The Best Open Source Data Quality Tools for Modern Data Teams

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by Emily Winks, Data governance expert, Atlan.Last Updated on: January 06th, 2026 | 30 min read

Quick answer: What are open source data quality tools?

Data quality tools help teams ensure their data is accurate, complete, consistent, and usable across systems. With open-source tools, you can operate at lower fixed costs while still maintaining the flexibility and control teams desire.

Top open source data quality tools to consider in 2026:

  • Deequ
  • dbt Core
  • MobyDQ
  • Great Expectations
  • Soda Core
  • Cucumber

These tools form the core of any data quality program, which is put in place to ensure data reliability. They scan datasets to identify errors such as missing values or broken relationships. Such scans are automated to maintain an organization’s trust in analytics and reporting at scale.

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Most data teams wake up to a broken dashboard or an executive asking why yesterday’s number no longer matches last week’s.

Data quality becomes visible when it fails. Then, stakeholders decide to solve for it. According to Gartner, when data quality fails, it costs organizations an average of $12.9 million a year. To ensure you aren’t at risk of such damages, multiple tools, teams, and frameworks play a part.

Open-source data quality tools exist because the failure patterns are predictable. For example, schema changes slip through. Volumes drop quietly. A null creeps into a key column and cascades into bad decisions. Naturally, teams respond by adding checks, then more checks, until quality becomes a pile of scripts no one wholly owns.

This article cuts through that noise. Instead of treating data quality tools as interchangeable rule engines, it explores what each open-source tool is actually good at and how teams use it in production. This helps you choose a tool that matches your data maturity, not your ambitions.

Data quality is not about running more tests. It’s about knowing when to trust your data and when not to. This guide helps you get there without the pretext that one tool can do everything.

Top 10 open source data quality tools: At a Glance

Here’s an overview of the top open source data quality tools and when they’re a best fit for an organization:

  • Great Expectations (GX): Best when a data engineering team wants expressive Python-based tests tightly integrated into CI pipelines and orchestration workflows.
  • Soda Core: Best when teams want lightweight SQL-native quality checks with broad connector support and fast setup inside existing pipelines.
  • dbt: Best when transformations are already run in dbt and teams want shift-left quality checks embedded directly in the modeling layer.
  • Cucumber: Best when business, product, and engineering teams need shared human-readable specifications that double as executable data validation rules.
  • Deequ: Best when data processing happens on Spark and quality checks must scale across massive datasets without sampling.
  • DataCleaner: Best for early-stage audits, ad-hoc profiling, and visual inspection of messy data before formal pipelines exist.
  • MobyDQ: Best when teams want collaborative indicator-based quality monitoring with a web UI rather than code-only frameworks.
  • OpenMetadata: Best when data quality must connect to ownership lineage and governance across a large shared data estate.
  • Datachecks: Best when teams want simple configuration-driven monitoring to detect reliability and distribution changes over time.
  • Open Source Data Quality (OSDQ): Best when the primary goal is profiling, cleansing, and standardizing messy data rather than pure validation.

When to use an open source vs. a licensed platform?

Licensed data quality tools make a case when teams require a vendor to support service-level agreements (SLAs), governance workflows, and need packaged integrations. If the organization lacks data engineering bandwidth, onboarding a platform can be the right call. Moreover, it makes it easier for non-technical stakeholders to operate.

You can start with an open-source data quality tool to prove value. Then graduate to a unified data quality and observability platform as volume and regulatory requirements increase.

If you’re confused about whether you should go with open-source vs. licensed platforms, the following section provides the clarity you need.

Open-source data quality tools vs. licensed data quality tools


Here’s a small reflection checklist that will help you get a clearer picture of which tool you need:

Ask yourself

Yes

No

Is the data environment getting too large to manage manually?

Do compliance and audit needs go beyond basic checks?

Are teams able to link data issues to business impact?

Is data quality now a shared responsibility across teams?

Is tool maintenance taking more time than fixing data?

If you have answered “Yes” to one or more questions in the table above, there is a high chance that you need a robust, licensed enterprise plan from a reliable vendor for data quality monitoring. Teams outgrow open source data quality tools when scale, governance, and coordination start to slow them down.

