The Complete Guide to Data Uniqueness

Remember the Healthcare.gov launch fiasco? Millions of Americans tried to sign up for health insurance—and couldn’t. The site crashed under heavy demand and even when people did manage to enroll, the system sometimes created multiple insurance plans for the same person.

Behind this chaos was an often-overlooked but critical aspect of data management: data uniqueness.

Think of data uniqueness as the “no doubles” rule – each piece of data should exist exactly once in your system. When you have duplicates floating around, it’s like having multiple versions of the same document – you never know which one is the one you should trust. Plus, getting rid of duplicates makes your systems run faster since there’s less redundant data to process.

All that Healthcare.gov’s developers had to do was let people pick their plan, allow only one plan per social security number, and you’re done. But by not enforcing data uniqueness they brought down a multi-million dollar government website. Now, how can you prevent something as basic as duplicate data from bringing down your systems?

What is data uniqueness?

Data uniqueness means each record in your dataset appears exactly once within its intended context. No duplicates, no redundant entries. Every piece of information stands alone as a distinct entity that serves a specific purpose.

This isn’t the same as having accurate or complete data. You can have perfectly accurate customer information that’s duplicated three times in your database. You can have complete records with every field filled that still overlap with other entries. Data uniqueness is about ensuring each record has its own identity and reason for existing.

Think about a Customer ID in your CRM system. When it’s unique, one ID points to one customer. Period. That customer might have multiple interactions, purchases, or support tickets, but they have exactly one identity in your system. Without uniqueness, you’re looking at the same customer through a broken mirror, seeing fragments instead of the whole picture.

The same principle applies to SKUs in inventory management. Each SKU represents one specific product variant. Not similar products, not the same product in different locations, but one exact item type. When you maintain SKU uniqueness, you know exactly what you’re counting, where it is, and how it’s moving through your supply chain.

The business impact is straightforward. Without data uniqueness, you’re making decisions based on inflated numbers, incomplete customer views, and unreliable forecasts. You’re essentially flying blind while thinking you have perfect vision.

Data uniqueness in the context of data quality

Data uniqueness doesn’t exist in isolation. It’s one of six core dimensions that determine whether your data is actually usable. The others are accuracy, completeness, timeliness, integrity, and validity. Together, they form the foundation of any serious data quality program.

These dimensions work like interconnected gears. When one fails, it creates problems across the system. Missing values in key identifier fields make it impossible to spot duplicates. You can’t tell if two records with blank customer IDs are the same person or different people. That’s completeness undermining uniqueness.

Data accuracy issues create their own version of this problem. Say you have customer names with inconsistent formatting. “Robert Smith,” “Bob Smith,” and “R. Smith” might be three records for the same person, but inaccurate data entry makes them look unique. Your system counts three customers where only one exists.

The relationship cuts both ways. Duplicate records often contain slightly different information, making it harder to determine which values are accurate. One record shows a customer in New York, another shows them in Boston. Which is right? Without uniqueness, you can’t trust accuracy.

Data governance frameworks like DAMA-DMBOK recognize these connections. They define uniqueness as a fundamental quality characteristic that supports data integrity and data reliability. In their model, uniqueness isn’t just about technical deduplication. It’s about maintaining clear business entity definitions and ensuring each real-world object has exactly one digital representation.

This matters because most organizations treat these dimensions separately. They run deduplication projects without fixing the completeness issues that cause duplicates. They validate data accuracy without addressing the uniqueness problems that make accuracy impossible to measure. It’s like treating symptoms while ignoring the disease.The practical takeaway is simple. You can’t achieve data uniqueness by focusing on uniqueness alone. You need a coordinated approach that addresses all data quality dimensions simultaneously. Otherwise, you’re just moving problems around instead of solving them.

How to measure data uniqueness

You can’t fix what you don’t measure. Data uniqueness requires specific metrics that tell you not just whether you have a problem, but how big it is and whether it’s getting better or worse. Here are the five key measurements that actually matter.

Duplicate count

This is your starting point. Count how many times each record appears when it should only appear once. You’re looking for exact matches on key fields that should be unique.

The approach is straightforward. Group your records by the field that should be unique, like email address or customer ID. Count how many records fall into each group. Anything with more than one record is a duplicate. Sort by frequency to find your worst offenders first.

