Discover the simplest way to check data in sql server: Quick Checks, Data Validation, and T-SQL Techniques 2026

Discover the simplest way to check data in sql server. Quick fact: you shouldn’t chase complex tools when a few solid queries and a clear plan can give you trustworthy results fast. In this guide, you’ll get a practical, step-by-step approach to validating data in SQL Server, plus real-world tips that save time. Here’s what you’ll find:

A fast starter kit: essential checks you can run in minutes
How to validate data quality across tables and databases
Simple techniques for spotting anomalies, NULLs, duplicates, and outliers
Practical examples you can adapt to your schema
Troubleshooting tips and performance-minded best practices

Quick starter checks

Verify row counts across related tables
- SELECT t.name AS table_name, SUMp.rows AS row_count
  FROM sys.tables t
  JOIN sys.partitions p ON t.object_id = p.object_id
  WHERE p.index_id IN 0,1
  GROUP BY t.name
  ORDER BY row_count DESC;
Check for NULLs in key columns
- SELECT ‘TableName’ AS table_name, COUNT* AS null_count
  FROM TableName
  WHERE KeyColumn IS NULL;
Validate ranges for numeric columns
- SELECT MINNumericColumn AS min, MAXNumericColumn AS max
  FROM TableName;
Spot obvious duplicates on a composite key
- SELECT KeyPart1, KeyPart2, COUNT AS cnt
  FROM TableName
  GROUP BY KeyPart1, KeyPart2
  HAVING COUNT > 1;
Inspect a sample of text data for consistency
- SELECT TOP 100 TextColumn
  FROM TableName
  ORDER BY NEWID;

Introduction to data checking mindset

Start with high-level metrics: row counts, totals, and basic integrity checks
Move to column-level validations: NULLs, ranges, and data type mismatches
Finish with cross-table integrity: foreign keys, referential integrity
Always test on a copy or a staging environment when possible

Core techniques and how to use them

Row count sanity checks

Why it matters: mismatched counts hint at deletes, soft deletes, or ETL errors
How to do it: compare counts between source system and SQL Server
Example:
- SELECT COUNT* AS total_rows FROM Orders;
- SELECT COUNT* AS total_rows FROM dbo.OrdersArchive WHERE Archived = 0;
Helpful tip: use CHECKSUM or HASHBYTES for broader comparisons on large datasets

NULL and NOT NULL validation

Why it matters: unexpected NULLs break joins and analytics
How to do it: look for NULLs in columns that should be populated
Example:
- SELECT COUNT* AS nulls_in_email FROM Customers WHERE Email IS NULL;
Pro tip: create is_null checks as part of daily data quality dashboards

Data type and format checks

Why it matters: values outside expected types cause errors in downstream apps
How to do it: scan for out-of-range or malformed data
Example:
- SELECT TOP 100 * FROM Payments WHERE Amount < 0;
- SELECT TOP 100 * FROM Users WHERE Email LIKE ‘%@%.%’
Quick wins: enforce constraints and use TRY_CAST to catch conversion issues

Range and business rule validation

Why it matters: values must respect business constraints
How to do it:
- SELECT MINAge, MAXAge FROM Customers;
- SELECT OrderDate FROM Orders WHERE OrderDate > GETDATE;
Example of a common rule: OrderDate should not be in the future

Duplicate detection

Why it matters: duplicates distort counts and analytics
How to do it:
- SELECT CustomerId, OrderDate, COUNT AS dups
  FROM Orders
  GROUP BY CustomerId, OrderDate
  HAVING COUNT > 1;
Action: investigate root cause ETL, upserts, or source data

Referential integrity checks

Why it matters: orphaned rows break relationships
How to do it:
- SELECT o.OrderID FROM Orders o LEFT JOIN Customers c ON o.CustomerID = c.CustomerID WHERE c.CustomerID IS NULL;
Keep in mind: foreign keys help, but checks still useful in data warehouses

Nullability and defaults review

Why it matters: default values may not reflect real data
How to do it:
- SELECT ColumnName, is_nullable, COLUMN_DEFAULT
  FROM INFORMATION_SCHEMA.COLUMNS
  WHERE TABLE_NAME = ‘YourTable’;
Use this to document schema expectations and catch drift

