SelectZero now ingests metadata from both dbt Core and dbt Cloud. Your dbt models, sources, seeds, snapshots, tests, and lineage flow into the SelectZero catalog and merge with metadata from every other system you connect – your BI tools, ETL jobs, operational databases, and warehouses. dbt teams get a single place to see how their models are actually used downstream. Data teams outside dbt get visibility into what dbt is doing upstream. Everyone gets one catalog, one lineage graph, one source of truth.

The problem dbt users keep running into

dbt is excellent at what it does. You version your transformations, test them, document them, and ship them with confidence. The dbt docs site gives you a clean view of your project: models, sources, tests, lineage between them.

But your dbt project is not the whole picture. It is a slice in the middle.

Upstream, data arrives from operational databases, SaaS systems, files, message queues, and CDC pipelines. Downstream, your models feed dashboards, reports, reverse-ETL jobs, ML pipelines, and exports to other teams. dbt sees the part it owns. It does not see where the source tables actually come from, and it does not see where the marts actually end up.

This creates familiar pain:

• An analyst changes a column in a core model and breaks a Power BI report nobody linked to the dbt project.
• A platform team deprecates a source table without realising six dbt models depend on it.
• Compliance asks “where does this PII column end up” and dbt docs can answer for the warehouse but not for the BI layer or the export jobs.
• New engineers spend weeks learning which dbt models are actually important versus abandoned.

dbt docs solve the in-project problem. SelectZero solves the cross-system problem.

What the integration actually does

SelectZero connects to your dbt project and ingests the standard dbt artifacts: manifest.json, run_results.json, and catalog.json. For dbt Cloud, we pull these from the Cloud Admin API and enrich them with metadata from the Discovery (GraphQL) API, using a service token. For dbt Core, we read the same three artifacts from S3 or any S3-compatible object store – point your CI job at a bucket and we’ll pick them up from there.

From those artifacts we extract:

• Models, sources, seeds, and snapshots as first-class catalog objects, tagged with their dbt project, schema, and materialization.
• Column-level lineage linking each dbt model to the warehouse table it materialises and through to its upstream sources and downstream consumers, so a column in a mart connects back through staging to the raw source it came from.
• Tests registered as SelectZero test cases, with the latest pass/fail outcomes pulled from run_results.json so dbt test history shows up next to the rest of your data-quality runs.
• Source freshness captured from the manifest and attached to the source.
• Descriptions, tags, and meta fields from your schema.yml files (merged with column comments from catalog.json), so the documentation you already wrote shows up in the catalog without duplication.

The result is that every dbt object appears in the SelectZero catalog with a dbt badge, and its lineage connects on both sides: backwards into the warehouse tables and operational sources that feed it, forwards into the BI dashboards, reports, and downstream consumers that depend on it.

The built-in dbt Explorer

Alongside the unified catalog view, SelectZero ships a dedicated dbt explorer: a tree of every model, source, seed, snapshot, and test in your project, grouped by type, with a detail panel that shows columns, dependencies, the compiled metadata, and the latest run status for whatever you click. If your team already lives in dbt docs, the explorer gives them something they recognise – but with everything else SelectZero knows about that object one click away.

Concrete value by audience

For dbt developers

You stop guessing at downstream impact. Before you change a model, you can see every Power BI report, Tableau dashboard, Looker explore, reverse-ETL sync, and ad-hoc consumer that touches the columns you are about to modify. dbt’s –defer and state comparison tell you what changed inside the project. SelectZero tells you who outside the project will feel it.

You also get a public surface for your work. The descriptions and tests you write in schema.yml are visible to business users who would never open a dbt docs site – they search the catalog, find your model, and trust it because the tests next to it are green.

For data platform and governance teams

dbt becomes a transparent part of the estate instead of a black box that produces tables. You can answer “where does customer email show up” in one query, across operational DBs, dbt models, marts, BI tools, and exports. PII tags follow dbt lineage automatically. Access reviews, retention policies, and data contracts apply uniformly whether a column lives in a source table, a dbt staging model, or a downstream report.

For BI and analyst teams

When a dashboard breaks or a number looks wrong, you trace it backwards through SelectZero’s lineage into the exact dbt model and the exact source table, with the latest test results and freshness right there. No more “ask the dbt team” tickets to find out why a metric shifted.

