Google Dataplex
Data fabric software
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What is Google Dataplex
Google Dataplex is a Google Cloud data fabric service for organizing, governing, and discovering data across data lakes and data warehouses. It provides a unified logical layer for metadata, data classification, data quality, and access policies across Google Cloud storage and analytics services. Dataplex targets data platform teams and governance stakeholders who need consistent controls and cataloging for analytics and AI workloads. It differentiates through tight integration with Google Cloud services and managed governance capabilities rather than acting as an independent, cross-cloud virtualization layer.
Unified governance and metadata
Dataplex centralizes technical metadata, business metadata, and policy management for datasets registered in the platform. It supports data classification and tagging to help standardize how data is described and controlled. This reduces duplicated governance configuration across multiple analytics services. It also supports discovery workflows through catalog-style search and metadata browsing.
Native Google Cloud integration
Dataplex integrates closely with Google Cloud storage and analytics services, including BigQuery and Cloud Storage, to apply governance and metadata consistently. It works with Google Cloud IAM and policy constructs to manage access controls. This can simplify operations for teams already standardized on Google Cloud. The managed nature reduces the need to deploy and maintain separate governance infrastructure.
Built-in data quality tooling
Dataplex includes capabilities for defining and running data quality rules and monitoring results. These checks help data teams detect schema and content issues earlier in pipelines and analytics workflows. Quality results can be tied back to governed assets to support stewardship processes. This provides a governance-adjacent alternative to assembling separate quality components.
Primarily Google Cloud scoped
Dataplex is designed around Google Cloud services and governance primitives, which can limit fit for organizations needing equal-depth coverage across multiple clouds and on-prem systems. While external data can be referenced via integrations, the strongest capabilities are typically realized when core data platforms run on Google Cloud. This can increase platform dependency for governance and catalog workflows. Buyers seeking a vendor-neutral control plane may require additional tooling.
Not a full virtualization layer
Dataplex focuses on governance, cataloging, and management rather than providing broad data virtualization and query federation as a primary function. Organizations that need a single query layer across heterogeneous sources may still need separate federation/virtualization technology. As a result, Dataplex may complement rather than replace other data fabric components. This can add architectural complexity in mixed-source environments.
Complexity at enterprise scale
Implementing consistent domains, policies, and stewardship processes across many datasets can require significant upfront design and operating discipline. Costs and administrative overhead can grow with the number of assets, quality rules, and governed projects. Teams may need specialized Google Cloud skills to configure and operate the service effectively. Governance outcomes also depend on organizational adoption, not only tooling.
Plan & Pricing
Pricing model: Pay-as-you-go (usage-based)
Free tier/trial: Dataplex Universal Catalog Standard Processing: 100 DCU-hour free per month (Google Cloud Free Tier). Premium Processing not included in free tier. Google Cloud free trial/credits also apply to new customers (general Google Cloud free trial).
Core billed SKUs / Example costs (USD, default rates shown on the official Dataplex pricing page):
- Dataplex Universal Catalog — Standard processing: $0.06 per DCU-hour (default). Discounted consumption models listed: BigQuery CUD - 1 Year: $0.054 per DCU-hour; BigQuery CUD - 3 Year: $0.048 per DCU-hour.
- Dataplex Universal Catalog — Premium processing (covers data lineage, data quality, profiling): $0.089 per DCU-hour (default). Discounted consumption models: BigQuery CUD - 1 Year: $0.0801 per DCU-hour; BigQuery CUD - 3 Year: $0.0712 per DCU-hour.
- Metadata storage (Dataplex Universal Catalog storage): $0.002739726 per 1 gibibyte hour (billed as monthly average storage). (This is the metadata storage SKU; note: automatically ingested Google Cloud technical metadata is offered at no charge.)
Other notes:
- Some Dataplex functionality triggers executions in other Google Cloud services (Dataproc Serverless, BigQuery, Dataflow, Cloud Scheduler) and those usages are billed under those services' pricing.
- Dataplex Universal Catalog API calls for creating/managing resources and catalog search are free of charge.
Discount options: Consumption-model discounts shown (BigQuery CUD 1-yr and 3-yr commitments) reduce per-DCU rates. Contact sales for custom quotes.
Seller details
Google LLC
Mountain View, CA, USA
1998
Subsidiary
https://cloud.google.com/deep-learning-vm
https://x.com/googlecloud
https://www.linkedin.com/company/google/