Run this daily for critical systems, weekly for everything else. Track the results over time to spot trends. If your duplicate count jumps from 50 to 500 overnight, something broke in your data pipeline. If it creeps up slowly over months, you have a process problem.

A declining duplicate count means your data quality initiatives are working. An increasing count signals that new duplicates are being created faster than you’re removing them. Zero isn’t always the goal either. Some business contexts legitimately allow controlled duplication.

Uniqueness ratio

This metric gives you the big picture. It’s the percentage of your records that are actually unique.

Calculate it by dividing the number of distinct records by the total number of records, then multiply by 100. If you have 950 unique customer emails out of 1,000 total records, your uniqueness ratio is 95%.

A declining ratio is your early warning system. It means duplicates are accumulating faster than your dataset is growing. Most industries should maintain above 95% uniqueness for critical identifiers. Financial services and healthcare often require 99% or higher. E-commerce can sometimes tolerate 90% if they have strong matching logic.

Check this metric weekly and compare it month over month. Sudden drops usually indicate a specific event like a bad data import or system migration. Gradual declines point to systematic issues in your data entry or integration processes.

Violation counts

Your database already tracks uniqueness violations if you’ve set up proper constraints. These are records that tried to break uniqueness rules but got rejected.

Look at your error logs or data quality monitoring tools. Count how many times records failed to insert or update because they violated uniqueness constraints. Track which specific constraints get violated most often. This tells you where your upstream systems are repeatedly trying to create duplicates.

Monitor these in real time if possible. High violation counts don’t always mean bad data quality. They might mean your constraints are working perfectly, catching problems before they corrupt your database.

Zero violations might actually be the problem. It could mean your constraints aren’t configured or your data isn’t even attempting to maintain uniqueness.

Cardinality trends

Cardinality is the count of distinct values in a field. When it changes unexpectedly, you’ve got issues.

Track the number of unique values over time for fields that should remain relatively stable. A product catalog should show steady growth in unique SKUs as you add new products. A sudden drop while total records stay stable means you’re creating duplicates. A spike might indicate you’re artificially splitting records that should be unified.

For example, if your unique product SKU count drops from 10,000 to 8,000 overnight but your total inventory records remain at 50,000, you’ve likely introduced duplicate SKUs. Normal business growth shows steady, predictable cardinality increases.

Review cardinality weekly for volatile datasets, monthly for stable ones. Set up alerts for changes beyond normal variance. A 10% swing in either direction deserves investigation.

Mean time to repair (MTTR)

This measures your operational efficiency at fixing duplicates once you find them. It’s the average time between detecting a duplicate and resolving it.

Track when each duplicate was identified and when it was resolved. Calculate the average time gap. Also look at the median to understand typical performance versus outliers that skew the average.

Best in class organizations resolve critical duplicates within 24 hours. Standard business data should be cleaned within a week. Anything taking longer than a month isn’t really being managed.

MTTR tells you whether your data quality process actually works. Low duplicate counts don’t matter if the duplicates you do have sit there for months. High MTTR usually means manual processes, unclear ownership, or lack of proper tooling.

Track MTTR monthly and aim for continuous improvement. If your MTTR is increasing, you need more resources, better tools, or clearer processes. If it’s decreasing, whatever you’re doing is working.

Common causes of data uniqueness issues

Duplicates don’t appear randomly. They have specific, predictable causes that you can identify and prevent. Knowing these root causes helps you move from constantly cleaning data to preventing problems before they start.

Data entry errors

Manual data entry remains one of the biggest sources of duplicate records. It’s not just typos. It’s people creating new records instead of updating existing ones because searching seems harder than starting fresh.

Sales reps create new customer records during calls because the search function takes too long. Customer service agents add duplicate tickets because they can’t find the original. Warehouse staff enter new SKUs because the existing one has a slightly different description. Each instance seems minor, but they compound into major data quality problems.

The fix isn’t eliminating human input. It’s making the right action easier than the wrong one. Implement type-ahead search that finds records as users type. Add duplicate warnings that fire before submission, not after. Create mandatory verification steps for new record creation. If your system asks “Are you sure this isn’t John Smith from Acme Corp?” before allowing a new entry, most duplicates never get created.