For leadership and compliance

One catalog covers the whole estate. Audit questions about data flow, ownership, and quality get answered from a single system instead of stitched together from dbt docs, warehouse metadata, BI tool catalogs, and tribal knowledge.

dbt test results land where the rest of your DQ lives

Every dbt test we find in the manifest is registered as a SelectZero test case against the column or model it covers. Each time we sync run_results.json we attach the latest outcomes as test-case executions, so dbt tests live in the same execution history as every other SelectZero check. Failed runs and stale models surface immediately in the catalog, not after someone notices a broken dashboard.

Row-level issues are immediately available to users with the right privileges.

dbt Core and dbt Cloud, same experience

We support both.

• dbt Cloud users provide an account ID and a service token. SelectZero pulls artifacts from the Admin API and enriches the model graph through the Discovery (GraphQL) API. Access and Discovery URLs are configurable, so regional dbt Cloud instances work out of the box.
• dbt Core users point us at an S3 or S3-compatible bucket (with an endpoint override for MinIO, R2, GCS-with-HMAC, etc.) where your CI uploads manifest.json, run_results.json, and catalog.json. No agent, no local file path, no extra service to host.

The catalog experience is identical from there. If you run a mixed setup – multiple dbt projects across Core and Cloud – they all land in the same SelectZero catalog and lineage graph.

Getting started

1. Add your dbt project in Settings → dbt. Pick dbt Cloud (account ID + service token) or Artifacts (S3 bucket + credentials), and link the warehouse connection it builds against.
2. Connect at least one upstream source (your warehouse or operational DB) and one downstream consumer (BI tool, reverse-ETL, or export job). The value of cross-system lineage scales with what is connected.
3. Open the catalog or the dbt explorer. Your dbt models are there, with tests, freshness, descriptions, and full upstream/downstream lineage.

That is the whole setup. No changes to your dbt project, no new YAML, no required tags or conventions. SelectZero reads what dbt already produces.

Closing

dbt gave the industry a great pattern for transformation. SelectZero extends that pattern across the rest of the estate. For teams running dbt who care what happens to data before it arrives and after it leaves, the integration is built for exactly that.

SelectZero now ships a built-in Model Context Protocol (MCP) server. Point Claude Desktop, Cursor, Claude Code, VS Code, or any other MCP-aware client at your SelectZero instance, and your AI assistant gets first-class access to your catalog, glossary, lineage, tests, and audit history – scoped to whatever the user behind the token is allowed to see. Twelve read-only tools and three guided “recipes” mean an analyst, engineer, or steward can have a conversation with their data stack instead of clicking through screens.

The problem

Every team is now running AI clients next to their work. Engineers live in Cursor and Claude Code. Analysts open Claude Desktop. PMs paste schemas into ChatGPT. The pattern is the same everywhere: ask a question, hope the model has enough context.

The catch is that none of these clients know anything about your data estate. They cannot tell the engineer that the column they are about to drop feeds three Power BI dashboards. They cannot tell the analyst that “customer_email” is tagged as PII and lives in eleven tables. They cannot tell the on-call engineer that the failing test was added two weeks ago by someone who has since left the company.

You end up either:

• Pasting fragments of context into prompts and hoping for the best.
• Asking the AI questions it has no way to answer, and getting confident-sounding fabrications back.
• Switching out of your AI client into a separate catalog UI, doing the lookup by hand, switching back. The exact manual work the AI was supposed to eliminate.

MCP fixes this. SelectZero now speaks it.

What the integration actually does

SelectZero embeds an MCP server directly in the Spring Boot backend. It is exposed over streamable HTTP at /api/v1/mcp, switched on from the settings page, and authenticated with Personal Access Tokens (PATs) – user-bound tokens each person generates in their own profile.

A PAT carries the user’s role. Every MCP tool call runs through the same privilege and per-connection access checks as the rest of SelectZero – if a user cannot see a connection in the UI, the MCP server will not return its objects, columns, lineage, or test cases either. The same license tier rules apply. There is no separate permissions surface to keep in sync.

