DataOps Validation + Data Observability + AnalyticsOps Validation

One Platform that Validates

Q: How does it differ from Monte Carlo or QuerySurge?

QuerySurge and iceDQ test ETL pipelines only — they have no BI validation, no data quality monitoring, and no test data capability. Monte Carlo monitors production anomalies statistically — no pre-production testing, no 100% row validation, no BI dashboards. DataOps Suite covers all five layers: ingestion, ETL/ELT, data quality, BI dashboards, and AI/ML inputs — on a shared Spark engine with one ruleset and one audit trail. Technical differentiators: Agentic AI self-healing tests, embedded LLM with zero data egress, and Gartner recognition in both DataOps and Data Observability — which neither competitor holds.

Q: Does it connect to Snowflake, Power BI, Databricks?

Yes — 200+ native connectors, zero connector engineering required. Data layer: Snowflake, Databricks, Azure Synapse, Redshift, BigQuery, Oracle, SQL Server, SAP, IBM DB2, and 170+ more — with native pushdown execution to keep data in-environment. BI layer: Power BI, Tableau, Oracle Analytics, BusinessObjects, Cognos, MicroStrategy. DevOps layer: Jenkins, GitHub, GitLab, Azure DevOps, Collibra — for pipeline-embedded test execution. All connections are point-and-click. No JDBC wiring, no custom adaptors, no professional services required. View all integrations →

Q: Is my data secure — does it leave my environment?

No. The embedded LLM runs inside your own infrastructure — no data is sent to OpenAI, Anthropic, or any external AI service during test authoring, quality analysis, or anomaly detection. For cloud deployments, processing runs within your own cloud tenant. DataOps Suite is SOC 2 Type II and ISO 27001 certified. Test Data Manager generates HIPAA and GDPR-compliant synthetic data with deterministic PII masking, so sensitive records never enter test environments. Role-based access control and column-level audit trails are enforced across all four modules. View compliance capabilities →

BI Analytics & AI Output

DataOps Suite — An Agentic AI DataOps and Data Observability Platform that replaces
five disconnected tools with one connected source of truth across ETL, BI & AI

DataOps Suite Platform Overview

Discover how enterprises automate data testing, quality validation, BI assurance, and observability from a single platform—accelerating cloud modernization while delivering trusted data, faster

One Platform. Five Validation Layers. Shared Everything.

Most organisations run separate tools for ETL testing, data quality monitoring, and BI validation creating rule silos, inconsistent coverage, and no cross-stage lineage. DataOps Suite eliminates that entirely: one shared rules engine, one lineage graph, and a unified data health dashboard across all five pipeline stages.

ETL Validator is a low-code platform for automated ETL/ELT testing, ensuring accurate, trustworthy data across cloud and on-prem pipelines for faster, reliable delivery.

BI Validator is a no-code BI testing tool that automates validation of dashboards, data, and performance – ensuring integrity, consistency, and trust across analytics environments.

Data Quality Monitor ensures complete, accurate, and trusted data with AI-powered validation, data observability, profiling, and reconciliation across all stages of your data pipeline.

Test Data Manager uses AI to generate high-quality, privacy-safe synthetic data, enabling realistic, scalable testing of applications, pipelines, and analytics without exposing sensitive information.

Each Product Solves a Distinct Data Trust Problem

Deploy the full DataOps Suite for end-to-end coverage — or start with the product that solves your most urgent problem today. As your needs grow, seamlessly extend coverage across data quality, ETL testing, BI validation, and test data management.

Automated 100% row-level validation across ETL, ELT pipelines, and cloud migrations — every record, every run, no sampling.

Cell-level regression and functional testing for Power BI, Tableau, and Oracle Analytics — catches dashboard errors before they reach decision-makers.

Continuous data quality monitoring with Agentic AI — proactive anomaly detection, quality scoring, and real-time observability from ingestion to AI.

Agentic AI-powered synthetic data generation and PII masking — production-realistic test environments with zero real customer data.

DATAGAPS DataOps Suite

The Comprehensive End-to-End Data Validation Platform for Every Enterprise

Unmatched Credentials You can Trust

The only DataOps and Data Observability platform with US patent, Informatica certification, SOC 2 Type II, and an embedded LLM — your data never leaves your environment

DataOps Suite vs Point Solutions

Every alternative is a point solution. DataOps Suite covers the full lifecycle.
Here is the technical capability comparison that enterprise evaluation teams run.