Integration mismatches

When systems talk to each other, translation errors create duplicates. Your CRM identifies customers by email, your billing system uses account numbers, and your support platform relies on phone numbers. Every sync between these systems risks creating duplicates.

The problem gets worse with timing mismatches. System A updates every hour, System B updates daily, and System C processes in real-time. A customer who updates their email address might exist as three different people until all systems sync up.

Prevent this by establishing a golden record system. Pick one system as the source of truth for each data type. Customer data lives in the CRM. Product data lives in the inventory system. Everything else subscribes to these sources. Use consistent matching logic across all integrations. If you match on email plus last name in one integration, use the same logic everywhere.

System migrations

Migrations are duplicate factories. The same data gets imported multiple times during testing. Rollbacks leave partial data behind. Parallel runs create double entries. Teams import “just to be safe” when they’re not sure if the first import worked.

The classic scenario happens during CRM transitions. The old system exports customers with internal IDs. The new system generates its own IDs. Without careful mapping, every customer exists twice. Making it worse, teams often run systems in parallel for months, creating new duplicates daily.

Build deduplication into your migration process from day one. Create mapping tables that link old IDs to new ones. Run test imports in isolated environments that get completely wiped between attempts. Set up clear cutover procedures so everyone knows exactly when to stop using the old system. Most importantly, budget time for post-migration cleanup. It’s not if you’ll have duplicates, it’s how quickly you’ll find and fix them.

Schema changes

Database constraints are your last line of defense against duplicates. When they disappear during updates or migrations, duplicates flood in.

It happens innocently enough. A developer drops a unique constraint to speed up a data load. They plan to add it back but forget. Or someone changes a unique index to a non-unique one to solve an immediate problem, not realizing they’ve opened the floodgates. Cloud migrations are particularly dangerous because constraint syntax differs between platforms.

The prevention strategy is simple but requires discipline. Document every constraint in your system and why it exists. Include constraint validation in your deployment pipeline. Make constraint changes require the same approval as code changes. Test that constraints work by deliberately trying to violate them. If your test data successfully creates duplicates, your production data will too.

Lack of governance

This is the root cause behind all other root causes. When nobody owns data quality, everybody assumes somebody else is handling it.

Without clear ownership, different departments create their own rules. Marketing defines a unique customer one way, sales another, and finance a third. Three versions of truth means duplicates are inevitable. Teams create workarounds instead of fixes. They build complex matching logic to deal with duplicates instead of preventing them.

Assign specific people to own data quality for specific domains. The customer data owner decides what makes a customer unique. The product data owner defines SKU standards. These aren’t committee decisions or consensus-building exercises. They’re clear accountability assignments.

Give these owners actual authority. They need the power to reject bad data, mandate cleanup efforts, and block system changes that compromise uniqueness. Without teeth, data governance is just documentation that nobody reads.

How to enforce data uniqueness

Knowing you have duplicates is only half the battle. You need enforcement mechanisms that prevent them from appearing in the first place. Different systems require different approaches, and choosing the right one depends on your data architecture and business requirements.

Database constraints

Database constraints are your first and strongest line of defense. They physically prevent duplicate data from entering your system. Primary keys ensure each record has a unique identifier. Unique constraints guarantee specific fields or field combinations remain distinct. Unique indexes do the same while improving query performance.

Use database constraints when you have direct control over the database and clear uniqueness rules. They’re perfect for customer IDs, email addresses, SKUs, and other business identifiers that must remain unique.

The biggest advantage is reliability. The database enforces these rules at the lowest level, making it impossible for any application or user to violate them. They work regardless of how data enters your system. The performance impact is minimal for writes and actually improves read performance through indexing.

The downside is inflexibility. Constraints either work or they don’t. They can’t handle fuzzy matching or complex business rules. When legitimate duplicates need temporary existence during processing, constraints become obstacles. Changing them requires database migrations that can lock tables and disrupt service.

For example, adding a unique constraint to an email column prevents any duplicate email addresses from entering your customer table. But if you need to allow duplicate emails for different account types, you’ll need a composite unique constraint on email plus account type.

Data pipelines

ETL and ELT pipelines offer more sophisticated deduplication than database constraints. You can apply complex matching logic, handle fuzzy duplicates, and clean data before it reaches your warehouse. This is where you implement business rules that go beyond simple field matching.