The server exposes twelve read-only tools, grouped into three areas:

Catalog and metadata

• searchCatalogObjects – filtered, paginated search across tables and views, with column counts, row counts, test coverage, profiling status, and any custom fields you have configured.
• getCatalogColumns – column-level drilldown: data types, nullability, PK status, null percentage, unique count, linked glossary terms, per-column custom fields.
• getColumnLineage – one-hop column-level upstream/downstream, across connections.
• getObjectDependencies – multi-hop object lineage (1–10 hops), with direction and relation type, deduplicated to object level so downstream analysis stays readable.
• searchGlossaryTerms – search the business glossary by name or description; returns term hierarchy, linked columns, and custom field values.
• discoverCustomFields – list every custom field configured in SelectZero, by entity type, with allowed option values inline. Let the AI client adapt to your taxonomy on the fly.

Quality and audit

• searchTestCases – find data-quality checks by description, connection, target object, suite, severity, or custom field.
• getExecutionHistory – execution timeline for a specific test case, with date range and status filters.
• getAuditHistory – version history for any entity (catalog object, term, test case, column) – what changed, who changed it, when.
• getAuditSnapshot – the full JSON state of an entity at a specific audit event, so the AI can diff versions for you.

Suggestions

• suggestProfiling – deterministic, rule-based profiling suggestions derived from existing profiling history (uniqueness, value sets, patterns, bounds).
• suggestTestCases – AI-backed test suggestions, optionally scoped to columns plus a free-text hint. This is the one tool that calls out to your configured LLM (OpenAI, Azure, or Ollama) – it composes the prompt from real column schema, optional profiling examples, and optional glossary terms, and returns structured test-case suggestions. Off by default; turn it on per deployment.

Every tool is read-only. The MCP server cannot create, edit, or delete anything in SelectZero. Mutations stay in the UI and the REST API where they get the full audit treatment.

Recipes: guiding less-capable clients through multi-step workflows

Calling one tool is easy. The interesting questions need three or four tools chained together – and not every AI client is great at deciding which to call next. SelectZero ships three curated recipes, exposed as MCP Resources that any client can pull on demand:

• Root-cause analysis – start from a failing test, walk through execution history, audit history, audit snapshots, and upstream dependencies to find the change that broke things.
• Coverage gaps – discover custom fields, find catalog objects in a given domain, identify which have no tests, and propose profiling for the gaps.
• Impact analysis – given an object, find its downstream dependencies and surface the tests on each, so you know what to verify before a change.

These are not hardcoded workflows. They are markdown documents the AI reads, decides whether to apply, and adapts to your specific question. Think of them as institutional memory for how SelectZero is best used, shipped alongside the tools themselves.

Concrete use cases

Engineer about to change a column (in Cursor or Claude Code)

“I’m about to rename customer.email to customer.email_address in the orders schema. What breaks?”

The agent calls searchCatalogObjects to locate the table, getColumnLineage to find downstream columns, getObjectDependencies for the dashboards and reports that read from it, and searchTestCases for the checks it would invalidate. You get a precise impact summary in your IDE without leaving your IDE.

Analyst chasing a number (in Claude Desktop)

“The weekly revenue dashboard dropped 12% – start with mart_revenue_weekly and tell me what changed in the last seven days.”

The agent walks the root-cause recipe: latest run on tests covering that table, audit history on the model, audit snapshots to diff the SQL, upstream lineage to find a source that started returning fewer rows. End-to-end investigation, conversational.

Data steward auditing coverage

“List every table in the customer_360 domain that has no critical test, and propose what to add.”

The agent uses discoverCustomFields to find the domain field, searchCatalogObjects to filter, searchTestCases to identify gaps, and suggestProfiling (or suggestTestCases if copilot is enabled) to propose checks. A coverage audit that used to be a multi-day task becomes an afternoon.

Compliance and governance

“Show every column tagged PII across our warehouses, who owns each table, and whether each has an active test.”

discoverCustomFields → searchCatalogObjects filtered by PII tag → getCatalogColumns per match → searchTestCases per object. A single conversation produces an evidence pack instead of a multi-day spreadsheet exercise.

