How to Supercharge Google Dataplex to Ensure Data Reliability in Google Cloud Lakehouses 

Enterprise data teams are increasingly adopting cloud-native architectures as they re-architect their ecosystems to enable advanced analytics and AI workloads. On Google Cloud, this transformation has accelerated with the introduction of BigLake, which unifies BigQuery and Cloud Storage to provide the scalability and flexibility to store, process, and analyze data under a single architecture.

However, with the flexibility of such environments comes operational complexity that introduces new challenges. Distributed pipelines may be ingesting data from multiple sources, each with its own refresh cadence, schema evolution, and data quality characteristics. Without proactive controls, a single unnoticed anomaly can slip through downstream applications, impacting business-critical processes. This is where Google Dataplex steps in.

In this article, we explore how Google Dataplex delivers unified governance and rule-based data quality for BigQuery and Cloud Storage, and how combining it with AI-powered data observability solutions like Telmai creates a comprehensive reliability framework for Google Cloud lakehouses.

Google Cloud Dataplex — Unified Governance for the Lakehouse

Google Cloud Dataplex is Google Cloud’s unified data management and governance layer, designed to bring BigQuery, Cloud Storage, and other Google Cloud assets under a single control plane. It enables enterprises to organize, catalog, and govern data at scale without sacrificing flexibility or performance.

It goes beyond storage and query orchestration, offering a holistic suite of capabilities:

• Metadata & Cataloging: A centralized inventory for datasets, tables, and files, enriched with both business and technical metadata. Dataplex is full integrated with Google Data Catalog, allowing users to search, tag, and classify assets with ease.
  
• Fine-Grained Policy Enforcement: With Dataplex, users can apply access controls across data domains. It supports granular security and role-based permissions, ensuring that sensitive information is only accessible to authorized users, which is essential for regulatory compliance and internal governance.
  
• Lineage and Data Classification: Dataplex automatically traces data movement, mapping how data flows through pipelines and transformations. Built-in tools help identify personally identifiable information (PII) and apply custom tags, making it easier to audit data use and understand the impact of changes.
  
• Built-in Data Quality Monitoring: Dataplex empowers teams to define, schedule, and execute data quality rules directly on their lakehouse assets.Native rule-based profiling and validation for data in BigQuery and Cloud Storage, with flexible rule types such as null checks, value ranges, regex matching, referential integrity, and custom SQL-based rules.
  
• Integrated Alerting & Monitoring: Seamless connections to Cloud Monitoring and Pub/Sub for proactive alerting, enabling issues to be addressed before they impact downstream processes.

In the next section, we’ll further explore how Dataplex serves as the governance backbone of the Google Cloud lakehouse, excelling in consistent, rule-driven validation and embedding data quality checks close to where the data lives.

How Does Dataplex Work and Where Does It Shine

Dataplex’s Data Quality (DQ) scans are purpose-built to help organizations operationalize governance-driven validation directly inside their Google Cloud lakehouse. By running close to the data and tightly integrating with native GCP services, they deliver scalable, low-friction quality monitoring that aligns with enterprise governance policies.

Automated Profiling and Intelligent Rule Recommendations

Dataplex can automatically scan datasets in BigQuery and Cloud Storage, profiling them to surface structural and content patterns such as column data types, distribution statistics, and basic completeness measures. This reduces manual setup and accelerates the onboarding of new datasets into governance workflows.

Comprehensive Rule-Based Checks

Dataplex offers a rich library of predefined data quality checks that operate both at the row and aggregate level:

• Row-Level: Null checks, range expectations, regex validations, set checks.
• Aggregate: Uniqueness, statistical range checks, and more.

For flexibility, custom SQL-based rules are supported, allowing advanced users to tailor validations to complex business requirements. These rules execute natively in BigQuery or as serverless jobs, minimizing infrastructure overhead.

See below for a snapshot of the types of custom SQL rules you can define.

Rule Management & Governance

Rule management is accessible for everyone. Business users can leverage an intuitive UI for implementing data quality rules. Scans could be automated via YAML/JSON (enabling CI/CD workflows) or programmatic APIs for broader integration. Integration with Data Catalog means every rule and result is tracked alongside business and technical metadata for discoverability and auditability.

Alerting integrates natively with Cloud Monitoring and Pub/Sub, enabling proactive notifications that plug seamlessly into incident management routines. DQ scans can be scheduled, triggered by events, or executed on demand, keeping validations tightly aligned with data refresh cycles and SLAs.

