Sharing a dataset with another team or company without copying it
Analytics Hub is a BigQuery data sharing platform that lets organizations publish and subscribe to datasets as secure, managed exchanges without copying or moving data. It is the recommended approach when you need to share large-scale analytics data with external partners, other business units, or the public while retaining control over access and usage. Use it instead of ad hoc BigQuery dataset sharing whenever governance, scalability, or monetization of data sharing is a requirement.
Must-know
- Analytics Hub uses a publisher/subscriber model where publishers create 'listings' inside a 'data exchange' and subscribers link to them, gaining query access without the data being copied or duplicated.
- Because subscribers query a linked dataset directly against the publisher's underlying BigQuery storage, data stays fresh in real time and publishers only pay for storage while subscribers pay for their own query compute.
- Choose Analytics Hub over manually granting IAM roles on BigQuery datasets when sharing with many external or cross-organization consumers, since it centralizes discovery, access management, and revocation at scale.
- Data exchanges and listings support fine-grained access control, letting publishers control who can discover and subscribe to specific listings, including private exchanges for internal-only sharing.
- Analytics Hub is appropriate for sharing BigQuery-native and BigQuery-compatible data (including some public/commercial datasets) but is not a general-purpose file or object sharing mechanism like Cloud Storage.
- Subscribers cannot modify the source data through the linked dataset, since it remains read-only and governed by the publisher, making it well-suited for controlled, one-to-many analytics distribution.
A data practitioner is deciding whether to use Analytics Hub or grant BigQuery IAM roles directly on a dataset. In which scenario is Analytics Hub the more appropriate choice?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
Coverage checked against the published exam guide on Aug 12, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.