New team member onboarding

“Tell me everything you know about the orders table – columns, glossary terms, upstream sources, downstream consumers, current tests, recent changes.”

One question, six tools, a complete picture. The conversation transcript itself becomes onboarding documentation.

On-call during an incident

“Tests on fct_payments started failing 40 minutes ago. Walk it back.”

getExecutionHistory for the failures, getAuditHistory on the table and its upstream dbt model, getAuditSnapshot to diff the change, getObjectDependencies to scope the blast radius. The investigation runs at conversation speed.

Setup

1. In Settings → General → Modules, toggle the MCP server on. If you want AI-backed test suggestions (suggestTestCases), enable the Copilot MCP toggle as well – it requires Copilot to be configured under AI settings.
2. Have each user generate a Personal Access Token in their profile.
3. Point your MCP client at https://<your-selectzero>/api/v1/mcp with the PAT in the auth header.

That is the whole setup on our side. Whatever your AI client of choice – Claude Desktop, Claude Code, Cursor, VS Code, any MCP-aware tool – will pick up the twelve tools and three recipes automatically and start using them.

What is not in scope yet

• Read-only. Creating a test case, editing a glossary term, or tagging a column still happens in the UI. Write functionality is a future roadmap item; we want the read surface to be rock-solid first.
• No streaming tool calls. Synchronous request/response; long-running operations are not exposed yet.
• Per-tool audit logging is not separately recorded. Permissions are enforced at every call, but the existing audit log captures mutations only.

Closing

MCP turns your data catalog from a place people visit into a service every AI client can use. SelectZero already has the metadata, lineage, tests, glossary, and history – the MCP server just makes all of it available to the assistant your team is already talking to.

If your engineers, analysts, and stewards are spending half their day in an AI client, they should be talking to your catalog, not around it.

Documentation – https://selectzero.com/docs/mcp-setup.html

SelectZero 2026.5 brings the platform’s biggest expansion yet into the modern data stack, with native dbt integration, AI-powered catalog enrichment, and MCP support that opens SelectZero data to AI assistants. This release also introduces a centralised alerts view, organisation-wide guest access for broader collaboration, customisable table layouts across the platform, and a range of connectivity and lineage improvements that make daily work faster and more transparent.

SelectZero now natively integrates with dbt, ingesting manifest, catalog, and run artifacts to give teams a live, always-current view of their dbt project. A new dbt Model Catalog page lets anyone browse models, sources, seeds, and snapshots without touching the CLI or static docs. Lineage nodes show a dbt badge where a matching dbt model exists, and historical run storage keeps a full timeline of freshness, failures, and schema changes so teams can answer not just “is it working now” but “when did it break.”

Read more about SelectZero dbt integration here.

SelectZero data is now exposed as Model Context Protocol resources, making catalog metadata, lineage, test cases, and data quality results directly accessible to AI assistants and copilots. This unlocks a new class of LLM-powered workflows on top of SelectZero, from natural language queries over your catalog to automated impact analysis through your assistant of choice.

Read more about SelectZero MCP here.

Catalog object and attribute descriptions can now be generated by AI, either individually or as a mass action across multiple tables and attributes at once. Suggestions take metadata, profiling values, and related glossary terms as context, and every suggestion is shown in a preview where users can pick exactly what to apply.

A new alerts view brings every enabled alert across the platform into one place, with an admin perspective spanning all users and a personal view for end users. No more checking each test case, object, or term individually to understand what is currently being monitored and by whom.

A new user type makes it easy to give broader, view-only access across the organisation without full user provisioning. Perfect for stakeholders, auditors, or wider business teams who need visibility into data catalog and quality without becoming managed users.

Catalog page tables now supports column customisation. Choose which columns to display, reorder them via drag-and-drop, and reset to defaults at any time. Layouts persist locally, so teams can tailor each view to how they actually work.

Webhook notifications can now include a direct link to the test result report, opening either as a downloadable Excel file or as an in-browser HTML page with authentication checks in place. Teams using Slack, Teams, or Jira can jump straight from a notification to the full report, with no extra steps. The webhook configuration view has also been rebuilt with copyable parameters and ready-made samples for popular destinations.