As data environments expand, manual setup and maintenance become increasingly complex, especially across hundreds of pipelines and datasets. Moreover, when data quality moves beyond engineering, leaders need to see how data issues affect dashboards and AI models, not just technical checks.

What are the top open source data quality tools in 2026?

If you’re starting and want to prove value with open source data quality tools, here’s a deep dive into the top options on the market.

In this article, we’ll talk about these open-source data quality tools in detail:

  1. Validation and testing frameworks: Great Expectations (GX), Soda Core, data build tool (dbt), Cucumber*
  2. Big data and distributed processing tools: Deequ
  3. Profiling and discovery tools: DataCleaner
  4. Collaborative and Web-Based Platforms: MobyDQ and OpenMetadata
  5. Specialized and Emerging Tools: Datachecks and Open Source Data Quality (OSDQ)

Note: Go through this list chronologically, or feel free to check out the choices under your consideration individually.

A. Validation and testing frameworks


Validation and testing frameworks define rules for correct data and ensure all data meet quality standards before being used in dashboards or models. Data teams write reusable tests that run automatically as part of pipelines or on a schedule. When a test fails, the system immediately flags the issue, making data quality observable and repeatable rather than manual and ad hoc.

1. Great Expectations


Great Expectations (GX) takes a Python-first approach and asks teams to define expectations that clearly describe what valid data should look like. These expectations serve as tests and documentation simultaneously.

GX works best when data quality is treated as part of engineering, not as an afterthought.

Why practitioners choose GX


Here are a few things about GX that make it stand out:

  • Clear and expressive validation model: Teams write rules such as “this column should never be null” or “values should fall within this range” in a readable format. These rules look close to business logic, and non-authors can review them easily.
  • Large library of built-in checks: GX ships with hundreds of prebuilt expectations covering completeness, uniqueness, formats, distributions, and basic statistics. This saves time compared to writing custom SQL or Python checks for common issues.
  • Strong ecosystem integrations: Teams frequently highlight how well GX fits into modern data stacks. It integrates natively with orchestration tools and works across warehouses and file systems.
  • Data Docs improve visibility: GX automatically generates them, turning test results into browsable reports. It makes data quality visible without building custom dashboards.
  • Scales with disciplined teams: For large data engineering teams, GX becomes a shared language for quality. Expectations live in version control, run in CI pipelines, and fail builds when data breaks contracts.

Did you know? Open-source data quality tools determine whether the data is valid right now. However, business teams ask if they can trust this data to make a decision. This disconnect between data validity and the trust needed to make a decision creates friction. Atlan removes the friction.

Why do you need Atlan with an open-source data quality tool like GX?

Atlan Data Quality Studio sits above GX and connects the dots. It uses data quality signals from tools like GX and aggregates business context by attaching quality checks to tables, columns, models, and metrics, with clear descriptions.

Atlan maps failed checks to owners and downstream assets, so issues don’t die in logs. This helps you operate on data quality rather than just running checks.

What to look out for when choosing Great Expectations


GX might feel heavy for simple checks. Even basic validations require configuring datasources, suites, and storage backends. It can feel excessive if your team only needs row counts, null checks, or freshness checks.

Some users feel the need for more consistent documentation and expect it to match the current product. GX works best when a dedicated data engineering team owns it.

Best fit for: GX is suitable for data engineering teams seeking comprehensive Python-based testing with strong orchestration integration.

2. Soda Core


Soda Core is an open-source command-line tool and Python library that helps teams test data quality directly within their data platform. It uses Soda Checks Language (SodaCL), a YAML-based domain-specific language (DSL) built specifically for data reliability checks.

Teams prefer Soda Core when they want a lightweight way to define checks or run scans in a CI/CD or orchestration.

Why practitioners choose Soda Core


Below are some reasons people choose Soda Core.