Data distribution and statistics

Why it matters: helps spot skew and unusual spikes
How to do it:
- SELECT ColumnName, COUNT* AS cnt, AVGCASTColumnName AS FLOAT AS avg, STDDEVCASTColumnName AS FLOAT AS stddev
  FROM YourTable
  GROUP BY ColumnName;
Visual aid: export to CSV and plot in a charting tool

Data quality dashboards practical setup

Build a simple dashboard with:
- Key metrics: total rows, NULL counts, duplicates, and rule violations
- Trend lines over time to catch drift
- Alerts when thresholds are breached
Tools: SQL Server Reporting Services SSRS, Power BI, or simple spreadsheets

Performance-conscious checks

Avoid heavy queries on production tables during peak times
Use pre-aggregated summaries or sampling for quick checks
Schedule nightly integrity checks and capture delta results

Schema-aware checklists by common data domains

Customers
- Nulls in essential fields CustomerID, Email, CreatedDate
- Valid email formats and domain sanity
- Unique CustomerID and no duplicate composite keys
Orders
- OrderDate within acceptable range
- Amount non-negative
- Foreign keys to Customers and Products exist
Products
- Price and Cost non-negative
- Stock quantity non-negative
- Category references valid
Transactions
- Timestamp precision and time zone consistency
- Amount sums consistent with related orders

Tables, views, and indexes that help you check data faster

Use indexed views for pre-aggregated checks
Use filtered indexes on columns frequently checked for NULLs or ranges
Create computed columns to simplify recurring checks

Data validation patterns you can reuse

Pattern A: nullability guardrails
- Create a view that exposes only columns that should be NOT NULL, and test that count of NULLs is zero
Pattern B: type-safe checks
- Use TRY_CAST to identify conversion problems without failing the entire query
Pattern C: referential integrity in ETL
- Before loading, validate that all foreign keys refer to existing keys in the target reference table

Examples: practical queries you can copy

Check for NULLs in critical columns
- SELECT ‘Customers.Email’ AS table_column, COUNT* AS null_count FROM Customers WHERE Email IS NULL;
Identify negative or zero values where not allowed
- SELECT ‘Orders.Amount’ AS column_name, COUNT* AS invalid_count FROM Orders WHERE Amount <= 0;
Detect duplicates on a composite key
- SELECT CustomerID, OrderDate, COUNT AS dupes
  FROM Orders
  GROUP BY CustomerID, OrderDate
  HAVING COUNT > 1;
Validate date ranges
- SELECT COUNT* AS future_dates
  FROM Orders
  WHERE OrderDate > GETDATE;
Ensure foreign key references exist
- SELECT o.OrderID
  FROM Orders o
  LEFT JOIN Customers c ON o.CustomerID = c.CustomerID
  WHERE c.CustomerID IS NULL;

How to document your findings

Create a simple data quality notebook or doc
Include:
- What was checked
- The SQL used
- Result summaries counts, min/max, anomalies
- Recommended remediation steps
Share dashboards with stakeholders for transparency

Automating checks and setting up alerts

Use SQL Server Agent to run nightly data quality jobs
Create a job with steps:
- Run the above checks
- Save results to a table or file
- If thresholds are breached, email the data team
Sample alert idea: if null_count > 0 on critical fields, trigger alert

Common pitfalls and how to avoid them

Pitfall: Running heavyweight checks during business hours
- Solution: optimize with sampling and indexing
Pitfall: Not differentiating between truly NULL and empty string
- Solution: treat empty string as NULL where appropriate
Pitfall: Overlooking time zones in date fields
- Solution: normalize dates to a single time zone for checks
Pitfall: Ignoring historical drift
- Solution: compare current checks to a baseline from the previous week or month

Best practices for SQL Server data checks

Start with a clear data quality charter and define what “clean” means for each table
Keep checks automated and repeatable
Use TRY_CONVERT and TRY_CAST to identify conversion problems without failing
Validate both the data and its lineage from the source to the warehouse
Track changes over time to catch subtle drift

