create a listing – Add the BigQuery dataset as a listing with metadata, description, and access controls.
3. Configure subscriber access – Define who can subscribe (specific organizations, anyone, etc.).
4. Subscriber subscribes – The consumer subscribes to the listing, which creates a linked dataset in their project. No data is copied — it remains in the publisher's project.
5. Query the linked dataset – Subscribers query the linked dataset as if it were their own, but the publisher retains control over the source data.
Key Concepts to Remember
• Authorized Views vs. Materialized Views – Authorized views are for security (controlling access); materialized views are for performance (pre-computed results). Don't confuse them.
• Analytics Hub eliminates data duplication – Subscribers don't get a copy; they get a reference. This reduces storage costs and ensures data freshness.
• Principle of Least Privilege – Always grant the minimum permissions required. Use BigQuery Data Viewer instead of BigQuery Data Editor when consumers only need read access.
• Policy tags are enforced at query time – Even if a user has access to the dataset, policy tags on columns will prevent them from reading restricted columns unless they have the appropriate taxonomy role.
• Row-level security is transparent to users – Users query the table normally; the row access policy automatically filters results based on their identity.
• Cross-region sharing – BigQuery datasets are regional. Analytics Hub supports cross-region data sharing through cross-region linked datasets.
• Billing – In BigQuery, the project running the query pays for the compute (unless using reservations). The publisher pays for storage. This is an important cost consideration.
Common GCP Services Involved
• BigQuery – Primary data warehouse for sharing structured datasets.
• BigQuery Analytics Hub – Managed data exchange for publishing and subscribing.
• Data Catalog – Metadata management, discovery, and policy tag enforcement.
• Dataplex – Data governance and data mesh architecture support.
• Cloud IAM – Access control at every layer.
• Cloud DLP – Sensitive data detection and de-identification.
• VPC Service Controls – Network-level data exfiltration prevention.
• Cloud Audit Logs – Tracking data access for compliance.
Exam Tips: Answering Questions on Data Sharing Rules and Dataset Publishing
1. Look for the keyword "without copying data" – If a question mentions sharing data across organizations or projects without data duplication, the answer is almost always Analytics Hub.
2. Authorized views are the go-to for row/column restriction in sharing scenarios – If a question describes sharing a subset of a table (specific rows or columns) with another team, think authorized views first. For column-level restrictions on the table itself, think policy tags.
3. Know the difference between dataset-level, table-level, column-level, and row-level access – The exam tests your ability to choose the right granularity of access control. Dataset-level IAM is coarse; column-level policy tags and row-level access policies are fine-grained.
4. Billing model matters – Questions may ask who pays for queries on shared datasets. Remember: the project executing the query pays for compute. The publisher pays for storage.
5. VPC Service Controls appear in data exfiltration questions – If a question mentions preventing data from leaving an organization's boundary, even if a user has IAM access, think VPC Service Controls.
6. Cloud DLP for sensitive data before sharing – If the question involves sharing data that may contain PII or sensitive information and asks how to protect it, Cloud DLP for scanning/masking/de-identification is the answer.
7. Data Catalog for discoverability – If a question asks how to make datasets discoverable or how to manage metadata across the organization, Data Catalog is the answer.
8. Dataplex for data mesh and domain-level governance – If a question describes a decentralized data architecture where individual teams own and publish their data, Dataplex is likely the answer.
9. Eliminate answers that involve manual data copying – GCP favors managed, automated solutions. If an answer involves manually exporting data to Cloud Storage and sharing the bucket, it's likely not the best answer when Analytics Hub or authorized views are options.
10. Watch for "external" sharing vs. "internal" sharing – For external (cross-organization) sharing, Analytics Hub is preferred. For internal (same organization, different projects), dataset-level IAM with authorized views is often sufficient.
11. Remember that linked datasets in Analytics Hub are read-only – Subscribers cannot modify the publisher's data. If a question asks about collaborative editing, a different approach (like shared datasets with Editor roles) would be needed.
12. Practice elimination – Many exam questions have two plausible answers. Use the principle of least privilege, managed services preference, and no-data-duplication preference to eliminate distractors.
Summary
Data Sharing Rules and Dataset Publishing on GCP revolve around a layered security model (IAM, authorized views, policy tags, row-level security, VPC Service Controls) combined with managed publishing platforms (Analytics Hub, Data Catalog, Dataplex). For the exam, focus on matching the level of access control granularity to the scenario, prefer managed and no-copy solutions, and always apply the principle of least privilege. Understanding the billing implications and compliance considerations will further strengthen your ability to select the correct answer.