  • SodaCL keeps checks simple and readable: SodaCL reads like “row_count > 0” or “missing_count(column) = 0,” so engineers and analysts collaborate on what constitutes good data without writing much Python.
  • Soda Core stays SQL-native: Soda Core translates checks into aggregated SQL queries and runs them where the data lives. This design reduces data movement and fits modern warehouse-first stacks.
  • Strong connector coverage: Soda publishes connectors for newer platforms and formats, not just the usual big three warehouses. For example, it supports DuckDB, Trino, Dremio, and Dask/Pandas workflows.
  • Works well in pipelines and orchestration: Users regularly run Soda scans as a CLI step or programmatically via Python. Then, they use exit codes to determine whether a pipeline step passes or fails. This makes it easy to integrate into Airflow, Dagster, dbt workflows, and CI jobs.

What to look out for when choosing Soda Core


Some practitioners describe Soda Core OSS as nice but somewhat limited compared to full observability platforms. Teams often pair Soda Core with additional tooling to gain broader visibility and improve operational workflows.

Soda introduced SodaGPT for generating checks from natural language. Still, Soda now positions this capability as “Ask AI,” and it is tied to Soda’s product packaging rather than to pure open-source usage. Treat it as a productivity layer, not a substitute for data domain knowledge.

Best fit for: Soda Core is ideal for teams wanting comprehensive connector coverage with an accessible domain-specific language for defining checks. To get unified monitoring, an organization can rely on Atlan to aggregate Soda Core checks alongside various other quality signals.

3. data build tool (dbt)


dbt treats data quality as part of the transformation work. It runs tests right next to the SQL models that create your tables and views.

dbt’s quality model is simple: define tests in the codebase, run them every time the pipeline builds, and decide whether failures should warn or stop the run.

Why practitioners prefer dbt


Below are a few reasons why people choose dbt as their data quality tool.

  • Puts quality where most breakages happen: Most analytics break when transformations change. dbt catches these failures in the transformation layer, where teams write business logic.
  • Ships with useful default tests: dbt includes four built-in generic tests that cover many first-line-of-defense quality needs, such as unique, not_null, accepted_values, and relationships (foreign-key-style checks).
  • Makes testing repeatable inside the DAG: dbt compiles models, builds them in dependency order, and runs tests as part of the same workflow. Teams treat transformations and tests as a single deployable unit rather than as scattered scripts and ad hoc queries.
  • Scales test coverage through packages: dbt-expectations ports Great Expectations-style checks into dbt macros. It allows teams to run richer validation directly in the warehouse.

What to look out for when choosing dbt


dbt tests may not automatically detect every anomaly, like a subtle distribution drift, unless you explicitly add a test for it. They work best for rule-based checks and contract-like constraints. Many teams add monitoring on top when early detection is required across hundreds of tables.

Additionally, dbt might feel cumbersome due to poor project structure and weak modeling discipline.

Best fit for: Organizations already using dbt for transformations wanting to consolidate testing within existing workflows.

4. Cucumber


Cucumber is a behavior-driven development (BDD) framework that lets teams write tests in plain language using Gherkin. It does not typically start as a data quality tool. Still, teams can use it to express data rules as executable specifications when they need tight alignment between business intent and technical validation.

Gherkin isn’t a prettier way to write automation scripts. It’s more of a collaboration artifact.

Why practitioners prefer Cucumber


Here’s an overview of why people prefer Cucumber when comparing options:

  • Offers plain-language specs: Gherkin provides teams with a consistent format for describing expected behavior using Given/When/Then steps. Cucumber executes those steps through step definitions, so the spec stays connected to real checks.
  • Executable specifications that double as documentation: Cucumber encourages living documentation where the same scenarios describe requirements and confirm them in automation runs. This reduces drift between what teams say they want and what the system actually enforces.
  • Strong fit for requirement alignment: Practitioners often report that BDD with Cucumber improves consistency in acceptance criteria and reduces chaos across teams when they focus on shared understanding first, not automation first.
  • Language-agnostic ecosystem: Cucumber implementations exist for multiple programming languages, making it easier to adopt across different stacks.

What to look out for when choosing Cucumber


Cucumber delivers on collaboration when QA, product, and engineering actively co-author scenarios. If used in a vacuum, you might not get the benefits of its collaboration features.

Best fit for: Organizations that practice BDD and need a tight alignment between business requirements and technical validation.

B. Big Data and Distributed Processing Tools


When your data lives in Spark, Hadoop, or massive lakehouse tables, standard data quality tools might choke. Big data and distributed processing data quality tools run quality checks in parallel, close to where the data already sits.

