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DataOps Suite
The complete platform
Platform Overview
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DataOps Suite

Data Quality and Agentic AI platform that validates, reconciles, scores, monitors and remediates data from ingestion to BI and AI.

Why one platform

Agentic AI-powered test and rule creation
Agents generate data quality rules, validation tests and SQL from prompts, with your approval.
Data quality score per dataset
Shows the current score, what changed since the last run and the failing records behind it.
Validation and observability inside the pipeline
Every load is compared with its source, transformations are tested against business logic, and freshness, volume and schema changes are monitored.
Reports that match the source
Dashboard figures are compared with the source data behind them before users see them.
Test data without real records
Synthetic data that mirrors production replaces sensitive records in test environments.
Quality gates for releases
Pipeline and report changes are tested before they go to production.

Data Quality Monitor

Continuously profiles, validates and scores enterprise data, with Agentic AI that recommends rules and flags anomalies, so analytics and AI run on trusted data.

Capabilities

AI-recommended DQ rules and scoring
Agents suggest completeness, validity and uniqueness rules, with scores down to the column.
Data profiling
Analyzes distributions, patterns and frequencies to surface outliers.
AI-based observability
Agents monitor pipelines end to end, detect anomalies and suggest fixes.
Data catalog
Organizes models and tables in one catalog, with AI-drafted descriptions.
Data lineage
Traces data from origin to destination through every transformation.
Data quality issue tracking
Assigns every failing record to an owner and tracks it to resolution.

ETL Validator

Automates ETL/ELT testing, with Agentic AI that builds tests from mapping docs, confirming every record arrives complete, accurate and correctly transformed.

Capabilities

AI-generated ETL tests
Turns mapping docs into checks for joins, transforms and edge cases.
Source-to-target validation
Matches loaded data to the source, down to record counts and required fields.
Transformation validation
Verifies business logic with SQL, Python or Scala.
Migration validation
Checks schema, row counts and integrity for cloud migrations.
Metadata change audit
Catches schema and metadata discrepancies before they break pipelines.
Root cause and adaptive tests
Pinpoints where data broke, and agents update tests when mappings change.

BI Validator

Automates testing and governance for Power BI, Tableau and Oracle Analytics, with Agentic AI that builds tests from plain English and keeps reports accurate and reliable.

Capabilities

AI-created BI tests
Describe a test scenario in plain English, and agents build it.
Visual-to-source comparison
Compares the data behind every visual to the source using auto-generated SQL.
Regression and upgrade checks
Pixel-level comparisons across environments, with AI summaries of what changed.
Measure logic checks
Converts BI measures and calculations to SQL to confirm KPI logic.
Performance and load testing
Simulates concurrent users and measures load and refresh times.
BI governance
Finds unused measures, duplicate reports and orphaned tables.

Test Data Manager

Uses Agentic AI to detect and mask sensitive data and generate synthetic data that mirrors production, so teams can test without exposing real records.

Capabilities

AI synthetic data generation
Learns patterns from a small sample to create production-like data.
PII discovery and masking
Detects PII and PHI and replaces it with realistic fake values.
Relationship-aware data
Keeps linked tables connected with referential integrity.
Millions of records in minutes
Scales for load, stress and performance testing.
Multi-format output
CSV, Parquet or JSON, or loaded straight into test databases.
Compliant by design
Cloud or on-prem, supporting GDPR and HIPAA requirements.
Data Quality Use Case
The problem to solve
ROI calculator
Estimate the time and cost youโ€™ll save.
Financial services
SOX, BCBS 239 and NAIC MAR

Banking, Financial Services, and Insurance (BFSI)

Stop reporting errors before examiners find them, with every number traced from core system to board dashboard.

How Datagaps helps

Core platform migrations
Cut over banking and insurance platforms, including mainframe to cloud, without losing a record.
Regulatory reporting
Hold submissions until the data meets quality thresholds.
Financial reconciliation
Match GL to subledger automatically, with every break flagged.
Risk and credit data
Catch drift before risk, lending and pricing models degrade.
Dashboard attestation
Prove capital and board dashboards match the warehouse.

Healthcare

Keep claims and clinical data accurate through every migration, without exposing patient records in testing.

How Datagaps helps

EHR migrations
Confirm every record survives the move to Epic, Cerner or Oracle Health.
Claims data quality
Catch errors in claims and remittance feeds as they arrive.
APCD submissions
Check every file against state templates before you submit.
Clinical dashboards
Catch KPI and report changes after every refresh.
Provider directories
Keep provider listings accurate, current and audit-ready.