Capability	DataOps Suite Platform	ETL / Pipeline Testing Tools	Data Observability Platforms	In-house / LLM-Built
BI & Report Testing
BI Regression & Visual Testing	Power BI, Tableau, OAC, Looker, QuickSight & more	Data-Level Only	Not Supported	Not Supported
ETL / Pipeline Validation Depth	Report Performance Testing	Not Supported	Not Supported	Not Supported
BI Functional Testing	Test report data directly against source databases	Tedious To Configure At Scale	Monitoring Only	No Unified Workflow
AI & Data Intelligence
AI Anomaly Detection	Auto-detects; no manual rules needed	Explicit Tests Only	Core capability	Hallucination Risk
Data Profiling & DQ Scoring	Built-in profiling + quantified DQ score	Pass/Fail Only; No DQ Score	Profiling Only; Limited Scoring	Must Be Built Separately
Data Lineage Tracking	End-to-end pipeline lineage & audit trail	Execution Logs; Basic Lineage	Lineage Tracking; Varies By Platform	No Systematic Lineage Capture
Test Automation & Usability
AI Test Case Generation	Auto-generates from ETL mapping docs;	ETL-Focused Only	Not Supported	Requires Manual Validation
Low-Code / No-Code Interface	Drag-and-drop; no SQL required	SQL Expertise Required	Config-Based; Ops Only	Engineers Only
Synthetic Test Data	AI-ML generated; PII-safe, no prod copy	Not Supported	Not Applicable	Privacy Risk Remains
Integration & Extensibility
CI/CD Integration	Azure DevOps, GitHub, GitLab & more	Core strength	API-Based Only	Custom Build Required
Reusable Templates & Rules	Cross-project & cross-environment	Limited Portability	Not Cross-Platform	Bespoke Per Pipeline
Alerting & Notifications	Failures + DQ threshold breaches	Test Failure Only	Anomaly Thresholds Only	Custom Build Required
Security & Deployment
Data Security & LLM Privacy	Embedded LLM; data stays in-environment	Varies By Vendor	Some Egress Risk	High Data Egress Risk
Cloud-Native Deployment	Kubernetes, Databricks, on-prem	No Native K8s / Databricks	Monitoring-Focused Only	Manual Infra Management
Architecture & Performance
Scalability & Execution Engine	Apache Spark; petabyte-scale auto-scaling	DB Execution Only; Degrades At Scale	Monitoring Scale Only	Breaks At High Volume
Source & Target Connectivity	200+ native connectors	~100–200; JDBC-Focused	~30–100; Read-Only	Custom-Built Per Source
Row-Level Validation Accuracy	100% rows; zero sampling gaps	Sampling At High Volumes	Statistical Sampling Only	Depends On Implementation

Trusted by Data Teams Worldwide

See how leading companies are transforming their data operations with Datagaps

“With Datagaps, we saw about 70% time savings, an 80% reduction in turnaround time and 100% adherence to our UI validation best practices.”

“ETL Validator has really freed the IT department to look at real business problems and not worry about the trust and veracity of the data… “

“With Datagaps, we moved from reactive data correction to proactive data governance, with audit-ready reports available for our leadership and ETL teams.”

Trusted by 100+ Leading Enterprises

Get Started Today

See DataOps Suite in Action

Our team will run a live demo against your specific use case — your stack, your data problem, your pipeline.
No slides. No generic walk-through. Your actual scenario.

SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment

Frequently Asked Questions

Common questions from Enterprise Buyers

What is the Datagaps DataOps Suite?

DataOps Suite is Datagaps’ unified data testing platform — combining ETL Validator, BI Validator, Data Quality Monitor, and Test Data Manager on a single Apache Spark engine with shared rules, unified lineage, and one audit trail. Unlike point tools that test one stage, it validates the entire pipeline — ingestion, ETL/ELT, data quality, BI dashboards, and AI model inputs — from one platform. The only product in both Gartner’s DataOps Tools and Data Observability market guides. SOC 2 Type II. Embedded Agentic AI. 200+ native connectors. 100+ enterprise deployments.

How does Agentic AI work in DataOps Suite and does it write tests automatically?