These tools scale quality checks across large datasets and run rules such as completeness, uniqueness, range, and schema checks as distributed jobs on Spark.

Deequ is a popular tool in this category.

5. Deequ


Deequ is AWS Labs’ open-source library that helps teams run unit tests on data for huge datasets using Apache Spark. It runs natively on Spark, so it can validate data at the same scale and speed as the rest of your Spark jobs. This makes it a strong fit for Spark-based stacks.

Deequ focuses on computing quality metrics at scale and verifying constraints over time.

Why practitioners prefer Deequ


Here are some reasons why people pick Deequ in their data quality tech stack:

  • Built for big data scale: Deequ runs checks using Spark’s distributed processing. If your data already lives in Spark DataFrames, Deequ can test it without pulling data into a smaller system or sampling too aggressively.
  • Works on almost anything Spark can read: Deequ validates relational tables, CSVs, logs, and flattened JSON, as long as you can load it into a Spark DataFrame. This flexibility is practical for lake-style architectures.
  • Suggest constraints instead of starting from zero: Deequ includes a constraint suggestion system that profiles data and recommends checks based on observed patterns. This allows teams to bootstrap coverage faster, especially on unfamiliar datasets.
  • Scala-first with Python access when needed: Deequ is Scala-native. Teams that run Spark with Scala get the smoothest experience, and Python users can use PyDeequ for many workflows.

What to look out for when choosing Deequ


It’s not lightweight. If your pipelines don’t run on Spark, Deequ will feel heavier than warehouse-native tools that run simple SQL checks.

Although it’s powerful, Deequ isn’t a plug-and-play solution. You need orchestration, profiling, rule generation, and recurring jobs to run Deequ reliably in production. AWS published a DQAF framework to simplify several steps involved in production usage.

Best fit for: Deequ is suitable for organizations that use Spark-based data processing and AWS infrastructure and require big-data-scale validation.

C. Profiling and discovery tools


Profiling and discovery tools sit upstream in a data quality tech stack. These tools turn unknown, messy datasets into what you can govern and test with confidence.

6. DataCleaner


DataCleaner is suitable for quick, visual inspection of data quality issues. Teams often use it early in the data lifecycle for audits, cleanup, and exploratory quality analysis. DataCleaner works best as a plug-and-play profiling tool, but not as a continuous data quality system.

Why practitioners prefer DataCleaner


Below are some reasons why people choose DataCleaner.

  • Broad data source support: It connects to many relational databases, such as Oracle, MySQL, PostgreSQL, and SQL Server, as well as flat files such as CSV and Excel. It also handles less-structured and NoSQL-style sources, which helps teams dealing with heterogeneous data.
  • Visual dashboards help non-engineers: Unlike code-first tools, DataCleaner includes a GUI with charts and dashboards. These visuals help analysts and data stewards quickly spot quality issues without writing SQL or Python.
  • Usefulness for ad-hoc cleanup and audits: Teams often use DataCleaner for one-time data hygiene tasks, audits, or cleanup before loading data into analytics systems. Its profiling engine helps identify where problems exist before deciding how to fix them.

What to look out for when choosing DataCleaner


The project is maintained for compatibility, community feedback, and repository activity. There might be fewer frequent capability improvements.

The tool is inherently more manual. It expects users to run profiling jobs and review results manually. It makes it less suitable for automated CI/CD pipelines or production-grade data quality enforcement.

Best fit for: DataCleaner suits organizations needing visual profiling tools with minimal setup complexity.

D. Collaborative and web-based platforms


There are a few options that prioritize collaboration in data quality monitoring. MobyDQ and OpenMetadata are two popular names in this category.

7. MobyDQ


MobyDQ measures and tracks data quality indicators rather than building a complete observability platform. It offers a web interface, a Docker-based deployment model, and prebuilt indicator types. This allows teams to run quality checks without assembling the entire stack themselves.

MobyDQ positions data quality as something teams can monitor together, not just validate in code.