Life Sciences

Catch issues in clinical and real-world data before submission, and keep trial subject data out of test environments.

How Datagaps helps

Clinical data migrations
Move EDC, CTMS and LIMS data to the cloud without losses.
CRM migrations
Keep rebate calculations right through Veeva and Salesforce moves.
Pharmacovigilance data
Catch adverse event data issues before regulatory submission.
Commercial and clinical dashboards
Trace every KPI back to source clinical and lab records.
De-identified trial data
Keep subject data out of test, analytics and AI environments.

Manufacturing

Avoid broken ERP cutovers and supplier overpayments, and catch sensor drift before it hits production.

How Datagaps helps

ERP migrations
Move BOM, inventory and financial data across Oracle, SAP and cloud without breaks.
Supplier reconciliation
Catch overpayments and duplicate invoices with three-way PO, ASN and invoice matching.
Sensor data quality
Catch drift before it skews predictive maintenance and yield models.
Operations dashboards
Catch report breaks after every release or upgrade.
Safe ERP test data
Keep supplier pricing and production volumes out of lower environments.

Retail and Utilities

Catch settlement, inventory and pricing errors before your customers or regulators do.

How Datagaps helps

Settlement reconciliation
Match marketplace payout files to the general ledger, record by record.
Inventory accuracy
Keep POS, ERP and warehouse numbers in sync.
Pricing and promotions
Catch price and promotion mismatches across channels.
Reliability dashboards
Keep SAIDI and SAIFI reliability reporting accurate.
BI under load
Make sure reports hold up for thousands of concurrent users.

Higher Education & Research

Get enrollment, financial-aid and transcript data right before SIS cutover, before students feel it.

How Datagaps helps

SIS migrations
Validate moves from Banner or PeopleSoft to Workday Student or Ellucian.
Financial-aid reconciliation
Match aid and Bursar data to the general ledger.
Enrollment reporting
Keep census and admissions dashboards consistent from cycle to cycle.
Federal reporting
Monitor data quality ahead of IPEDS submissions.
Safe student data
Test and train AI without exposing real student records.

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BI and analytics

OBIEE 11g upgrade : Fixing consistency check errors during RPD Upgrade

OBIEE-11g-upgrade-Fixing-consistency-check-errors-during-RPD-Upgrade-22

This troubleshooting guide addresses 9 common consistency check warnings encountered when upgrading OBIEE RPDs from 10.1.3.4.1 to 11.1.1.7 using the Upgrade Assistant. It covers specific warning codes โ€” including 39003 (missing functional dependency), 38028 (invalid data type), 39057 (unused physical table mappings), 39009 (aggregate source join errors), and more โ€” explaining their root causes, typically tied to disabled Logical Table Sources or misaligned aggregation levels, and provides step-by-step solutions for each.

Key Takeaways

  • Disabled Logical Table Sources cause cascading warnings โ€” unused LTS from the 10g RPD get disabled during upgrade, directly triggering warnings 39003 and 38028; cleaning these up beforehand can prevent both.
  • Aggregation level mismatches drive multiple errors โ€” warnings 39009 and 39074 stem from fact and dimension tables having misaligned aggregation content levels (e.g., โ€œDetailโ€ vs. a higher level), requiring content tab updates to align grains.
  • Missing physical joins are a recurring root cause โ€” warnings 39008 and 39009 both point to missing or incorrect joins between Logical Table Sources and fact tables in the physical layer.
  • Some warnings need manual reconfiguration, not just cleanup โ€” issues like 39062 (wrong connection pool in init blocks) and 39076 (disabled โ€œUse for Displayโ€ property) require explicit manual fixes rather than pre-upgrade prevention.

Common OBIEE RPD Upgrade Consistency Check Warnings

This article walks you through the consistency check warnings found in the BI Apps RPD after running the 11.1.1.7 upgrade assistant on an 10.1.3.4.1 RPD.

  1. Warning [39003]โ€“ Missing functional dependency association of a column.
    Any Logical Table Source that has been unused in 10g is disabled during the upgrade. Cleaning up such tables before the upgrade might help avoid this warning.
    Solution: Enable all the Logical Table Sources that are disabled during the upgrade.
  1. Warning [38028]โ€“ column does not have valid data type. I encountered this error because of the disabled Logical Table Sources (from warning 39003). The datatype for all the columns belonging to the disabled LTS were set to null.
    Solution: Fixing the warning 39003 should fix this error, otherwise check to see if the column has a valid datatype.
  1. Warning [39057]โ€“There are physical tables mapped in Logical Table Sources that are not used in any column mappings or expressions.
    Solution: The recommendations to workaround this issue:
  1. Delete the unused logical tables. Delete the aggregations set for that dimension in the content tab of the fact.
  2. Create dummy mapping columns from that table.