Use pipeline deduplication when you’re consolidating data from multiple sources or when uniqueness rules involve business logic. It’s ideal for situations where customer names might have variations, addresses need standardization, or you need to match records across systems with different identifiers. This approach works well for batch processing scenarios where you can afford slight delays for thorough cleaning.

The main advantage is flexibility. You can implement any deduplication logic you need. Match on multiple fields with different weights. Handle variations in formatting. Preserve audit trails of what got deduplicated. You can also fix existing duplicates while preventing new ones. Pipeline deduplication lets you merge duplicate records intelligently, keeping the best data from each rather than simply discarding one.

The downside is complexity and performance. Pipeline deduplication requires more code, more testing, and more maintenance than database constraints. It adds processing time to your data loads. If your pipeline fails, duplicates might slip through. You also need to handle edge cases like what happens when your deduplication logic itself has bugs. Common approaches include using window functions to identify and remove duplicates based on specific ordering criteria. You might keep the most recent record, the most complete record, or merge multiple records into one. The key is documenting your deduplication logic so others understand why certain records survive while others don’t.

Streaming systems

Real-time data streams require different deduplication strategies. You can’t wait for a batch window to close. You need to identify and handle duplicates as they flow through your system.

Use streaming deduplication when you’re processing events, IoT data, or any real-time feeds where duplicates cause immediate problems. This is essential for payment processing, real-time analytics, and event-driven architectures.

The advantage is immediate protection. Duplicates get caught and handled in milliseconds, not hours. You can maintain exactly-once processing semantics even when source systems send duplicates. This prevents downstream systems from processing the same event multiple times.

The challenge is state management. To identify duplicates, you need to remember what you’ve seen before. This requires maintaining state stores that can grow large and complex. You also need to decide how long to remember events. Keep the window too short and duplicates slip through. Keep it too long and memory usage explodes.

A typical approach involves maintaining a sliding window of recent message keys. When a new message arrives, check if its key exists in the window. If it does, it’s a duplicate. If not, add it to the window and process the message. The window slides forward based on time or message count, forgetting old entries to save memory.

Data lakes

Data lakes present unique challenges for uniqueness. Files can contain duplicates internally and across files. Without schema enforcement, maintaining uniqueness requires deliberate strategy.

Use data lake deduplication when working with large-scale analytical workloads where storage is cheap but query performance matters. This applies to data science platforms, historical analysis systems, and archive storage.

The benefit is scale. Modern table formats handle petabytes of data while maintaining uniqueness guarantees. They support time travel, allowing you to query data as it existed before deduplication. You can optimize storage and query performance simultaneously.

The drawback is eventual consistency. Deduplication often happens asynchronously through compaction jobs. Duplicates might exist temporarily until the next compaction run. This works fine for analytical queries but not for operational systems requiring immediate consistency.

Table formats like Delta Lake or Apache Iceberg provide merge operations that deduplicate during writes. You define match conditions and update rules. When new data arrives, the system automatically merges it with existing data, handling duplicates according to your rules. Compaction jobs run periodically to optimize file layout and remove any duplicates that accumulated between merges.

Application layer validation

Sometimes the best place to enforce uniqueness is in your application code. Before data ever reaches your database or pipeline, validate it at the API or service layer. This gives you complete control over the user experience and lets you implement validation logic that would be impossible at the database level.

Use application validation when you need complex business rules, user-friendly error messages, or when you’re working with systems that don’t support native constraints. This approach works well for user-facing applications where you want to provide immediate feedback. It’s also necessary when uniqueness depends on external factors like checking against third-party systems or complex state machines.

The advantage is control and user experience. You can implement any validation logic, provide helpful error messages, and suggest corrections. When someone tries to create a duplicate customer, you can show them the existing record and ask if they want to update it instead. You can check uniqueness across multiple systems or implement soft deletes where records appear deleted but remain in the database. Application validation also lets you handle eventual consistency gracefully, queuing operations for retry when temporary duplicates are acceptable.

The risk is inconsistency. Every application needs to implement the same validation logic. If one application skips validation or implements it differently, duplicates creep in. This approach also doesn’t protect against direct database access or bulk imports that bypass your application. A developer running a manual SQL insert or a data migration script can create duplicates that your application layer never sees. Best practice combines application validation with database constraints. The application layer provides a good user experience and handles complex cases. Database constraints provide the ultimate safety net. This belt-and-suspenders approach ensures uniqueness even when applications misbehave or validation logic has bugs.