Why practitioners prefer MobyDQ


Here are a few reasons why practitioners prefer MobyDQ:

  • Ready-made indicators reduce setup effort: MobyDQ ships with predefined indicator types such as completeness, freshness, latency, and validity. Teams configure these indicators instead of writing custom logic for every check, which speeds up initial adoption.
  • Web interface lowers the barrier for non-engineers: Unlike code-only tools, MobyDQ includes a web UI that allows users to define indicators, run checks, and review results. This makes quality metrics visible to analysts, product owners, and other stakeholders.
  • Centralized storage of results: MobyDQ stores execution results in a Postgres backend. This allows teams to query historical outcomes and connect results to alerting or downstream workflows.
  • Docker-based deployment is practical: The project ships with Docker Compose, which makes it easier to deploy locally or in production environments. Teams do not need to wire together databases and services from scratch.
  • Designed to integrate into existing pipelines: MobyDQ does not try to replace orchestration tools. It focuses on running checks and exposing results, leaving scheduling, alerting, and remediation to the surrounding platform if needed.

What to look out for when choosing MobyDQ


MobyDQ focuses on predefined indicators and result storage. It doesn’t include advanced anomaly detection or a large ecosystem of third-party integration out of the box.

While indicators simplify setup, they may limit flexibility for teams with complex or highly custom business rules. Extending beyond built-in indicators requires a deeper understanding of the framework.

Best fit for: Teams needing collaborative quality monitoring with accessible interfaces for business stakeholders.

8. OpenMetadata


OpenMetadata combines metadata management, data discovery, lineage, and data quality into a single system. Instead of treating quality as a standalone activity, OpenMetadata connects quality checks directly to tables, schemas, owners, and downstream usage.

It positions data quality as a governance and visibility problem, not just a testing problem.

Why practitioners choose OpenMetadata


Below are a few reasons why OpenMetadata is a preferred choice for businesses:

  • Centralized metadata with real context: OpenMetadata ingests metadata from databases, data warehouses, BI tools, pipelines, and messaging systems. Teams can search datasets, understand schemas, see owners, and trace where data comes from and where it goes. This context is critical for making quality issues actionable.
  • End-to-end lineage and impact analysis: The platform provides lineage for tables, columns, and pipelines. When a quality check fails, teams immediately see downstream dashboards, reports, or models that may be affected.
  • Built-in data quality framework: OpenMetadata includes native support for data quality tests such as freshness, volume, null checks, and column-level rules. Teams define tests inside the platform and track results over time.
  • Works with existing quality tools OpenMetadata does not force teams to replace their current tools. It ingests quality results from external systems and displays them alongside metadata, lineage, and ownership. This creates a single place to understand data health.
  • Open source and extensible by design: The platform exposes APIs and supports custom ingestion connectors, policies, and extensions. Engineering teams adapt it to internal workflows and integrate it with CI/CD, access control, and governance systems.
  • Collaboration and ownership features: OpenMetadata supports documentation and issue tracking directly on data assets. This helps teams move from detecting issues to assigning and resolving problems.

What to look out for when choosing OpenMetadata


It might take some time to set up. Teams without a dedicated data platform might feel that the initial setup is heavy. Additionally, its community ecosystem is still maturing. Some features and connectors might need extra tuning in a real environment.

Verify these before you transition to OpenMetadata.

E. Specialized and emerging tools


There are a few data quality tools that are currently emerging and are specialized in a certain data quality practice, for example:

9. Datachecks


Datachecks is a newer open-source, configuration-driven tool for data quality monitoring. It focuses on running predefined metric checks from a YAML config and producing terminal and HTML reports that teams share. It supports both SQL databases and search data sources.

Datachecks positions itself closer to data monitoring than testing frameworks. It helps teams track whether data stays reliable over time, not just whether a single rule passes.

Why practitioners prefer Datachecks


Here are a few reasons why people choose Datachecks in their data quality tech stack:

  • Simple YAML configuration keeps adoption easy: Datachecks runs from a config file and a CLI command. This makes it approachable for teams that do not want a heavy framework, extensive code scaffolding, or a big UI rollout.
  • Metric-based monitoring covers early warning signals: Datachecks groups checks into reliability, numeric distribution, uniqueness, completeness, and validity metrics. This design helps teams detect both apparent failures and quieter changes, such as distribution drift or variance shifts.
  • SQL + search support is a practical differentiator: Many open source tools focus only on SQL sources. Datachecks explicitly supports SQL and search data sources, including Elasticsearch and OpenSearch, which can matter for log analytics and search-backed applications.
  • Reports make results shareable: Datachecks generates output in the terminal and can create an HTML report. That makes it easier to share findings with stakeholders who do not want to read logs.