Procedure parsing has been extended to SAP IQ, joining the existing TSQL support. Teams running SAP IQ data warehouses can now extract statements from stored procedures for lineage and validation, with no manual workarounds required.

Linked entities on business terms are now real navigable links. Clicking a linked asset, attribute, or rule takes users straight to the entity, making it faster to move between the glossary and the rest of the platform.

Lineage evidence SQLs now show when each relation was first detected, giving full transparency into the origin of every lineage edge. Stale lineage from old DDL is no longer permanent: users with manage rights can delete individual query references one by one.

OAuth providers now support a configurable default display name field, matching the existing LDAP and Active Directory experience. The login screen layout has also been refined for a cleaner sign-in flow.

Building a shared view of data quality across the public sector

Case study with Estonia’s Ministry of Education and Research

THE CUSTOMER

The Ministry of Education and Research plans national policies for education, research, youth, and language. It oversees all levels of education, from pre-primary through higher education, as well as hobby and adult learning, research and development, youth work, and the drafting of related legislation. The Ministry strategically manages several state agencies, foundations, and institutions by setting their goals, analyzing results, determining budgets, and providing supervision.

CHALLENGES

Prior to this initiative, the Estonian Ministry of Education and Research did not have a real time, consolidated view of data quality across its datasets, nor a single, centralized place for defining and managing data quality requirements. As a result, data validation was handled almost exclusively by analysts, placing the full responsibility for data quality on a limited group of specialists.

This operating model led to the increase of assumptions and differing interpretations of data and its quality. Data quality was frequently discussed and presumed, but validating or disproving these claims required significant time and manual effort. At the same time, there was a clear perception that data stakeholders lacked a shared understanding of the true state of the data. Teams expressed a need for faster access to reliable answers, reduced coordination overhead, and more consistent data checks across domains.

IMPACT

The Ministry’s data strategy aims to enable precise, evidence-based decision-making. A key prerequisite for this is the easy accessibility of necessary data and verified data quality. Using the platform allows the Ministry to conduct regular data quality checks, provide agreed-upon updates on key data quality aspects to relevant stakeholders, reduce decisions based on gut feeling or hearsay, and build greater confidence in existing data. In particular, the ability to assemble test suites is considered especially valuable.

Early results already show increased transparency and a more accurate picture of the available data, and as the Ministry continues to scale the platform internally, even greater benefits are expected.

In the long term, the Ministry aims to engage data providers more quickly and regularly than before, track compliance with data updates more easily, and monitor the impact of interventions. Increasingly, analysts, policymakers, and data support teams are seeing tangible benefits from using the software.

WHY SELECTZERO?

“Its flexibility and alignment with the Ministry’s data strategy make it a practical solution for evidence-based decision making and improved trust in data“

– Katre Seema, Head of Data Management at Estonia’s Ministry of Education and Research

Ensuring accurate and consistent master data across retail systems
Case study with Rimi

THE CUSTOMER

Rimi Baltic is one of the largest retailers in the Baltic States, operating since 2004. The company manages four store formats: Rimi Hyper, Rimi Super, Rimi Mini and Rimi Express, across more than 300 locations, offering extensive opportunities for growth and development. Rimi Baltic employs over 11,000 people throughout the region and is part of the Salling Group, Denmark’s largest retail organization.

CHALLENGES

Prior to implementing SelectZero, Rimi Baltic faced fragmented and manual data quality management practices. The company lacked a structured and centralized solution for defining and monitoring data quality across multiple data sources and business units. This made it difficult to ensure consistency and accuracy on a regular basis.

Inaccurate master data, particularly product-related, had a direct impact on the supply chain, store operations, e-commerce, finance, and overall customer experience. Incorrect product labeling and tax data also introduced compliance risks, while manual validation processes proved inefficient, time-consuming, and prone to human error.

This in turn affected Rimi’s business operations in numerous ways. The lack of automation meant an inefficient use of resources, with manual validations consuming significant time and requiring dedicated full-time employees. Limited automation capabilities also made it difficult to perform efficient data health checks. Over time, these issues contributed to an erosion of trust in data, resulting in reactive decision-making and negatively affecting regular reporting and analytics.

WHY SELECTZERO?

