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DataOps Suite
The complete platform
Data Quality Monitor
Continuous quality monitoring
ETL Validator
ETL, ELT and migration testing
BI Validator
Dashboard and report testing
Test Data Manager
Compliant synthetic test data
Platform Overview
See how it works

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
Cloud platforms
Warehouses, lakes and CRM
BI and analytics
Dashboards and reports
ROI calculator
Estimate the time and cost youโ€™ll save.
Financial services
SOX, BCBS 239 and NAIC MAR
Healthcare
HIPAA, EHR migration and APCD
Life sciences
GxP and 21 CFR Part 11
Manufacturing
ERP migration and supplier data
Retail and utilities
Settlement, pricing and NERC CIP
Higher education
FERPA, IPEDS and research data

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.

Trusted Data Summit

A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.

Save Your Spot
Nov 19th, 2026
Technology Partners
Cloud, data and BI platforms
SI Partners
Consulting and implementation

Become a partner

Resell, implement or co-deliver

Technology Partners

Datagaps works natively with the cloud, data and BI platforms your teams already run.

Our technology partners

SI Partners

Consulting and integration firms that deliver Datagaps validation for enterprise clients worldwide.

Our SI partners

Solutions

Use cases

Cloud platforms

BI and analytics

Oracle EBS R12 Upgrade Testing: Database Validation for Reimplementation and In-Place Upgrades

Database-Backend-Testing-for-Oracle-R12-Upgrade-10

Oracle E-Business Suite (EBS) R12 upgrades require comprehensive database testing to ensure data migration, referential integrity, and business-critical data remain accurate after reimplementation or in-place upgrades. This guide explains the differences between testing 11i-to-R12 migrations and R12.1-to-R12.2 upgrades, outlines essential validation techniques such as data comparison, integrity checks, and regression testing, and shows how automated database testing accelerates upgrade validation while minimizing data quality risks and production issues.

Key Takeaways

  • Database testing is critical during Oracle EBS R12 upgrades to verify data completeness, accuracy, and consistency after migration or upgrade activities.
  • Reimplementation projects (11i to R12) require end-to-end validation of migrated records, referential integrity, and business data rules to ensure successful migration.
  • In-place upgrades (R12.1 to R12.2) should include regression testing by comparing pre-upgrade and post-upgrade database snapshots to detect unexpected data changes.
  • Automated database testing reduces manual effort by validating data migration, comparing large datasets, enforcing business rules, and identifying data discrepancies more efficiently.

Oracle E-Business Suite (EBS) R12 is a significant new version with valuable new features and capabilities. Although there is an upgrade path from EBS 11i to R12, most companies reimplement R12 and migrate the data from their 11i instance. Reimplementation can be a complex project but it also gives them the option to improve their implementation.

When transitioning from EBS R12.1 to R12.2 companies generally perform an inplace upgrade. One of our customer was upgrading from EBS R12.1 to R12.2 and wanted to verify that the upgrade did not cause any issues to the data in their data warehouse. While testing the data warehouse and the dashboards can help identify data issues during the upgrade, it is important to test the data in the EBS R12 instance from the backend. This type of testing is called database testing.

Database Testing for Reimplementation (eg. 11i to R12 transition)

Database (or Backend) testing from the reimplementation project is similar to the testing of Data Migration projects where data gets migrated from a legacy application to a new application.

The main goals of the Reimplementation testing are :

  • Verify that the data has been fully migrated from 11i to the R12 instance. Most customers want to perform 100% data validation which may be required in regulated industries such as Pharma and Financial.
  • Verify the referential data integrity after the migration from 11i to R12 instance. For example, some of the records in the child table may get mapped to a different parent record or become orphan records during the migration.
  • In case of any data cleanup during the migration verify that the R12 data is following the data accuracy and consistency rules laid out for the cleanup effort.

Our ETL and Data testing tool, ETL Validator comes with several different types of test cases and test plans for simplifying and automating the Database testing for reimplementation project (or data migration testing)

ETL Validator Test TypeWhat It Validates
Query Compare Test CaseCompares large volumes of data between 11i and R12 databases (backend).
Data Profile Test CaseCompares aggregates (or checksums) between 11i and R12 databases.
Foreign Key Test PlanValidates the referential integrity of the migrated data in the R12 database.
Data Rules Test PlanValidates data accuracy and consistency rules for the migrated data in the R12 database.
Oracle-R12-Data-Migration-ETL-Validator
Database Testing for Inplace Upgrades (eg. R12.1 to R12.2 upgrade)

when performing an inplace R12 upgrade (for example, R12.1 to R12.2), teams should understand the upgradeโ€™s impact on the data and run regression tests against it, including verifying the impact on the ETL processes and the Data Warehouse. The recommended way to test data for inplace upgrades is to take a snapshot of the data prior to the upgrade and compare that snapshot with the data post upgrade. The snapshot can cover the entire contents of the tables or the results of SQL queries on the R12 database, depending on data volume. Any differences found between the snapshot data and the post-upgrade data need to be analyzed and validated.

Whether the project is a full 11i-to-R12 reimplementation or an R12.1-to-R12.2 inplace upgrade, the underlying testing goal is the same โ€” proving that every migrated or upgraded record is complete, referentially intact, and rule-compliant before the new EBS environment goes live. ETL Validatorโ€™s Query Compare, Data Profile, Foreign Key, Data Rules, and Baseline & Compare capabilities automate that proof across both upgrade paths, replacing manual, table-by-table spot checks with systematic, repeatable validation.