Common scenarios and recommended queries

Scenario: You suspect data quality issues after a recent ETL run
- Check for row count mismatches between source and destination
- Look for NULLs introduced during the ETL
Scenario: You need to validate new customer records are complete
- Check required fields: CustomerID, Name, Email, CreatedDate
- Validate email format and uniqueness
Scenario: You’re auditing billing data for a period
- Verify no negative charges
- Cross-check totals against a trusted reference

Advanced tips for power users

Use CROSS APPLY with a small, well-chosen subquery to inspect data slices
Leverage window functions to compute running totals and gaps
Create user-defined functions to encapsulate common validation logic
Consider partition switching for testing large datasets safely

Useful resources and references unlinked text

SQL Server data quality best practices – sqlservercentral.com
TRY_CAST and TRY_CONVERT usage in SQL Server – docs.microsoft.com
SQL Server performance and indexing guides – stackoverflow.com
Data quality framework examples – data.world
SQL Server Agent scheduling tips – docs.microsoft.com

Frequently Asked Questions

Table of Contents

How often should I run data checks in SQL Server?

Regular checks are best. Start with daily checks for critical pipelines and weekly deeper audits. Adjust based on data freshness needs and system load.

What’s the difference between NULL and empty string in checks?

NULL means unknown or missing, while an empty string is a deliberate empty value. Treat them differently where business rules require.

Use cross-table validation queries, starting with foreign key consistency, then do cross-table aggregates and referential integrity checks.

What tools are great for visualizing data quality results?

Power BI, SSRS, or simple Excel/Google Sheets dashboards work well for most teams. Export results for quick sharing.

How do I handle large datasets without slowing down checks?

Use sampling, pre-aggregated summaries, and indexed views. Run heavy checks in off-peak hours or on a replica environment.

Can I automate checks with alerts?

Yes. Use SQL Server Agent or a modern workflow tool to schedule checks and send alerts when thresholds are breached.

Should I assert data quality during ETL or in the warehouse?

Do both. Validate as you ETL to catch issues early, and maintain ongoing checks in the warehouse for long-term stability.

How do I handle data drift over time?

Track baseline metrics and set alerts for drift. Regularly review and update validation rules as business rules evolve.

What are TRY_CAST and TRY_CONVERT good for?

They let you attempt type conversion and safely identify values that can’t be converted, without causing query failures.

How do I start if I’m new to SQL Server data checks?

Begin with a small, well-defined table, create a few basic checks NULLs, ranges, and duplicates, and gradually expand to more complex rules.

Appendix: sample baseline template for your data checks

Table: Customers
- Checks: NULL Email count = 0, Unique CustomerID, Valid Email format
- Baseline: 0 NULL emails, 100% unique CustomerIDs, 95% of emails match a simple pattern
- History: Track daily counts and alert if NULLs increase by more than 5%

Appendix: quick-reference cheat sheet

NULL checks: SELECT COUNT* FROM Table WHERE Column IS NULL;
Negative values: SELECT COUNT* FROM Table WHERE Column < 0;
Duplicates: SELECT KeyColumn, COUNT FROM Table GROUP BY KeyColumn HAVING COUNT > 1;
Future dates: SELECT COUNT* FROM Table WHERE DateColumn > GETDATE;
Orphans: SELECT o.* FROM ChildTable o LEFT JOIN ParentTable p ON o.ParentID = p.ParentID WHERE p.ParentID IS NULL;

Useful URLs and Resources – text only

Apple Website – apple.com
Artificial Intelligence Wikipedia – en.wikipedia.org/wiki/Artificial_intelligence
SQL Server Documentation – docs.microsoft.com/en-us/sql
SQL Server Central – sqlservercentral.com
Stack Overflow – stackoverflow.com
Data Quality Framework Examples – data.world
Power BI Documentation – docs.microsoft.com/en-us/power-bi
SSRS Overview – docs.microsoft.com/en-us/sql/reporting-services
Turbo charge SQL performance – sqlperformance.com
Monitoring SQL Server with SQL Server Agent – sqlservercentral.com/articles/monitoring-sql-server-with-sql-server-agent

