Sep 14, 2025·8 min
How to Build a Web App for Data Quality Checks and Alerts
Learn how to plan and build a web app that runs data quality checks, tracks results, and sends timely alerts with clear ownership, logs, and dashboards.
Learn how to plan and build a web app that runs data quality checks, tracks results, and sends timely alerts with clear ownership, logs, and dashboards.
Before you build anything, align on what your team actually means by “data quality.” A web app for data quality monitoring is only useful if everyone agrees on the outcomes it should protect and the decisions it should support.
Most teams blend several dimensions. Pick the ones that matter, define them in plain language, and treat those definitions as product requirements:
These definitions become the foundation for your data validation rules and help you decide which data quality checks your app must support.
List the risks of bad data and who is impacted. For example:
This prevents you from building a tool that tracks “interesting” metrics but misses what actually hurts the business. It also shapes web app alerts: the right message should reach the right owner.
Clarify whether you need:
Be explicit about latency expectations (minutes vs. hours). That decision affects scheduling, storage, and alert urgency.
Define how you’ll measure “better” once the app is live:
These metrics keep your data observability efforts focused and help you prioritize checks, including anomaly detection basics versus simple rule-based validation.
Before you build checks, get a clear picture of what data you have, where it lives, and who can fix it when something breaks. A lightweight inventory now saves weeks of confusion later.
List every place data originates or is transformed:
For each source, capture an owner (person or team), a Slack/email contact, and an expected refresh cadence. If ownership is unclear, alerting will be unclear too.
Pick critical tables/fields and document what depends on them:
A simple dependency note like “orders.status → revenue dashboard” is enough to start.
Prioritize based on impact and likelihood:
These become your initial monitoring scope and your first set of success metrics.
Document specific failures you’ve already felt: silent pipeline failures, slow detection, missing context in alerts, and unclear ownership. Turn these into concrete requirements for later sections (alert routing, audit logs, investigation views). If you maintain a short internal page (e.g., /docs/data-owners), link it from the app so responders can act quickly.
Before you design screens or write code, decide which checks your product will execute. This choice shapes everything else: your rule editor, scheduling, performance, and how actionable your alerts can be.
Most teams get immediate value from a core set of check types:
email.”order_total must be between 0 and 10,000.”order.customer_id exists in customers.id.”user_id is unique per day.”Keep the initial catalog opinionated. You can add niche checks later without making the UI confusing.
You typically have three options:
A practical approach is “UI first, escape hatch second”: provide templates and UI rules for 80%, and allow custom SQL for the rest.
Make severity meaningful and consistent:
Be explicit about triggers: single-run failure vs. “N failures in a row,” thresholds based on percentages, and optional suppression windows.
If you support SQL/scripts, decide upfront: allowed connections, timeouts, read-only access, parameterized queries, and how results are normalized into pass/fail + metrics. This keeps flexibility while protecting your data and your platform.
A data quality app succeeds or fails on how quickly someone can answer three questions: what failed, why it matters, and who owns it. If users have to dig through logs or decipher cryptic rule names, they’ll ignore alerts and stop trusting the tool.
Start with a small set of screens that support the lifecycle end-to-end:
Make the main flow obvious and repeatable:
create check → schedule/run → view result → investigate → resolve → learn.
“Investigate” should be a first-class action. From a failed run, users should jump to the dataset, see the failing metric/value, compare with previous runs, and capture notes on the cause. “Learn” is where you encourage improvements: suggest adjusting thresholds, adding a companion check, or linking the failure to a known incident.
Keep roles minimal at first:
Every failed result page should show:
A data quality app is easier to scale (and easier to debug) when you separate four concerns: what users see (UI), how they change things (API), how checks run (workers), and where facts are stored (storage). This keeps the “control plane” (configs and decisions) distinct from the “data plane” (executing checks and recording outcomes).
Start with one screen that answers, “What’s broken and who owns it?” A simple dashboard with filters goes a long way:
From each row, users should drill into a run details page: check definition, sample failures, and last known good run.
Design the API around the objects your app manages:
Keep writes small and validated; return IDs and timestamps so the UI can poll and stay responsive.
Checks should run outside the web server. Use a scheduler to enqueue jobs (cron-like) plus an on-demand trigger from the UI. Workers then:
This design lets you add concurrency limits per dataset and retry safely.