For operational flexibility, data teams can run scans across whole tables, only incremental segments, or even sampled partitions, striking the right balance of coverage, cost, and speed. All executions generate logs and quality metrics in Cloud Logging, facilitating historic analysis and compliance reporting.

Because DQ scans are fully embedded in the GCP ecosystem, they automatically inherit centralized metadata, lineage tracking, and fine-grained IAM controls, enabling consistent policy enforcement across organizational domains.

But DQ rules alone have limits

While Dataplex excels at making rule-based data quality scalable and manageable across Google Cloud lakehouses, its approach is inherently defined by what you know to check:

• Unknown Unknowns: Dataplex can only catch issues you define in advance. Silent anomalies, data drift, or schema shifts often go unseen until they have already impacted downstream processes.
  
• Scaling Across Velocity and Variety: As data volumes grow, ingestion speeds accelerate, and formats multiply, maintaining up-to-date rules for every asset becomes a manual, error-prone endeavor. In large environments, blind spots and gaps are almost inevitable.
  
• Reactive vs. Proactive: Traditional DQ rule checks operate after the data lands, enabling detection and resolution only post-ingestion. In critical pipelines powering dashboards, ML models, or regulatory reports, organizations increasingly require proactive quality assurance—catching issues before they cause costly disruptions.

This is why extending Dataplex with AI-driven observability, pairing its strong governance backbone with adaptive anomaly detection to surface, diagnose, and resolve issues that static rules cannot anticipate.

Telmai + Dataplex: Scalable, AI-Driven Data Observability for the Google Lakehouse

Google Cloud Dataplex delivers the essential governance and rule-based data quality foundation for lakehouse environments. Yet, static data quality rules alone often fall short in addressing the complexity of modern data landscapes that are prone to data drift, schema changes, and silent anomalies.  As organizations demand more adaptive, proactive data quality frameworks, combining Dataplex with an advanced AI-driven observability platform like Telmai becomes the new standard for reliable data workloads.

Telmai is natively architected on GCP, leveraging services like Dataproc (Spark), Pub/Sub, GKE (Kubernetes), and BigQuery to deliver scalable, low-latency monitoring without overloading operational systems. Deployments are flexible with options available as SaaS or within your own GCP VPC  and fully align with GCP IAM and security best practices.

Layering AI Observability over Dataplex’s Rule Engine

Rule-based + ML-driven detection: While Dataplex enforces known business rules, Telmai continuously profiles data across BigQuery, Cloud Storage, and downstream pipelines. Telmai automatically surfaces issues the moment patterns deviate from baseline, detecting the “unknown unknowns” Dataplex rules might miss and flagging:

• Unusual spikes or drops in row counts.
• Schema changes or type mismatches.
• Outliers, rare categories, and sudden shifts in distribution.
• Data drift, unexpected null patterns, or category explosions.

Real-time & batch coverage: Telmai supports both streaming and batch data sources, monitoring BigQuery, Cloud Storage, and open table formats like Apache Iceberg, Delta Lake, and Hudi, enabling visibility across heterogeneous lakehouse architectures.

Root Cause Analysis and Rapid Remediation: Upon detection, Telmai correlates anomalies with recent data ingests, schema evolution, or upstream operational events, empowering data teams with actionable insights and reducing “mean time to resolution” from days to minutes.

Telmai and Dataplex can also share metadata and integrate with Data Catalog, Cloud Monitoring, and operational workflows, providing a single source of truth for both technical and business users and closing the loop from detection to resolution.

Conclusion

Dataplex provides the unified control plane, metadata intelligence, and rule-based quality checks needed to enforce consistency and trust at scale.

Telmai complements this foundation with AI-driven observability, continuously monitoring for the anomalies, drifts, and changes that rules alone can’t anticipate. Together, they offer organizations a comprehensive, future-proof data reliability framework, one that is built on Google Cloud, scales with enterprise demands, and adapts to the dynamic nature of modern data. 

Telmai is also available on the Google Cloud Marketplace, enabling consolidated billing and quick deployment with your Google Cloud credits.

Ensure every insight from your Google Cloud lakehouse is built on reliable data. Click here to talk to our team and learn how Telmai can scale data quality across your Google Cloud ecosystem.

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