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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.
Explore DataOps Suite Platform

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.
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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.
Explore ETL Validator

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.
Explore BI Validator

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.
Explore Test Data Manager
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 you’ll save.

Data Quality Use Case

Start from the outcome your team needs, with Agentic AI doing the groundwork.

Data Migration Testing
Proves data moved intact from legacy and on-prem systems to new platforms through cutover.
Data Reconciliation
Finds missing, mismatched, orphan and duplicate rows between source and target.
Data Observability
Tracks freshness, volume and schema changes across pipelines, with AI agents tracing root causes.
Data Remediation
Routes failing records to owners, suggests fixes and tracks each one to resolution.
Data Compliance
Audit-ready evidence for SOX, HIPAA, GDPR, CCPA, FERPA, NAIC and MAR.
AI Ready Data
Validates the data feeding ML models and catches drift before retraining.

Cloud Platforms Use Case

Validate and monitor data and its quality on the platforms you already run, backed by Agentic AI.

Snowflake
Validates and scores data inside Snowflake with SQL pushdown, catching schema drift and migration gaps.
Databricks
Validates Delta Lake tables from Bronze to Gold inside notebooks, with results in Unity Catalog.
Microsoft Fabric
Validates Fabric data from OneLake ingestion through Lakehouse and Warehouse to Power BI semantic models.
Azure Synapse
Validates Synapse migrations and Medallion-layer transformations, with tests running in Azure DevOps.
Salesforce
Checks Salesforce objects, uploads and updates for completeness and quality on every migration and ingest.

BI and Analytics Use Case

Keep every report accurate across your BI tools, as Agentic AI builds the tests for you.

Power BI
Matches visuals to source data and tests DAX, slicers and row-level security from Dev to Prod.
Tableau
Matches dashboards to source tables, checks calculated fields and row-level security, and catches drift across versions.
Oracle Analytics
Matches analyses to SQL results, tests subject areas and permissions, and validates OBIEE to OAC migrations.
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.
See How it Works

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.
See How it Works

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.
See How it Works

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.
See How it Works

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.
See How it Works

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.
See How it Works

Trusted Data Summit

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

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

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Solutions

Use cases

Data migration testing
Proves data moved intact from legacy and on-prem systems to new platforms through cutover.
Data reconciliation
Finds missing, mismatched, orphan and duplicate rows between source and target.
Data observability
Tracks freshness, volume and schema changes across pipelines, with AI agents tracing root causes.
Data remediation
Routes failing records to owners, suggests fixes and tracks each one to resolution.
Data Compliance
Audit-ready evidence for SOX, HIPAA, GDPR, CCPA, FERPA, NAIC and MAR.
AI Ready Data
Validates the data feeding ML models and catches drift before retraining.

Cloud platforms

Snowflake
Validates and scores data inside Snowflake with SQL pushdown, catching schema drift and migration gaps.
Databricks
Validates Delta Lake tables from Bronze to Gold inside notebooks, with results in Unity Catalog.
Microsoft Fabric
Validates Fabric data from OneLake ingestion through Lakehouse and Warehouse to Power BI semantic models.
Azure Synapse
Validates Synapse migrations and Medallion-layer transformations, with tests running in Azure DevOps.
Salesforce
Checks Salesforce objects, uploads and updates for completeness and quality on every migration and ingest.

BI and analytics

Power BI
Matches visuals to source data and tests DAX, slicers and row-level security from Dev to Prod.
Tableau
Matches dashboards to source tables, checks calculated fields and row-level security, and catches drift across versions.
Oracle Analytics
Matches analyses to SQL results, tests subject areas and permissions, and validates OBIEE to OAC migrations.

ROI calculator →

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DataOps Suite Sandbox

Getting Started – Sandbox Documentation

Introduction to DataOps Suite Sandbox

Welcome to the DataOps Suite Sandbox. Before starting with the specific use cases you want, we highly recommend watching this below video and documentation. This introduction will start with an overview of how test cases are created from scratch, followed by a few exercises. The solutions of the exercises are already present in the Sandbox for reference.

ETL Testcase Creation Flow

DataOps Suite has a full-fledged ETL tool as its backbone to support its ETL Validation capabilities. Thus, it helps to look at the product as kin to typical ETL tools.

Firstly, a source and target connection need to be defined to pull and push datasets with the Suite.

These connections are defined as “Sources” in the Sources Tab and can range from JDBC Connections to Flat Files to APIs and much more.

Once the connections are defined, data can be pulled into Suite and then transformed, tested, compared, profiled, analyzed, and/or pushed back into a different target. This is achieved in the Dataflows Tab. After test cases (dataflows) are created, they can be bundled together, sequenced, and ran as Pipelines in Pipeline Tab. The Scheduler helps you schedule the pipeline run. All dataflow and pipeline runs have a History section to look back at previous runs.