Since 2023 Rimi Baltic has evolved from piloting SelectZero’s solution to becoming a full customer. Recognizing SelectZero as a promising and adaptable platform, Rimi’s feedback played a key role in refining the solution to better meet the needs of the retail company.

By replacing time-consuming manual checks with automated processes, SelectZero has enabled Rimi Baltic to operate more efficiently, make decisions with greater confidence, and minimize business and compliance risks. Its flexible rule definition, scheduling, and automation capabilities enable the company to maintain consistent and accurate product information, prevent compliance penalties, and reallocate resources to higher-value activities, all while driving continuous improvement in data governance practices.

“SelectZero’s data quality improvement tool is user-friendly and intuitive, helping to streamline our approach to data management. The vendor’s clear communication helped us explore data challenges from new angles, making collaboration smooth and insightful.”

– Andris Vedigs, Innovation Lead, Rimi

Gartner has published its latest Market Guide for Data Observability Tools, and SelectZero is proud to be recognized once again as a Representative Vendor.

We are proud to announce that SelectZero has been recognized as a Representative Vendor in the Gartner Market Guide for Data Observability Tools. This recognition reflects the growing importance of data observability as a core capability for modern organizations, and validates the work our team has put into building a platform that makes data truly reliable.

Why data observability matters

The stakes around data quality have never been higher. Poor data quality costs businesses an estimated $3.1 trillion annually in the US alone, and the average organization spends around 15 hours resolving a single data quality incident. At the same time, AI initiatives, regulatory requirements like BCBS239, DORA, and GDPR, and increasingly data-driven decision-making mean that unreliable data is no longer just a technical headache, it’s a business risk.

Data Observability gives organizations the visibility they need to detect, diagnose, and prevent data issues before they cause damage.

What SelectZero does

SelectZero is a data observability platform built to turn data from liability into reliability. Our platform covers the full spectrum of observability needs: automated data validation with real-time alerts, column-level data profiling with ML-based anomaly detection, end-to-end data lineage, a quality-driven data catalog, and a business glossary that bridges the gap between technical teams and business stakeholders.

We also believe that good tooling should be accessible to the whole data team, not just engineers. SelectZero is built for data and ETL engineers, BI analysts, and business stakeholders alike.

What this recognition means to us

Being included in the Gartner Market Guide sits alongside recognition from customers such as SEB, Citadele Bank, and Telia, who have trusted SelectZero to help them manage and improve data quality at scale.

We see this as motivation to keep building. If your organization is evaluating data observability tools, we’d be glad to demonstrate what SelectZero can do. Book a demo or start a free 14-day trial here, or reach out to our team directly.

SelectZero 2026.3 is here, bringing a range of new features and improvements across the platform. This release focuses on deeper data quality insights, broader connectivity, and a more streamlined user experience. From row-level execution diffs and percentage-based test cases to AWS S3 integration and enhanced admin tooling, there is something for every team working to raise the bar on data quality.

Users can now see the differences between previous and new executions directly within the execution history. Each run is compared against the previous one, clearly showing which invalid rows are newly introduced, which have been resolved, and which remain active. This makes it easier than ever to track data quality progress over time and stay on top of recurring issues.

Note: Test case result CSV row limit has been reduced to a maximum of 10000 rows.

Users can now choose a new test case type that allows adding a separate SQL query for the total row count alongside the usual invalid row count query. When executed, SelectZero automatically calculates the percentage result, making it straightforward to define and evaluate proportion-based data quality rules without complex single-query workarounds. The invalid row count, total row count, and calculated percentage are all displayed in the execution results for full transparency.

It is now possible to see all upstream and downstream dependencies for any object, enabling simpler column-level impact analysis and root cause investigation. Derived from column-level lineage, this gives users a clear picture of what is affected when a data quality issue is discovered, as well as what upstream sources may be responsible for a failing test case.

SelectZero now supports connecting to AWS S3 via Amazon Athena, allowing users to query files stored in S3 buckets directly within the platform. This opens up data quality management for teams working with cloud-based file storage as part of their data infrastructure.

Links stored in custom fields are now rendered as clickable, taking users directly to the relevant external resource. This makes custom fields more useful for teams who reference external documentation, tickets, or tools as part of their workflow.