Use distinct storage for:
This separation keeps dashboards fast while preserving detailed evidence when something fails.
If you want to ship an MVP quickly, a vibe-coding platform like Koder.ai can help you bootstrap the React dashboard, Go API, and PostgreSQL schema from a written spec (checks, runs, alerts, RBAC) via chat. It’s useful for getting the core CRUD flows and screens in place fast, then iterating on the check engine and integrations. Because Koder.ai supports source code export, you can still own and harden the resulting system in your repo.
A good data quality app feels simple on the surface because the data model underneath is disciplined. Your goal is to make every result explainable: what ran, against which dataset, with which parameters, and what changed over time.
Start with a small set of first-class objects:
Keep raw result details (sample failing rows, offending columns, query output snippet) for investigation, but also persist summary metrics optimized for dashboards and trends. This split keeps charts fast without losing debugging context.
Never overwrite a CheckRun. Append-only history enables audits (“what did we know on Tuesday?”) and debugging (“did the rule change or the data change?”). Track check version/config hash alongside each run.
Add tags like team, domain, and a PII flag on Datasets and Checks. Tags power filters in dashboards and also support permission rules (e.g., only certain roles can view raw failing-row samples for PII-tagged datasets).
The execution engine is the “runtime” of your data quality monitoring app: it decides when a check runs, how it runs safely, and what gets recorded so results are trustworthy and repeatable.
Start with a scheduler that triggers check runs on a cadence (cron-like). The scheduler shouldn’t run heavy work itself—its job is to enqueue tasks.
A queue (backed by your DB or a message broker) lets you:
Checks often execute queries against production databases or warehouses. Put guardrails in place so a misconfigured check can’t degrade performance:
Also capture “in-progress” states and ensure workers can safely pick up abandoned jobs after crashes.
A pass/fail without context is hard to trust. Store run context alongside every result:
This is what enables you to answer: “What exactly ran?” weeks later.
Before activating a check, offer:
These features reduce surprises and keep alerting credible from day one.
Alerting is where data quality monitoring either earns trust or gets ignored. The goal isn’t “tell me everything that’s wrong”—it’s “tell me what to do next, and how urgent it is.” Make every alert answer three questions: what broke, how bad, and who owns it.
Different checks need different triggers. Support a few practical patterns that cover most teams:
Make these conditions configurable per check, and show a preview (“this would have triggered 5 times last month”) so users can tune sensitivity.
Repeated alerts for the same incident train people to mute notifications. Add:
Also track state transitions: alert on new failures, and optionally notify on recovery.
Routing should be data-driven: by dataset owner, team, severity, or tags (e.g., finance, customer-facing). This routing logic belongs in configuration, not code.
Email and Slack cover most workflows and are easy to adopt. Design the alert payload so a future webhook is straightforward. For deeper triage, link directly to the investigation view (for example: /checks/{id}/runs/{runId}).
A dashboard is where data quality monitoring becomes usable. The goal isn’t pretty charts—it’s letting someone answer two questions quickly: “Is anything broken?” and “What do I do next?”
Start with a compact “health” view that loads fast and highlights what needs attention.
Show:
This first screen should feel like an operations console: clear status, minimal clicks, and consistent labels across all data quality checks.
From any failed check, provide a detail view that supports investigation without forcing people to leave the app.
Include:
If you can, add a one-click “Open investigation” panel with links (relative only) to the runbook and queries, e.g. /runbooks/customer-freshness and /queries/customer_freshness_debug.
Failures are obvious; slow degradation isn’t. Add a trends tab for each dataset and each check:
These graphs make anomaly detection basics practical: people can see whether this was a one-off or a pattern.
Every chart and table should link back to the underlying run history and audit logs. Provide a “View run” link for each point so teams can compare inputs, thresholds, and alert routing decisions. That traceability builds trust in your dashboard for data observability and ETL data quality workflows.
Security decisions made early will either keep your app simple to operate—or create constant risk and rework. A data quality tool touches production systems, credentials, and sometimes regulated data, so treat it like an internal admin product from day one.
If your organization already uses SSO, support OAuth/SAML as soon as practical. Until then, email/password can be acceptable for an MVP, but only with the basics: salted password hashing, rate limiting, account lockout, and MFA support.
Even with SSO, keep an emergency “break-glass” admin account stored securely for outages. Document the process and restrict its use.