Yes — and it operates entirely inside your environment. DataOps Suite’s embedded LLM auto-generates test cases from your schema, SQL, or plain-English prompts; extracts field-level mappings from ETL documentation and stored procedures automatically; self-heals tests when schema drift occurs — no manual rework; summarises BI report differences after each refresh; and recommends context-aware data quality rules based on actual data patterns in DQ Monitor. No data is sent to OpenAI, Anthropic, or any external AI service at any stage. See it in action →

How does DataOps Suite validate ETL pipelines?

ETL Validator uses Apache Spark for parallel execution — validating 100% of records across source and target simultaneously, not sampling. Agentic AI auto-generates test cases from your schema or stored procedures; pushdown execution runs natively inside Snowflake, Databricks, and Azure Synapse without data movement. For data migration testing, row-level parity checks, transformation logic validation, and reconciliation reports are generated with no manual SQL scripting. ZS Associates validated 100% of records in a Snowflake migration with zero post-go-live defects. See ETL testing in depth →

How does it differ from Monte Carlo or QuerySurge?

QuerySurge and iceDQ test ETL pipelines only — they have no BI validation, no data quality monitoring, and no test data capability. Monte Carlo monitors production anomalies statistically — no pre-production testing, no 100% row validation, no BI dashboards. DataOps Suite covers all five layers: ingestion, ETL/ELT, Data Quality, BI dashboards, and AI/ML inputs — on a shared Spark engine with one ruleset and one audit trail. Technical differentiators: Agentic AI self-healing tests, embedded LLM with zero data egress, and Gartner recognition in both DataOps and Data Observability — which neither competitor holds.

Does it connect to Snowflake, Power BI, Databricks?

Yes — 200+ native connectors, zero connector engineering required. Data layer: Snowflake, Databricks, Azure Synapse, Redshift, BigQuery, Oracle, SQL Server, SAP, IBM DB2, and 170+ more — with native pushdown execution to keep data in-environment. BI layer: Power BI, Tableau, Oracle Analytics, BusinessObjects, Cognos, MicroStrategy. DevOps layer: Jenkins, GitHub, GitLab, Azure DevOps, Collibra — for pipeline-embedded test execution. All connections are point-and-click. No JDBC wiring, no custom adaptors, no professional services required. View all integrations →

Is my data secure — does it leave my environment?

No. The embedded LLM runs inside your own infrastructure — no data is sent to OpenAI, Anthropic, or any external AI service during test authoring, quality analysis, or anomaly detection. For cloud deployments, processing runs within your own cloud tenant. DataOps Suite is SOC 2 Type II and ISO 27001 certified. Test Data Manager generates HIPAA and GDPR-compliant synthetic data with deterministic PII masking, so sensitive records never enter test environments. Role-based access control and column-level audit trails are enforced across all four modules. View compliance capabilities →

Can it integrate with our CI/CD pipeline?

Yes — DataOps Suite is designed for pipeline-native automated testing. Native integrations include Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, and Collibra, plus a full REST API for any tool not covered natively. Test cases execute automatically on each commit, merge, or deployment trigger. Validation results return as pass/fail gates — blocking bad data from promoting to production without manual intervention. Parameterised test cases run across dev, staging, and production with no reconfiguration. See CI/CD integration documentation →

How does DataOps Suite handle schema changes without breaking existing tests?

Automatically. Agentic AI monitors schema changes in real time and self-heals affected test cases — no manual rework. When a column is added, a type changes, or a table is renamed, the platform detects the drift, adjusts relevant validations, and flags changes for review in a single dashboard. For cloud migrations and continuous data observability scenarios, drift detection runs through go-live and beyond. Both ETL Validator and DQ Monitor include self-healing test maintenance — removing the primary reason automated testing programmes fail at scale.

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One Platform that Validates

DataOps Suite Platform Overview

One Platform. Five Validation Layers. Shared Everything.

Each Product Solves a Distinct Data Trust Problem

ETL Validator

Row-level source-to-target reconciliation

AI-generated test cases

Self-healing tests

BI Validator

Self-healing BI tests

Cross-layer trace

Performance + stress testing

Data Quality Monitor

1,000+ configurable quality rules

Agentic AI rule generation

ML-based anomaly detection

Test Data Manager

Dynamic PII masking

Synthetic data generation

AI/ML training datasets

DATAGAPS DataOps Suite

Ingestion

Transformation

Semantic Model

BI Reports

AI Consumption

Unmatched Credentials You can Trust

The only data validation platform in both Gartner DataOps Tools and Data Observability Market Guides