What to look out for when choosing Datachecks


On PyPI, the project lists its development status as “Alpha.” You might expect breaking changes and occasional rough edges. On GitHub, there are a few operational rough spots, for example, the HTML report generation failing. There are multiple open feature requests, suggesting that the tool is evolving, and you would need hands-on troubleshooting in real-world deployments.

Datachecks emphasizes metric generation and reporting. It does not position itself as a complete incident management, lineage, and ownership workflows suite. If you need advanced alert routing, SLAs, anomaly models, and rich dashboards, you will likely add other tooling.

10. Open Source Data Quality (OSDQ)


OSDQ is a suite of open-source tools focused on data quality and data preparation. It gives teams a single place for profiling, governance-style checks, similarity matching, enrichment, and alerting, rather than stitching together many small utilities.

OSDQ exists in multiple forms, like a core library, a classic desktop UI, a web/REST layer, and a Spark/Hadoop-oriented module for big data scenarios.

Why practitioners prefer OSDQ


Here are a few reasons why people get OSDQ in their data quality tech stack:

  • Covers quality and preparation on a single platform: OSDQ explicitly targets profiling, filtering, governance-style checks, similarity checks, and enrichment/alteration as part of a single toolkit. That makes it worthwhile when the real need is to fix and standardize messy data, not just run tests.
  • Supports similarity checks with fuzzy logic: OSDQ highlights similarity checks and fuzzy matching as core capabilities, which can help with deduplication and entity resolution tasks. For example, matching customer names or addresses that do not precisely match.
  • Offers big data support through Spark/Hadoop modules: OSDQ publishes a Spark-based module and related Hadoop-oriented work to support large-scale profiling and processing. This matters if your quality work runs in a big data environment rather than only inside a warehouse.

What to look out for when choosing OSDQ


The Spark module is explicitly labeled as beta with an older release date, suggesting you should expect gaps compared to modern Spark-first quality tools.

Additionally, OSDQ offers a broad scope. In practice, broad-scope tools often require more configuration and more substantial internal ownership to fit cleanly into a real data platform. The web/ project itself positions itself as pre-beta, which is a sign to plan for hands-on integration work.

How to choose the right open-source data quality tool?

Most teams do not fail at data quality because they lack rules. They fail because they choose tools that do not match their maturity.

  • Early teams should prioritize simple profiling, easy rule definition, and low setup cost.
  • Growing teams need strong validation frameworks and orchestration support to keep quality checks reliable at scale.
  • Mature organizations should focus on trend analysis, metadata integration, and operational visibility, not just on pass/fail checks.

The right data quality tool is the one that fits your workflows and does not block where your data platform is headed next.

Here’s an overview of what evaluation areas to look for and capabilities to compare while choosing an open-source data quality tool:

Evaluation Area

What It Covers

Key Capabilities To Look For

Data Profiling and Discovery

Helps teams understand data before defining quality rules by analyzing structure, distributions, and patterns

• Basic profiling: data types, null rates, value distributions • Advanced profiling: outliers, correlations, seasonality • Automated rule suggestions based on discovered patterns • Optional ML-based anomaly detection without fixed thresholds

Validation and Testing Frameworks

Defines how quality rules are written, executed, and maintained

• Schema validation • Completeness checks • Uniqueness checks • Range and format validation • Cross-column logic (e.g., end_date > start_date) • Referential integrity checks • DSL-based rules for accessibility or Python for flexibility

Integration and Orchestration

Ensures quality checks run within existing data pipelines and tools

• Native support for databases, warehouses, lakes, and file formats • Orchestration integrations (Airflow, Prefect, Dagster, dbt) • Metadata and catalog integrations • Alerting via Slack, PagerDuty, Jira, or email

Results Storage

Stores historical validation results and enables long-term analysis

• Result storage in object storage, databases, or Parquet • Long-term retention support • Time-based trending of quality metrics • Visual indicators of degradation vs one-off failures

Practical guidelines on choosing the best data quality approach


The biggest mistake teams make is evaluating tools in isolation. The right choice depends on where and when quality checks run, not how powerful the rule engine looks on paper.