Run a quick SELECT TOP 100 * FROM your_table to preview sample data. That simple move is the fastest way to get eyes on what you’re dealing with, and from there you can run a handful of trusted checks to verify structure, quality, and consistency. In this guide, you’ll get a practical, reader-friendly playbook for checking data in SQL Server—from a speedy first look to robust validation you can automate. Think of it as a practical toolkit rather than a wall of theory. We’ll cover:

Quick-start checks to sanity‑check data now
How to validate data types, NULLs, ranges, and constraints
Techniques for spotting duplicates and orphaned references
How to compare datasets across environments
Tools, scripts, and best practices for reliability and speed
Real-world examples you can copy-paste and adapt

Useful URLs and Resources:

SQL Server Documentation – docs.microsoft.com/en-us/sql/sql-server
SQL Server Central – sqlservercentral.com
MSSQLTips – mssqltips.com
Stack Overflow SQL Server tag – stackoverflow.com/questions/tagged/sql-server
GitHub – sql-server-samples

Quick start: preview data and confirm the basics

Previewing a slice of data is the fastest way to confirm what you’re looking at. A few routine checks right after you load or refresh a table or view can save you hours later in debugging.

Step 1: Take a quick sample
Query: SELECT TOP 100 * FROM . ORDER BY SELECT NULL.
Why this helps: you get a representative glance at column values, formats, and obvious anomalies without scanning the entire table.
Step 2: Confirm row counts line up with expectations
Query: SELECT COUNT* AS total_rows FROM ..
Why this helps: a mismatch in expected vs. actual row counts is often the first sign of ETL issues or truncation.
Step 3: Inspect a few critical columns
- Query: SELECT TOP 100 , , FROM . ORDER BY SELECT NULL.
- Why this helps: ensures values look sane dates aren’t future-dated by decades, IDs aren’t negative, etc..

Pro tip: set NOCOUNT ON to avoid counting messages that slow down lots of checks in larger scripts.

Validate data types, NULLs, and basic quality

Data type correctness and NULL handling are the backbone of data quality. If you’ve got a column that should be non-null, or a numeric column that must stay within a range, you’ll want quick checks that highlight violations.

Check for unexpected NULLs
Query: SELECT COUNT* AS nulls_in_col FROM . WHERE IS NULL.
Extend to multiple columns with conditional sums or a CROSS TAB approach.
Validate data types by inspecting a sample
- Query: SELECT TOP 100 FROM ..
- Look for values that don’t match the declared type e.g., strings in a numeric column, or dates in an inconsistent format.
Enforce simple constraints in queries
- Example: Ensure dates aren’t in the future
- Query: SELECT COUNT* AS future_dates FROM . WHERE > GETDATE.
- Example: Ensure numeric columns stay in expected ranges
- Query: SELECT COUNT* AS out_of_range FROM . WHERE < 0 OR > 1000.
Validate string lengths
- Query: SELECT , LEN AS len FROM . WHERE LEN > 255.

Real-world note: many data issues originate from ETL boundary logic. Pair these checks with a quick look at the transforming steps to isolate where the problem crept in.

Check for duplicates and referential integrity

Duplicates and broken references are two of the most common quality issues in relational databases. Quick queries can surface these problems fast. Discover the Ultimate Guide to Setting Up Your Discord Server with Bots 2026

Find duplicates in a single column
Query: SELECT , COUNT AS cnt FROM . GROUP BY HAVING COUNT > 1.
Duplicates across a composite key
Query: SELECT , , COUNT AS cnt FROM . GROUP BY , HAVING COUNT > 1.
Orphaned foreign keys referential integrity
- Parent/child example:
  - SELECT c. FROM . c LEFT JOIN . p ON c. = p. WHERE p. IS NULL.
- Why this helps: reveals rows in the child table that don’t have a corresponding parent.

Practical tip: add a small index on the columns you frequently check for duplicates or referential integrity to speed up these scans on larger tables.

Check data ranges, validity, and business rules

Business rules are where data quality often diverges from the truth. Validate common rules with straightforward queries.

Date logic
- Ensure date columns aren’t in the future beyond a tolerable window
- Query: SELECT COUNT* FROM . WHERE > DATEADDyear, 1, GETDATE.
Finance or quantity checks
- Validate non-negative quantities
- Query: SELECT COUNT* FROM . WHERE < 0.
Categorical integrity
- Confirm that a dimension column only contains expected values
  - Query: SELECT DISTINCT FROM ..
- If you have a known list, compare with it
- Query: SELECT DISTINCT FROM . EXCEPT SELECT * FROM VALUES ‘A’, ‘B’, ‘C’ AS tv.