Separate “viewing results” from “changing behavior.” A common set of roles:
Enforce permissions on the API, not just the UI. Also consider workspace/project scoping so a team can’t accidentally edit another team’s checks.
Avoid storing raw row samples that may contain PII. Store aggregates and summaries instead (counts, null rates, min/max, histogram buckets, failing row count). If you must store samples for debugging, make it an explicit opt-in with short retention, masking/redaction, and strict access controls.
Keep audit logs for: login events, check edits, alert-route changes, and secret updates. An audit trail reduces guesswork when something changes and helps with compliance.
Database credentials and API keys should never live in plaintext in your database. Use a vault or environment-based secret injection, and design for rotation (multiple active versions, last-rotated timestamps, and a test-connection flow). Limit secret visibility to admins, and log access without logging the secret value.
Before you trust your app to catch data problems, prove it can reliably detect failures, avoid false alarms, and recover cleanly. Treat testing as a product feature: it protects your users from noisy alerts and protects you from silent gaps.
For every check you support (freshness, row count, schema, null rates, custom SQL, etc.), create sample datasets and golden test cases: one that should pass and several that should fail in specific ways. Keep them small, version-controlled, and repeatable.
A good golden test answers: What’s the expected result? What evidence should the UI show? What should be written to the audit log?
Alerting bugs are often more damaging than check bugs. Test alert logic for thresholds, cooldowns, and routing rules:
Add monitoring for your own system so you can spot when the monitor is failing:
Write a clear troubleshooting page covering common failures (stuck jobs, missing credentials, delayed schedules, suppressed alerts) and link it internally, e.g. /docs/troubleshooting. Include “what to check first” steps and where to find logs, run IDs, and recent incidents in the UI.
Shipping a data quality app is less about a “big launch” and more about building trust in small, steady steps. Your first release should prove the loop end-to-end: run checks, show results, send an alert, and help someone fix a real issue.
Begin with a narrow, reliable set of capabilities:
This MVP should focus on clarity over flexibility. If users can’t understand why a check failed, they won’t act on the alert.
If you’re trying to validate the UX quickly, you can prototype the CRUD-heavy parts (check catalog, run history, alert settings, RBAC) in Koder.ai and iterate in “planning mode” before committing to a full build. For internal tools like this, the ability to snapshot and roll back changes can be especially helpful when you’re tuning alert noise and permissions.
Treat your monitoring app like production infrastructure:
A simple “kill switch” for a single check or an entire integration can save hours during early adoption.
Make the first 30 minutes successful. Provide templates like “Daily pipeline freshness” or “Uniqueness for primary keys,” plus a short setup guide at /docs/quickstart.
Also define a lightweight ownership model: who receives alerts, who can edit checks, and what “done” means after a failure (e.g., acknowledge → fix → rerun → close).
Once the MVP is stable, expand based on real incidents:
Iterate by reducing time-to-diagnosis and lowering alert noise. When users feel the app consistently saves them time, adoption becomes self-propelled.
Start by writing down what “data quality” means for your team—typically accuracy, completeness, timeliness, and uniqueness. Then translate each dimension into concrete outcomes (e.g., “orders load by 6am,” “email null rate < 2%”) and pick success metrics like fewer incidents, faster detection, and lower false-alert rates.
Most teams do best with both:
Decide explicit latency expectations (minutes vs hours) because it affects scheduling, storage, and how urgent alerts should be.
Prioritize the first 5–10 must-not-break datasets by:
Also record an owner and expected refresh cadence for each dataset so alerts can route to someone who can act.
A practical starter catalog includes:
These cover most high-impact failures without forcing complex anomaly detection on day one.
Use a “UI first, escape hatch second” approach:
If you allow custom SQL, enforce guardrails like read-only connections, timeouts, parameterization, and normalized pass/fail outputs.
Keep the first release small but complete:
Each failure view should clearly show , , and .
Split the system into four parts:
This separation keeps the control plane stable while the execution engine scales.
Use an append-only model:
Focus on actionability and noise reduction:
Include direct links to investigation pages (e.g., /checks/{id}/runs/{runId}) and optionally notify on recovery.
Treat it like an internal admin product:
Store both summary metrics and enough raw evidence (safely) to explain failures later, and record a config version/hash per run to distinguish “rule changed” from “data changed.”