Data quality typically falls into four distinct categories. Your tool should align with at least one approach clearly and not fight the others.

1. Pipeline-embedded validation


Best for teams that want to stop bad data early.

If your goal is to prevent broken data from reaching dashboards, models, or downstream systems, you should prioritize tools that embed validation directly into pipelines. This approach shifts quality left. Checks run during transformations and block bad data before it spreads.

Use this to guide tool choice:

  • dbt tests work well if transformations already live in dbt and teams want simple, SQL-native checks.
  • Great Expectations and Soda Core fit teams that need more expressive rules but still want validation tied to pipeline stages.
  • Orchestrator support matters. Airflow, Prefect, or Dagster must treat quality checks as first-class pipeline tasks.

Pipeline-embedded validation is the default for modern data teams. You should choose based on how tightly these tools integrate with the transformation and orchestration layers.

Choose this approach if:

  • Your transformations already run in dbt or orchestrated pipelines
  • You want data quality failures to fail pipelines, not dashboards
  • Engineers own quality rules and want them versioned with code

2. Orchestrated scheduled checks


Best for monitoring drift and compliance

Not all quality issues appear during pipeline execution. Some emerge gradually as upstream systems change, volumes grow, or business behavior shifts. Scheduled checks run independently of pipelines and monitor data at regular intervals.

Modern implementations rely on orchestration tools to schedule execution, manage dependencies, and route alerts. More advanced setups use metadata-driven scheduling, where quality rules stored in catalogs automatically trigger execution without manual orchestration configuration.

Choose this approach if:

  • You need visibility into quality trends over time
  • Checks should run even when pipelines do not
  • Multiple teams consume the same datasets
  • Scheduled validation complements pipeline checks and doesn’t replace them

3. Real-time validation at ingestion


Best for high-risk or high-volume data

Some organizations cannot afford bad data entering the platform at all. In these cases, quality checks must run at ingestion boundaries before data is written to storage. Real-time validation commonly applies to streaming pipelines, APIs, and CDC systems. Patterns include validating events before writing to Kafka topics, enforcing schema and format checks at API gateways, and verifying CDC payloads as they arrive.

Most open source data quality tools are not designed for low-latency execution. Teams typically rely on streaming frameworks such as Kafka Streams, Apache Flink, or cloud-native services to embed validation logic directly into ingestion flows.

Choose this approach if:

  • You process high-volume or real-time data
  • Data errors create immediate business or operational risk
  • Latency and throughput matter more than rich reporting

Be cautious here. Forcing batch-oriented tools into real-time paths usually creates reliability issues.

4. Metadata-driven quality enforcement


Best for scaling across large data estates.

As data platforms grow, table-by-table rule configuration becomes unmanageable. Metadata-driven quality enforcement ties rules to data classifications and policies rather than individual assets.

For example, fields classified as PII may automatically require completeness and format checks. Business-critical tables may trigger stricter thresholds and improved monitoring. Ownership and alerting routes are derived from metadata rather than hardcoded logic. This approach scales quality management across thousands of assets without manual configuration. It also shifts quality from isolated checks to shared accountability.

Atlan’s Data Quality Studio follows this model by acting as a unified control plane. It integrates with open source and commercial tools and centralizes visibility. Quality issues get routed into daily workflows such as Slack, Jira, and BI tools.

Choose this approach if:

  • You manage hundreds or thousands of datasets
  • Multiple teams share responsibility for data quality
  • Governance and accountability matter as much as validation

How Atlan empowers a data quality tech stack

Open source data quality tools solve isolated problems well, but they struggle to deliver end to end data quality. They generate signals, not outcomes. The real challenge is not detection, but coordination and accountability. Harvard Business Review reports that only 3 percent of company data meets basic quality standards. Atlan closes this gap by turning technical checks into business confidence. Think of it as a control panel for data quality.