Detecting data uniqueness violations

Finding duplicates after they exist is playing defense, but it’s still essential. You need both automated detection that catches problems immediately and analytical tools that help you understand patterns. The best detection strategy combines proactive monitoring with reactive investigation capabilities.

Data profiling

Data profiling examines your data’s characteristics before it moves through your pipeline. It’s like quality control at the factory entrance. You catch uniqueness problems before they spread downstream.

Run profiling on incoming data to understand its actual uniqueness characteristics versus what you expect. Count distinct values in fields that should be unique. Calculate uniqueness ratios. Look for patterns in what would become duplicates. This tells you whether the source system is sending clean data or if you need to add deduplication steps.

Modern profiling tools integrate directly into your data pipeline. They automatically check uniqueness rules as data flows through. When a batch contains more duplicates than normal, they flag it for review. You can set thresholds that match your business tolerance. Maybe 1% duplicates is acceptable for marketing data but unacceptable for financial records.

The real value comes from profiling over time. You learn the normal duplicate rate from each source system. When that rate changes, you know something broke. A vendor updated their export process. An API started sending duplicate events. A manual process changed. Profiling catches these shifts before they become data quality incidents.

Observability platforms

Data observability platforms continuously monitor for data anomalies, including uniqueness violations. They learn what normal looks like for your data and alert you when something changes.

These platforms track uniqueness metrics automatically. They notice when your customer table suddenly has 10% more duplicates than usual. They detect when a normally unique field starts having repeated values. They correlate these issues across tables to identify systemic problems versus isolated incidents.

The advantage over manual monitoring is intelligence. Observability platforms understand relationships between datasets. They know that duplicates in your orders table probably stem from duplicates in your customers table. They trace the problem upstream automatically, saving hours of investigation. They also reduce alert fatigue by distinguishing real problems from normal variation.

Implementation is straightforward. Point the platform at your data warehouse or lake. It starts learning immediately. Within days, it understands your normal patterns. Within weeks, it’s catching uniqueness issues you didn’t know existed. The best platforms require no configuration for basic monitoring, though you can add custom rules for business-specific requirements.

Monitoring KPIs

Dashboards turn uniqueness metrics into visible, actionable information. They’re your mission control for data quality, showing duplicate trends at a glance.

Build dashboards that track your key uniqueness metrics over time. Show duplicate counts for critical tables. Display uniqueness ratios with clear threshold lines. Include trend lines that reveal whether problems are getting better or worse. Make these dashboards visible to everyone who creates or uses data, not just the data team.

The most effective dashboards focus on business impact, not just technical metrics. Show how many duplicate customers received multiple marketing emails. Display the dollar value of duplicate orders. Calculate the time wasted on duplicate support tickets. When people see the business cost of duplicates, they take uniqueness seriously.

Update frequency matters. Real-time dashboards catch problems immediately but can be noisy. Daily dashboards smooth out variation but might miss urgent issues. The right frequency depends on your data velocity and business requirements. Financial systems might need minute-by-minute monitoring. Monthly reporting systems can check daily.

Root cause analysis

When duplicates appear, you need to trace them back to their source. This is where data lineage becomes invaluable. It shows you exactly how data flowed from source to destination.

Modern lineage tools automatically map your data flows. They track every transformation, every join, every system that touched your data. When duplicates appear in your warehouse, lineage shows you the path they traveled. You can identify exactly where uniqueness broke down. Was it the source system? The extraction process? A transformation that accidentally duplicated records?

The investigation process becomes systematic instead of guesswork. Start with the duplicate records. Use lineage to identify their immediate source. Check if duplicates existed there. If not, you’ve found where they were introduced. If they did exist, trace back another level. Continue until you find the root cause.

Lineage also reveals impact. When you find duplicates in one dataset, lineage shows you every downstream table they’ve contaminated. This helps you prioritize fixes and communicate impact to stakeholders. If duplicates in your product catalog affect pricing, recommendations, and inventory, everyone needs to know immediately.

Best practices for maintaining data uniqueness

Maintaining data uniqueness isn’t a one-time project. It’s an ongoing discipline that requires the right processes, tools, and accountability structures. These practices separate organizations with trustworthy data from those constantly fighting duplicates.