A vast majority of application uses are to follow here, such as:
  • REST API Connections to DataOps Suite for automation and integration
  • Parameterization
  • Plugins
  • Password Vault
  • CLI Commands
  • In-Suite Wizards for Automation of Test Case Creation

For these, we highly recommend tinkering with the application, going through the documentation, and connecting with our support desk (accessible via the application). We also provide a full fledge training certification for the Suite.

Read: 5 Must haves in your Data Testing Platform

Sandbox Exercises

Before testing specific use cases, kindly attempt the following exercises in the order provided to understand the Suite better. We will be working with the following business use cases as the basis of our exercises.

Our architecture involves a SQL Server, an Oracle DB, and an AWS s3 as our main 3 data sources. We want to perform the following validation –

    1. The Dataset “DimCustomer” is being Migrated from Oracle to SQL Server.
      1. Validate the Metadata of the dataset between Oracle and SQL Server.
      2. Validate all records between the two datasets to ensure data has been migrated correctly.
    2. The records in the file “Customers.csv” in the s3 location have to be validated for the following –
      1. Null checks on Primary key
      2. Check for duplicates in the Primary key
      3. Validate if the Year of Birth is between 1930 and 1990
      4. Validate the Email Addresses to contain “@company.com”
      5. Validate that the average credit limit is below 6500
    3. ETL validation (White Box Testing) – A Developer has updated the DimEmployee Table with the logic as directed by the business team. You need to validate the transformation using White Box Testing Technique.
      1. You are provided with the data dictionary of a table
      2. You are provided with the transformation logic
      3. You are provided with the updated file
      4. Validate the transformation by re-creating the transformation

Source Validation

Note: The first step of the process, the creation of Sources, is already done in the Sandbox.

Before proceeding, kindly validate your Data Sources. To do so –
  1. Go to the “Sources” Tab
  2. Click on “Data Source(s)”
  3. Click on each source and click on “Test” (top-right)
  4. This will validate the connection
Post this; the exercise is focused on the “Dataflows” tab. Here you should see three flows already built as solutions for the exercises for reference.
Source Validation

Exercise 1 – Migration Validation

Context – Validation of migrated assets is a primary use case in many migrations and DQ processes.

The Dimension Table “DimProduct” was transferred from an Oracle DB to a SQL Server. We need first to validate the metadata of both tables; then, we need to validate all the records of both tables against each other.

Create a new dataflow within the Dataflow Tab using the “Create Dataflow” button (top-right).

Nodes used – JDBC Connector, Metadata Compare, Data Compare

Adding source nodes in DataOps Suite
Adding source nodes in DataOps Suite
Adding a data compare node in DataOps Suite
Adding a data compare node in DataOps Suite

Steps –

    1. First, let’s compare the metadata of the table in Oracle and SQL Server.
      1. Add the Metadata Compare Node to the tray
        1. Select the Sources as intended
        2. In the “Objects” tray, drag and drop only “DimProduct”
          1. Its counterpart should automatically be found and added
        3. Click “Run” to see the results. This wraps up the Metadata Comparision part.
      2. Next, let’s pull the dataset DimProduct from the Oracle DataSource.
        1. Add the JDBC node to the tray
        2. Select Oracle as the Data Source
        3. In the Query Window, either use the query builder, drag the table from the metadata window post-search or type out the required query
        4. You can preview and row-count your query before clicking run
        5. This dataset is now loaded
      3. Repeat the process for the “DimProduct” from SQL Server
      4. Now, we do the data comparison test –
        1. Add the “Data Compare” Node. Make sure this node is dependent on both the JDBC connectors.
        2. Select the two datasets from the two distinct data sources
        3. Update the options as per the metadata results. Note – Since there are inherent differences, if you want to validate the data while converting the data types to the correct middle ground, define those and other options here and in the Mapping Tab
        4. In the Mapping Tab, make the necessary datatype conversions (in most cases, simple conversion to integer or double would suffice). Remove the columns which you don’t want to compare.
        5. Run the Node to see the results.
Results –
  1. The metadata is very different and filled with mismatches. This is expected when working with different databases, as auto-importing and settings change the data.
  2. The datatype mismatches and lack of columns between the two sets cause a lot of mismatches to be found.
  3. Post, using the advanced functions, mapping a few columns to integers and doubles, along with removing the “LargePhoto” and “FinishedGoodsFlag”, we get a full match.
    1. The reason we removed the flag has to do with the conversion of “true/false” to “1/0” which would require a conversion. This is possible using the custom expression in the mapping. For the sake of simplicity, we remove this column for now.
    2. The reason we removed LargePhoto has to do with the lack of the column in the SQL Server
  4. We see that the “Weight” column has 14 mismatched records. Note that these mismatched records can be pulled as a dataset in subsequent nodes for data reconciliation, processing, or sinking.