Atlan is the only active metadata platform that brings data quality, governance, and discovery into a single control plane. With Data Quality Studio, teams define, automate, and monitor quality rules directly in cloud warehouses like Snowflake and Databricks. Trust signals, contracts, and alerts are embedded into everyday workflows, making data quality visible, actionable, and ready for AI. Atlan also integrates upstream tools like Monte Carlo, Soda, and Anomalo to deliver a complete, 360 degree view, and is recognized as a leader by Gartner, Forrester, and Snowflake.

Here’s an overview of how AItlan acts as the operating model to derive quality outcomes for business teams:

A. Business-first data quality

  • No-code templates and natural language rules let any domain certify data is fit-for-purpose.
  • AI-suggested rules and smart scheduling scale quality coverage without bottlenecks.

B. Unified control plane

  • Integrates discovery, governance, and quality for a single source of truth.
  • Pulls in signals from partner tools for a holistic, 360° view of data health.

C. Embedded trust & automation

  • Quality checks run natively in Snowflake/Databricks—no data movement, instant results.
  • Trust signals, badges, and alerts surface in Atlan, BI tools, and Slack for real-time action.

D. AI & compliance ready

  • Data contracts and policy enforcement ensure only high-quality data powers analytics and AI.
  • Lineage and reporting center provide auditability and compliance at scale.

While open-source tools excel at execution, Atlan provides the coordination required to enable organization-wide trust in data.

Key capabilities of Atlan and pairing it with open source tools


  • No-code & SQL rule creation: For business and technical users to define expectations.
  • Native warehouse execution: Quality checks run directly in Snowflake/Databricks, leveraging existing compute.
  • Real-time trust signals: Badges, scores, and lineage overlays show data fitness instantly.
  • 360° quality integration: Aggregates signals from Monte Carlo, Soda, Anomalo, and more.
  • Automated alerts & reporting: Slack notifications, dashboards, and a reporting center for coverage, failures, and business impact.
  • Data contracts & policy enforcement: Formalize expectations and automate compliance.

Avoid simply running more checks. Think qualitatively and operate on data quality to drive outcomes with Atlan.

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FAQs about open source data quality tools

Are open source data quality tools free?


Open source is no license, but not free. Checks run on warehouse/Spark compute, and noisy checks create alert fatigue. These checks increase the cost of Snowflake/Databricks queries. There might be an additional maintenance burden aggregated by schema changes, late-arriving data, evolving accepted values, or upstream source changes.

Where do data teams get stuck with open source data quality tools?


Here are some common situations:

  • Overfitting to the current schema makes every change painful.
  • Alert fatigue occurs because thresholds and severities are not tuned.
  • Lack of ownership with hundreds of tests and no clear owners.
  • Siloed implementations per team (analytics vs. ML vs. ops) without central standards.

What core capabilities do open source data quality tools cover?


  • Data profiling, validation against rules, anomaly detection, lineage context, and continuous monitoring/alerts.

When should teams choose open source vs. buy?


  • Open source fits teams prioritizing flexibility, code-first workflows, and lower software cost; consider total effort for maintenance and scale.
  • “Build vs. buy” often hinges on time-to-market, breadth/depth of checks, and ongoing maintenance burden noted by many teams evaluating DQ stacks. Internal doc (access required).

What are the 7 C’s of data quality?


The 7 C’s of data quality include Completeness, Consistency, Accuracy, Timeliness, Uniqueness, Validity, and Relevance. These principles help organizations assess and improve the quality of their data.

How does Atlan work with open-source data quality?


  • Atlan integrates with several DQ/observability tools (e.g., Great Expectations, Monte Carlo, Soda) and surfaces quality metrics and alerts on asset pages via metadata ingestion/APIs.
  • Atlan Data Quality Studio runs checks natively in your warehouse (Snowflake DMFs; Databricks) with a push-down model, and centralizes results in Atlan for trust signals/alerts.
  • Today, Atlan’s DQ checks execute in-warehouse (not at diverse upstream sources); for cross-system edge checks, customers often pair Atlan with open-source/partner tools and surface results in the catalog. Internal docs (access required).

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