Governance

Clear ownership prevents uniqueness problems before they start. Every dataset needs a specific person responsible for its quality, including uniqueness. Not a team, not a committee, but an individual with a name and email address.

Data owners define what makes a record unique in their domain. The customer data owner decides whether an email address, phone number, or a combination identifies a unique customer. The product owner determines if SKU alone is sufficient or if you need SKU plus warehouse location. These aren’t technical decisions. They’re business decisions that require domain expertise.

Escalation paths matter when uniqueness violations occur. Who gets notified when duplicate rates exceed thresholds? Who has authority to stop a data load that would introduce thousands of duplicates? Who decides whether to merge or delete duplicates when they’re discovered? Document these paths clearly. Post them where everyone can see them.

The most effective governance structures tie uniqueness metrics to performance reviews. If you own customer data and duplicate rates increase on your watch, that should affect your evaluation. This sounds harsh, but unclear accountability is why most data quality initiatives fail. When uniqueness metrics affect bonuses and promotions, people pay attention.

Automation

Manual deduplication doesn’t scale. You need automated processes that run continuously without human intervention. Schedule these jobs based on your data velocity and business requirements.

Set up daily deduplication for operational systems where duplicates cause immediate problems. Run weekly jobs for analytical systems where perfect uniqueness is less critical. Monthly deep cleans can catch edge cases that daily processes miss. The key is consistency. Regular small cleanups prevent the need for massive deduplication projects.

Automated deduplication should be self-healing. When the process finds and fixes duplicates, it should also identify why they occurred. Did a specific source system send duplicates? Did a particular transformation fail? Feed this information back into your monitoring systems. Over time, your automation gets smarter about preventing duplicates, not just fixing them.

Build safeguards into your automation. Set limits on how many records can be modified in a single run. Require human approval for changes affecting more than a certain percentage of your data. Create rollback procedures for when deduplication logic has bugs. Automation should make your life easier, not create new categories of problems.

Integration

Make uniqueness validation part of your development workflow. Just as code doesn’t deploy without passing tests, data shouldn’t flow without meeting uniqueness requirements.

Include uniqueness checks in your continuous integration pipeline. When developers modify ETL code, automatically test that their changes don’t introduce duplicates. Use sample datasets that include edge cases and known problem scenarios. If the modified pipeline creates duplicates in test data, it will create duplicates in production.

Pull request reviews should include uniqueness considerations. Does this new data source have natural keys? How will we match its records against existing data? What happens if the source sends duplicates? These questions should be answered before code merges, not after duplicates appear in production.

Testing should cover both prevention and detection. Verify that your uniqueness constraints actually work by deliberately trying to insert duplicates. Confirm that your monitoring catches violations by introducing known duplicates and checking for alerts. Test your deduplication logic with realistic dirty data, not just clean test cases.

Documentation

Your future self needs to understand why uniqueness rules exist and how they work. Document every unique constraint, what it protects, and what breaks if it’s removed.

Maintain a data dictionary that clearly identifies unique identifiers for each table. Explain whether uniqueness is enforced at the database level, application level, or both. Document any exceptions or edge cases where duplicates are temporarily allowed. When someone new joins your team, they should be able to understand your uniqueness requirements without reverse-engineering your constraints.

Business keys need special attention. These are the field combinations that identify unique business entities, regardless of technical surrogate keys. A customer might have a technical ID, but the business identifies them by email plus region. Document these relationships. They’re critical for deduplication logic and for understanding cross-system data flows.

Keep a decision log for uniqueness rules. When you decide that product uniqueness requires SKU plus warehouse, document why. What alternatives did you consider? What business requirements drove the decision? This context prevents future teams from unknowingly breaking critical uniqueness rules. It also helps when business requirements change and you need to revisit these decisions.

How to enforce data uniqueness in PostgreSQL

If all of your important data is in a database (please tell me it’s in a database!), making sure that all data will be unique is straightforward.