Exercise 2 – Data Quality

Context – Validation of the data based on integrity, completeness, business requirements, and logical checks is part of any type of data validation test case. One might want to validate a view, a function, the response from APIs or any type of data. In this example, we will validate the data present in the file “Customers.csv”. We want to validate the following –
  1. Null checks on Primary key
  2. Check for duplicates in the Primary key
  3. Validate if the Year of Birth is between 1930 and 1990
  4. Validate the Email Addresses to contain “@company.com”
  5. Validate that the average credit limit is below 6500

Create a new dataflow within the Dataflow Tab using the “Create Dataflow” button (top-right).

Nodes used – File Connector, Data Rule

Adding source nodes in DataOps Suite
Adding data rule in DataOps Suite

Steps –

  1. First, pull the file using the File Connector in Sources.
    1. Select AWS S3 as the source
    2. In the File Tab, browse and find the file “Customers.csv”
    3. Update the read parameters if needed
    4. Run the Node to load the dataset
  2. Now, we add the validation rules.
    1. Add the “Data Rule” Node. Make sure this node is dependent on the File connector.
    2. In the Rules Tab, add the rules as per the requirement. As a hint, here are the type of rules you will need for the business use cases are
      1. Duplicate Check
      2. Attribute Check
      3. Metric Compare
    3. Run the Node to view the results and the bad records
Results –
  1. Most of the records pass the test with a score of 99.1
  2. Year of Birth test fails as 1920s records exist in the dataset
  3. Email Address test fails as “company2.com” exists in the dataset

Exercise 3 – Transformation Validation

Context – Based on business requirements, the DimEmployee table has to be updated. The mappings are provided on the right. A developer already implements the changes, and the updated dataset is saved as a file named “Dim_Employee_Updated” in AWS S3. You are a QA tester and will perform white box testing to validate this transformation. Using the mapping spreadsheet, create an updated version based on your implementation of the raw data from the SQL Server and compare it to the updated CSV file to validate the transformation.

Create a new dataflow within the Dataflow Tab using the “Create Dataflow” button (top-right).

Nodes used – File Connector, JDBC Connector, SQL

Mapping Spread Sheet
Mapping Spread Sheet
Steps –
  1. First, using the JDBC Connection, create a node and connect to SQL Server
    1. Pull the DimEmployee Table
  2.  Using the SQL or Code node, apply the transformation logic
  3. Add a File Connector and pull the file from the folder – “Dim_Employee_Updated”
  4. Add a Data Compare node that compares the Transformed Data and the Updated CSV File with the correct options.
Results –
  1. We see a ~19% mismatch only on the BaseRate column.
  2. Further inspection reveals that the Developer likely used a “Floor” function instead of a “Round” function.

Data Quality Monitor

The sandbox also comes with a Data Model in the Data Quality Tab with rules already defined test cases to help new users see how business rules and data quality test cases can be defined. New users are encouraged to explore DQM by –

    1. Creating a New Data Model
    2. Importing Tables for the Model
    3. Adding Rules to tables
    4. Importing and Exporting Rules
    5. Using the Wizards for automation of Rules Creation

These activities are left for the user as an exercise. The video can be used as a reference.

How to add a Data Model in DataOps Suite

Conclusion

Thank you for completing the exercises. With the basics covered, you and your team should understand the plethora of nodes and toolsets the application provides better. Feel free to tinker with the environment to fit your specific use cases and explore all the nodes and applications. The application can also be connected via CLI Commands, REST APIs, and integrated plugins. Our documentation, which can be easily accessed in the bottom left corner of the Suite, will provide your team with ample starting resources. The application comes with wizards, pipelines, and monitoring capabilities that new users can easily configure and deploy. In case of any specific examples, feel free to contact us directly.

In case of any questions or errors that your team cannot resolve, feel free to contact our support team either by contacting us on our email or using the support portal present in the bottom left corner of the Suite.

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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 – Accelerated by AI Agents.

Data Quality Monitor software

DQ Monitor

Proactive Data Quality with Agentic AI – 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.

Solutions

Use Case

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Data Migration Testing
Data Observability
Data Reconciliation
Data Remediation
Compliance
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Snowflake
Databricks
Azure Synapse
Salesforce
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Power BI
Tableau
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Healthcare
BFSI
Life Sciences
Retail & Utilities
Manufacturing
Higher Education
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Datagaps DataOps Data Observability Tool

Data Observability

Data Reconciliation tool

Data Reconciliation

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