Metalink note: Warning 39057 [ID 1386095.1]
Cleaning up any unused logical tables pre upgrade, might help from running into this warnings.

  1. Warning [39009]โ€“ Logical fact table FACTS have an aggregate source that does not join to a Dimension: source at the proper level of detail.
    Solution: Make sure there is a physical join between the fact Logical Table Source and dimensionโ€™s Logical Table Source. Make sure the aggregation levels are set at the same grain for both dimension and fact on the content tab.
  1. Warning [39076]โ€“ Logical level does not have any key with Use for Display property on.
    โ€˜Use for Displayโ€™ option is turned off during the upgrade.
    Solution: Turn on the โ€˜Use for Displayโ€™ option for all the keys specified.
  1. Warning [39008]โ€“ Logical dimension table has a source that does not join to any fact source.
    The Logical levels on the content tab for LTS and Fact table are not set properly.
    The join between the LTS and Fact table is missing in the physical layer.

    Solution: Fix the Logical levels on the content tab for Logical Table Sources and Fact table. Both should be set to the same Aggregation level. Check for any missing foreign key joins between LTS and Fact table in physical layer.
  1. Warning [39062]โ€“ Initialization block uses Connection Pool which is used for report queries. This may impact query performance.
    Solution: Update the initialization blocks to use the right connection pool. If a connection pool for the init block does not exist, create a new connection pool so that it does not use the connection pool specified for the queries.
  1. Warning [39028]โ€“ The features in database does not match the defaults. This may cause query problems.
    Solution: Make sure all the required database features are turned on.
  1. Warning [39074]โ€“ Logical dimension table has a source with aggregation content set to level โ€˜Detailโ€™. The same dimension has aggregation content set in fact source to a higher level.
    Solution: The aggregation levels are set at different grain for fact dimension tables. Update the content tab for dimension and fact to be at the same level.

Conclusion

Most of the consistency check warnings that surface during an OBIEE 10g-to-11g RPD upgrade trace back to just two root causes: disabled Logical Table Sources left over from unused 10g objects, and mismatched aggregation content levels between fact and dimension tables. Cleaning up unused LTS objects and aligning aggregation grains before running the Upgrade Assistant can prevent several of these warnings โ€” 39003, 38028, and 39057 โ€” from appearing in the first place. The remaining warnings, like 39062โ€™s connection pool misconfiguration or 39076โ€™s disabled โ€œUse for Displayโ€ property, require manual reconfiguration after the upgrade rather than pre-upgrade prevention. Working through these systematically, rather than fixing them one at a time as they surface in production, is what keeps an RPD upgrade from turning into a longer troubleshooting cycle than it needs to be.

FAQs: OBIEE RPD Upgrade Warnings

1) What causes warning 39003 (โ€œcould not compute a functional dependencyโ€) during OBIEE RPD upgrade?

This warning typically occurs when a Logical Table Source (LTS) is disabled during the upgrade because it was unused in the original 10g RPD. Removing or cleaning up these disabled LTS objects before upgrading usually resolves the issue.

2) How do you fix warning 38028 (โ€œinvalid data typeโ€) in the Business Model layer?

Warning 38028 is commonly caused by the same disabled Logical Table Source (LTS) issue that triggers warning 39003. Reviewing and removing unused or disabled LTS mappings typically resolves both warnings together.

3) Why does warning 39009 (โ€œno fact source contains all mapped dimensionsโ€) appear?

This warning usually indicates a mismatch in aggregation content levels between fact and dimension tables or a missing physical join between the Logical Table Source (LTS) and the fact table. Correcting the content levels and physical joins resolves the warning.

4) What causes warning 39062 during an RPD upgrade?

Warning 39062 occurs when an initialization block references an incorrect connection pool. Unlike some upgrade warnings, it requires a manual update to the initialization blockโ€™s connection pool configuration after the upgrade.

5) How do you resolve warning 39076 related to the โ€œUse for Displayโ€ property?

This warning appears when the Use for Display property is disabled for a column that requires it. Re-enabling this property for the affected column resolves the warning.

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Rajesh Kumar A
Rajesh Kumar A

Digital Marketing Manager, Datagaps

Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.

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Subrahmanya Narayana Chirravuri

Senior Director, Technology, Datagaps

Senior Director of Technology at Datagaps. Leads engineering for the ETL, BI, and data-quality validation platforms.

Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL Validator, DataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  Datagaps

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