Tip:Keep a small reference table of valid codes and compare against it with a NOT EXISTS or NOT IN check to catch any drift.

Compare data across environments dev/test/prod

Consistency across environments is a common challenge. A few practical checks help you catch drift before it hits production.

Row count comparison
- Query: SELECT SUMROW_COUNT FROM sys.dm_db_partition_stats WHERE object_id = OBJECT_ID’schema.table’.
Use a simple baseline to compare environment A vs environment B: SELECT COUNT* FROM schema.table in each environment and compare.
Checksum-based comparisons
- Approach: compute a lightweight hash of key columns and compare across environments
- Example:
  - SELECT CHECKSUM_AGGBINARY_CHECKSUM, , AS checksum FROM ..
- Why this helps: a mismatch flags a data drift without comparing every row.
Random sampling for quick drift checks
Query: SELECT TOP 1000 * FROM . ORDER BY NEWID.
Compare distributions of key columns min, max, average, median if needed across environments.

Note: For large datasets, avoid full-table row-by-row comparisons. use aggregated checks and sampling to stay efficient. Discover what couldnt open connection from server means and how to fix it 2026

Tools and automation for reliable checks

Manual checks are good for quick diagnostics, but automation scales. SQL Server offers a few built-in options to keep data checks consistent and repeatable.

SQL Server Management Studio SSMS and Azure Data Studio
- Use templates, saved queries, and query windows for repeatable checks.
SQL Server Agent
- Schedule jobs that run data quality checks, log results, and raise alerts on failures.
PowerShell and SQLCMD
- Integrate with dashboards or CI/CD pipelines to run checks after deployments.
Database snapshots and anomaly detection
- For ongoing health, maintain lightweight snapshots of row counts and basic stats to detect sudden shifts.
Data quality tools
- Consider third-party or cloud-native data quality tools for more advanced profiling and rules, especially in data lakes or multi-source environments.

Performance tips: when you’re checking large tables, filter to relevant partitions or date ranges, and turn on statistics IO to understand the cost of checks. Use appropriate indexing for any routine checks that scan columns frequently.

Example scenarios you can implement today

ETL validation after a nightly load
- Quick checks:
  - Row count matches the staging table
  - No NULLs in critical columns
  - No negative values in strictly non-negative fields
- Then: a small business-rule check e.g., total revenue sums correctly
Data migration validation
- Compare sums and min/max values for key numeric columns
- Verify foreign key integrity in the new schema
Agile analytics checks for a dashboard dataset
- Ensure daily delta loads bring in at least a minimum number of rows
- Validate the most recent date aligns with today or yesterday depending on schedule

Tables, views, and index choices can help you tailor these checks to your schema and data volumes. Start simple, then layer on more coverage as needed.

Practical best practices for reliable checks

Keep checks focused and idempotent
- Each check should produce a clear pass/fail result and be safe to re-run.
Store results with timestamps
- Maintain a small results table check_name, run_time, result, details so you can trend over time.
Use parameterized checks
- Make checks reusable by turning them into stored procedures or scripts that accept date ranges or table names.
Separate concerns
- Have a dedicated schema or folder for all data-quality checks to avoid mixing with production queries.
Document the checks
- Keep brief notes about why each check exists and what constitutes a pass.
Train teammates
- Share a starter library of checks and a few guardrails for common data issues.

Common pitfalls to avoid

Running expensive full-table scans during business hours
- If you must, limit to a small subset or sample to avoid performance impact.
Blindly trusting a single check
- Combine multiple checks to cover schema, data quality, and referential integrity.
Ignoring environment drift
- Don’t assume dev and prod data behave the same—document expected differences and verify them.
Overcomplicating simple checks
- Start with straightforward queries. only add complexity when a basic check passes consistently.