Admins can now view different log types directly within the platform without needing to download them first. The logs are accessible through the Logs modal, streamlining troubleshooting and reducing the steps needed to inspect platform activity.

The new version of SelectZero is packed with new features and improvements designed to make data quality management faster, smarter, and more flexible. This release introduces Standard validations functionality for quick no-code test creation, a drag-and-drop mapping interface for data reconciliation, and enhanced alerting at the test case level. Teams can now track full change history on test cases, export reports as Excel files, and receive richer webhook notifications with test case results. On the integrations side, Power BI on-premises (PowerBI Report Server) and SAS are now supported, and the Glossary has been updated to handle duplicate terms across different contexts.

Setting up data quality validations just got a lot faster. The new Standard validations functionality lets you pick from a set of predefined validation types, including column completeness, allowed values, numeric ranges, regex checks, date/time checks, duplicate checks, data availability, and data freshness. Simply fill in the required details and you are good to go. No SQL writing required. Standard validations can also be created directly from the Catalog page when opening the Test cases section for any Object.

Creating data reconciliation tests is now more intuitive with a new drag-and-drop mapping interface. Select your source and target objects, then simply drag columns onto each other to define the mapping. WHERE filters can be added as needed, and the SQL is automatically generated from your mapping. Existing SQL is also parsed back into the visual mapping view when reopening a test case.

You can now track all changes made to a test case over time with the new change history feature. It provides a full audit trail of edits, making it easy to see what was changed, when, and by whom. This helps teams maintain accountability and transparency across their data quality test cases.

Alerts can now be configured directly on individual test cases, giving you more granular control over notifications. You can choose to receive alerts based on test case execution results, triggered by a schedule or API, as well as alerts when changes are made to the test case itself.

In addition to the existing HTML reports, test suite reports can now also be generated and sent by email as Excel files. The file includes a summary sheet with all test cases and their statuses, plus individual sheets for each test case with detailed results where available.

Webhook alerts (e.g. Slack, Teams, Jira) can now include a list of test cases and their results. This gives receiving systems and team channels richer context about what passed or failed, without needing to open the full report.

The Glossary now supports duplicate term names across different Glossaries, giving teams the flexibility to manage terms that share a name but carry different meanings in different contexts.

The Power BI connector now supports on-premises Power BI Report Server, in addition to the existing Power BI cloud service. Organizations running Power BI on-premises can now connect and monitor their reports directly, including field-level lineage.

SelectZero now connects to SAS via the IOM connector, allowing to run data quality tests directly against SAS 9.4 and Workspace Server environments. The SAS connector is available alongside the existing suite of database and platform integrations.

The Glossary term detail view has been redesigned with dedicated tabs for Graph, Relations, and Assets, making it easier to explore and manage term context. The legacy view has been unified with the Catalog layout, enabling a side-by-side experience with configurable panel widths that are saved per user for a more personalized workflow.

The platform now keeps a full change history for individual Glossary terms, catalog objects, and attributes. All updates are retained, making it easy to review what changed, when, and by whom, and to revert unwanted changes when needed.

Users can configure automatic notifications for changes to specific Glossary terms or Catalog objects, such as schema drift. Alerts are delivered by email and include direct links to the affected entities, making it easy to review changes as they happen.

Test cases can now evaluate results against a percentage threshold instead of a strict pass or fail. The measured percentage is propagated across the platform and reflected in data quality metrics and dashboards. For example, a validation that checks whether at least 90% of rows meet a rule will display the actual percentage result in dashboard visualizations.

Individual Test cases can now be added directly to Dashboard tiles, making it possible to build custom, validation-based data quality metrics and monitor specific Test cases.

Test case SQL can now be viewed in full screen using a dedicated button in the SQL editor, making complex validations easier to read and edit. In addition, users can define custom row-level result queries by using subqueries in the SQL projection, enabling more flexible and advanced Test case logic (see documentation for details).

New Custom Field types are now available, allowing values to be selected from existing Glossary terms, entered as dates, or captured using larger text areas. This enables richer metadata and more structured documentation across the platform.

Custom fields can now be assigned to Business rules, allowing teams to capture additional metadata and standardize documentation alongside existing rule definitions.