Just add the UNIQUE keyword.Imagine we have a table called users, where we want to make sure each user’s email is unique. To enforce this, we can use the UNIQUE constraint like so:

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    firstname VARCHAR(50),
    lastname VARCHAR(50),
    email VARCHAR(100) UNIQUE
);

That way when we start inserting users, and someone comes around with the same email:

-- Inserting the first user
INSERT INTO users (username, email) VALUES ('john_doe', 'john@example.com');

-- Inserting the second user with the same email as the first
INSERT INTO users (username, email) VALUES ('johnny', 'john@example.com');

We will get this error message:

ERROR: duplicate key value violates unique constraint "users_email_key"
DETAIL: Key (email)=(john@example.com) already exists.

How to check for duplicate data

But what if we think there is already some sort of duplicate data in our database?To find any exact duplicates, we can use a query with GROUP BY and HAVING:

SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

Where the output could look like this:

email	count
john@example.com	2
jane@example.com	3

Meaning that the email john@example.com was found twice while jane@example.com was found three times.

Finding near duplicates is also possible. For example, if you want to consider “Avenue” and “Ave.” as duplicates. The best way to do this is with a trigram search, which we can enable with the pg_trgm extension:

CREATE EXTENSION pg_trgm;

Then we can use this query to find any other addresses in the users table that are similar to the address 123 Avenue Street:

SELECT id, address, similarity(address, '123 Avenue Street') AS sim_score 
FROM addresses 
WHERE similarity(address, '123 Avenue Street') > 0.6 
ORDER BY sim_score DESC;

[similarity(address, ‘123 Avenue Street’)] is the key here. It calculates a similarity score for the address field based on 123 Avenue Street. Then the most similar addresses (those with a score higher than 0.6) are selected.

So the results might look something like this:

id	address	sim_score
1	123 Avenue Street	1.0
2	123 Ave Street	0.923
3	123 Avenue St	0.762
4	123 Avenue St.	0.675

And once we verify that these duplicates are not wanted, we can then pass them to a DELETE statement, cleaning up our database and improving our data uniqueness.

But there are even better ways to make sure that data is unique not only in your database, but also across your entire data infrastructure.

How can you make sure your data is unique?

Even with all of the constraints and checks, duplicate data can still sneak its way into your systems. You should always use a data observability platform like Monte Carlo to check your data for duplicates in real-time. That way you can deal with them quickly before they become a bigger problem.

METRIC	DESCRIPTION	COLUMN Types	MAC NAME
Unique (%)	Percentage of unique values across all rows	All	UNIQUE_RATE
Unique (count)	Count of unique values	All	UNIQUE_COUNT
Duplicate (count)	Count of duplicate values, calculated as the difference between the total number of rows with non-null values and total number of distinct non-null values	All	DUPLICATE_COUNT

A data observability platform like Monte Carlo also lets you:

• Track your data lineage, automatically detecting any other anomalies hiding in your system.
• Monitor the quality of datasets to see how you improve over time.
• Find the root cause for data issues quickly by analyzing trends and patterns.

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How Monte Carlo helps ensure data uniqueness

Monte Carlo automatically tracks uniqueness across your entire data stack without requiring manual configuration. It learns the normal patterns of your data and detects when duplicate rates change unexpectedly.

The platform monitors cardinality and uniqueness ratios for all your tables and columns. When your customer email field typically has 99% unique values but suddenly drops to 85%, Monte Carlo catches it. It tracks these metrics continuously, building a baseline of normal behavior for each dataset. This means you don’t need to manually set thresholds for thousands of fields. The system understands what’s normal and what’s not.

When uniqueness violations occur, Monte Carlo sends alerts through your existing channels. Slack, email, PagerDuty, whatever your team uses. The alerts include context about the severity and scope of the issue. You’ll know if duplicates appeared in 10 records or 10,000, whether it’s a gradual trend or sudden spike, and which specific fields are affected. This helps you prioritize response and allocate resources appropriately.

The lineage capabilities connect uniqueness issues to their root causes. When duplicates appear in your reporting tables, Monte Carlo shows you every upstream transformation and source system. You can trace the problem back through your pipeline to identify exactly where uniqueness broke down. Did the vendor send duplicate records? Did a join condition fail? Did a deduplication process stop running? The lineage map provides answers, turning hours of investigation into minutes of guided analysis.

Integration happens at the metadata level, so Monte Carlo doesn’t touch your actual data. It connects to your warehouse, observes patterns, and alerts on anomalies. This means you get uniqueness monitoring without performance impact, security risks, or complex setup procedures. The system starts providing value immediately while your team focuses on fixing problems rather than finding them.