EBS-R12-upgrade-testing

Conclusion:

Oracle EBS R12 upgrades put backend data integrity at real risk, whether youโ€™re reimplementing from 11i or performing an in-place R12.1 to R12.2 upgrade. Reimplementation projects demand full data migration validation, referential integrity checks, and business rule verification, while in-place upgrades rely on comparing pre- and post-upgrade snapshots to catch unintended changes. Manually running these comparisons across large EBS datasets is slow and error-prone, especially in regulated industries needing 100% validation. Automated tools like ETL Validator, with Query Compare, Data Profile, Foreign Key, and Baseline & Compare test plans, make this validation repeatable and fast. The right testing approach depends on which upgrade path youโ€™re taking, not a one-size-fits-all checklist. Getting this validation right before go-live is what prevents costly data issues from surfacing in production.

Frequently Asked Questions

1) What is database testing for an Oracle EBS R12 upgrade?

Database testing validates that data remains complete, accurate, and consistent after an Oracle EBS R12 upgrade. It verifies migrated records, referential integrity, and business rules to ensure the upgrade does not introduce data issues.

2) What is the difference between reimplementation and in-place upgrades in Oracle EBS?

A reimplementation (such as Oracle 11i to R12) migrates data into a newly implemented R12 environment, while an in-place upgrade (such as R12.1 to R12.2) upgrades the existing environment without migrating to a new instance. Each requires a different testing approach.

3) Why is regression testing important during Oracle R12 upgrades?

Regression testing compares database states before and after the upgrade to identify unexpected changes, missing records, or data inconsistencies that could impact downstream ETL processes, reports, and business operations.

4) What should be validated after an Oracle EBS database upgrade?

Post-upgrade validation should include data completeness, referential integrity, data quality rules, business rule compliance, ETL functionality, and consistency between pre-upgrade and post-upgrade datasets to ensure the upgraded environment functions correctly.

Get Started Today

Talk to a datagaps expert

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.

SPS Murthy
S P S Murthy Akella

Director, Technology Strategy, Datagaps

Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.

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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Data Quality Monitor

Continuously assess, score, and improve your enterprise data quality using rule-based and AI-powered validation
Automated Data Quality Checks at Scale

Validate uniqueness, completeness, domain accuracy, and detect orphan records.

AI-Driven Anomaly Detection and Alerts

Identify data drift and outliers using ML-based statistical methods and IQR-based profiling.

Low-Code Rule Configuration with Data Rule Wizard

Create and deploy validation rules quickly without coding, even across large datasets.

Graphical Scoring and Monitoring Dashboard

Visualize data quality trends across models, tables, and records with actionable insights.

CI/CD and Cloud Integration Ready

Enable continuous validation across pipelines using integrated APIs and DevOps compatibility.

Test Data Manager

Generate high-quality synthetic test data securely while maintaining regulatory compliance with HIPAA, GDPR, and CCPA
AI-Powered Synthetic Test Data Generation

Automatically create realistic data based on patterns in production while masking PII/PHI.

Reduced Cost and Time for Test Data Preparation

Eliminate manual rule-writing and speed up test readiness for complex use cases.

Support for Diverse Data Formats and Models

Generate millions of records in JSON, XML, CSV, relational, or hierarchical formats.

Secure, Policy-Driven Data Masking

Ensure sensitive fields are protected using deterministic, reversible, or random masking.

Flexible Deployment Across Cloud or On-Prem

Deploy within your secure environment and integrate into automated pipelines seamlessly.

ETL Testing

Maximize the efficiency, quality, and reliability of your data pipelines through intelligent automation, validation, and scalability.
100% Data Validation Across Pipelines

Validate billions of records using Spark-powered parallel execution across on-prem and cloud sources.

Accelerated Migration and QA Cycles

Reduce migration testing time by up to 60% and QA costs by 30% with automated workflows.

Automated Metadata and Transformation Testing

Detect schema mismatches and ensure business rules are correctly applied via AI-assisted validation.

Seamless Collaboration and Governance

Enable role-based access, ALM integration, and shareable web reports to unify cross-team efforts.

Low-Code/No-Code Test Creation with AI

Empower both technical and business users to build, schedule, and execute validations using prompt-based automation.

BI Validator

Ensure accuracy, performance, and security of your Business Intelligence dashboards and reports across platforms like Tableau, Power BI, and Oracle Analytics
Automated Regression Testing Across BI Reports

Detect broken visuals or logic changes post-upgrade and data refreshes.

Cross-Platform Validation of Reports and Dashboards

Compare visuals and data across environments and BI tools with zero manual effort.

Performance and Load Testing for BI Assets

Simulate concurrent user access to measure response times and report load failures.

Access and Security Validation

Ensure only authorized groups have access to the correct records and reports.

Aesthetic and Metadata Change Detection

Identify formatting inconsistencies, filter changes, and layout drift with each release.

Products

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DataOps Suite

Intelligent Data Validation and Analytics Testing Platform with Agentic AI.

ETL Validator automated ETL testing tool

ETL Validator

Automated Data Validation and ETL Testing with Agentic AI.

BI Validator automated BI testing tool

BI Validator

Smarter BI Validation For Power BI, Tableau, Oracle Analytics โ€“ Accelerated by AI Agents.

Data Quality Monitor software

DQ Monitor

Proactive Data Quality with Agentic AI โ€“ Predict, Prevent, Govern.

Test Data Manager software

Test Data Manager

Generate compliant and realistic test data for all your testing needs, enabled by Agentic AI.

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