The Multi-Cloud Data Challenge: Validating Across AWS, Azure, and GCP

Multi-Cloud Data Validation Across AWS, Azure & GCP | Best Practices + Tools

Why Multi-Cloud (And Why It’s Hard)

Why teams go multi-cloud: Avoid lock-in, optimize cost/performance per workload, meet regional/compliance needs, leverage best-of-breed services (e.g., BigQuery analytics + Snowflake sharing + Databricks ML).

What makes validation tough:

• Different SQL dialects & functions: SAFE_CAST vs TRY_CAST, date math, array struct handling.

• Type/precision differences: NUMERIC vs DECIMAL, TIMESTAMP vs DATETIME, timezone handling.

• Storage and file formats: Parquet/ORC/CSV compression, column order, metadata differences.

• Identity/security: IAM roles, service principals, private endpoints, network rules.

• Latency & eventual consistency: Cross-region replication delays; partial loads.

• BI assumptions: Calculated fields built on platform-specific semantics.

Validation Targets Across Clouds

• Ingestion: Object stores (S3/ADLS/GCS), streams (Kinesis/Event Hubs/PubSub), APIs, flat files.

• Transform: Snowflake, BigQuery, Redshift, Synapse, Databricks SQL/Delta.

• Serve: Gold tables, marts, curated views, materialized views.

• BI: Power BI, Tableau, Looker—metrics, filters, drill paths, row-level security.

Common Multi-Cloud Failure Modes

• Precision loss: DECIMAL(38,10) in Snowflake ≠ BigQuery NUMERIC precision, rounding impacts aggregates.

• Timezone drift: TIMESTAMP WITH TIME ZONE vs DATETIME (no TZ) causing day boundary errors.

• Null semantics: NVL/COALESCE differences; blank vs null treated inconsistently.

• Partitioning/clustering: Different pruning rules change performance and even result sets when predicates differ.

• Security filters: Row-level security not mirrored across platforms; BI reveals unintended records.

A Practical Validation Framework

1. Establish golden data contracts

   
   

   • Schemas, types, constraints, and business rules defined centrally.

   • Version control for contracts; CI checks on PRs.

   
   

2. Automate cross-cloud schema checks

   
   

   • Compare object-to-object (table/view) across platforms.

   • Alert on rename/type/nullable changes before downstream runs.

   
   

3. Reconcile row & aggregate parity

   
   

   • Counts, sums, min/max, distinct counts.

   • Stratified by partitions (date, region, product), not just whole-table.

   
   

4. Distribution & outlier monitoring

   
   

   • Check top-N categories, quantiles, and variability.

   • Catch anomalies introduced by platform default changes or upstream drift.

   
   

5. Business rule enforcement

   
   

   • Referential integrity in analytics layer, allowed ranges, code lists, KPI formulas.

   • Example: “Gross = Net + Tax + Fees” within 0.1% tolerance cross-cloud.

   
   

6. BI validation

   
   

   • Validate metric parity across Power BI/Tableau/Looker.

   • Confirm filters, row-level security, and drill paths behave identically.

   
   

Example: Snowflake ↔ BigQuery Parity

• Schema: Ensure DECIMAL scales map to BigQuery BIGNUMERIC when needed.

• Time: Convert all timestamps to UTC at the edge; store TZ offsets explicitly.

• Aggregation parity: Validate SUM(amount) per order_date and country within tolerance.

• Distribution: Track avg_order_value, refund_rate, top_10_skus weekly.

• BI parity: Compare KPI tiles and totals across mirrored dashboards.

Observability Essentials in Multi-Cloud

• End-to-end lineage: Know which upstream change caused a broken KPI downstream.

• SLA/SLO dashboards: Visibility into latency, freshness, and data quality.

• Ticketing & on-call: Create incidents automatically with failing samples attached.

• Governance & audit: Keep evidence for regulators: what changed, when, and how you mitigated.

How Vexdata Helps (In One Control Plane)

• Cloud-agnostic connectors: S3, ADLS, GCS; Snowflake, BigQuery, Redshift, Synapse, Databricks; plus relational, NoSQL, and flat files (CSV/JSON/XML/Parquet).

• Automated validation at each hop: Schema, rules, profiling, reconciliation, and BI checks.

• AI-powered anomaly detection: Learns normal patterns; flags deviations early.

• Playbooks & remediation: Open tickets in Jira/ServiceNow, attach diffs/samples, track SLAs.

• Compliance support: Detailed audit trails of tests, changes, and approvals.

Implementation Blueprint (90 Days)

• Weeks 1–2: Identify critical assets and KPIs. Define contracts & rules.

• Weeks 3–6: Automate ingestion + transform checks across clouds; enable alerts.

• Weeks 7–10: Add BI parity testing; integrate ticketing and on-call.

• Weeks 11–13: Tune thresholds, add drift dashboards; hand off to ops with playbooks.

Conclusion

Multi-cloud is powerful—but only if you can trust the data moving across it. With automated, cross-cloud validation, you can prevent silent drift, maintain KPI parity, satisfy auditors, and keep teams confident in the numbers. Vexdata gives you one platform to validate ingestion, transformation, and BI—across AWS, Azure, and GCP.

Running Snowflake, BigQuery, or Redshift side-by-side? See how Vexdata validates across clouds—book a free demo.