Performance and monitoring: how to keep checks fast

Use indexed columns in WHERE clauses and JOINs where possible
Schedule checks during off-peak windows or use read-replica environments if available
Break large checks into smaller batches and aggregate results
Collect and review query plans to optimize expensive scans
Use SET STATISTICS IO ON temporarily to diagnose I/O hotspots during checks

Data checks as part of a data governance routine

Checks aren’t just about catching errors. they serve as a governance layer that helps you prove data quality to stakeholders. Build a simple governance cadence:

Daily quick checks row counts, nulls, basic ranges
Weekly deeper checks duplicates, referential integrity, business-rule tests
Monthly audits full data profiling, distribution checks, schema drift
Quarterly reviews cleanup of deprecated columns, archival strategies

These steps align with typical data governance cycles and help ensure data remains trustworthy for dashboards, reports, and analytics. Discover the simple way to get the dns server through cmd: Quick Windows DNS lookup with ipconfig /all, nslookup, and tips 2026

How to get started fast: a starter checklist

Identify the top 5 critical columns that must be clean not null, reasonable values, correct types
Write a small set of checks for those columns
Create a shared repository Git or similar to store the scripts
Schedule a nightly run and route alerts to your team
Review results in a lightweight dashboard or email summary

If you’re just starting out, focus on a small, repeatable set of checks—you’ll quickly learn where your biggest pain points are and scale from there.

Frequently asked questions

What is the simplest way to check data in sql server?

Run a quick SELECT TOP 100 * FROM your_table to preview sample data, then add simple validations for nulls, ranges, and duplicates.

How do I preview data quickly in SQL Server?

Use a small, representative sample with ORDER BY NEWID to get random rows:
SELECT TOP 100 * FROM . ORDER BY NEWID.

How can I verify data types and NULLs in a table?

Query the data and inspect types with sample rows:
SELECT TOP 100 , , CAST AS VARCHAR50 FROM ..
Then run counts for NULLs in important columns:
SELECT COUNT* AS nulls_col FROM . WHERE IS NULL.

How do I find duplicates in a table?

Group by the columns of interest and filter by HAVING COUNT > 1:
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How can I check for orphaned foreign keys?

Left join the child table to the parent and filter NULL parent keys:
SELECT c. FROM . c LEFT JOIN . p ON c. = p. WHERE p. IS NULL.

What are good checks for date ranges?

Compare dates to a sensible window, for example:
SELECT COUNT* FROM . WHERE > GETDATE + INTERVAL ‘1 year’.
Adapt to T-SQL syntax: use DATEADD or similar based on your SQL Server version.

How can I verify data consistency across environments?

Use row counts and checksums to compare datasets, then validate key aggregates and sample distributions:
SELECT CHECKSUM_AGGBINARY_CHECKSUM, , AS checksum FROM ..

Which tools help with data quality in SQL Server?

SSMS or Azure Data Studio for ad hoc checks, SQL Server Agent for automation, and optional third-party data-quality tools for profiling and rule management.

How do I automate data checks in SQL Server?

Create SQL Server Agent jobs that run your checks nightly or on demand, write results to a log table, and trigger alerts if a check fails. Discover the fastest and most reliable dns servers with nslookup: Benchmark Latency and Reliability 2026

Are there performance tips when running checks on large tables?

Yes—limit scans to relevant partitions or date ranges, add appropriate indexes, run checks in off-peak windows, and batch large queries to avoid locking and long runtimes.

How do I organize checks for a data pipeline?

Treat checks as modular components: 1 schema sanity, 2 data-quality rules, 3 referential integrity, 4 performance and load metrics. Store scripts in a centralized repo and run them as part of your ETL jobs.

Can I do checks without heavy SQL?

Yes. Start with simple SQL queries, then supplement with lightweight data profiling or automated dashboards that summarize key metrics over time.

What is a good starter set of checks for a new project?

Row counts by table, NULL counts for critical columns, a duplicate check on primary keys or business keys, range checks on numeric fields, and a basic referential integrity test.

How should I handle false positives in checks?

Log the context and examine edge cases. Adjust thresholds, consider data latency, and partition the data to ensure checks reflect real-world usage rather than transient states. Discover the DNS Server IP on Linux a Step by Step Guide to Find DNS Addresses and Verify Connectivity 2026

How do I document data checks for teammates?

Create a simple README in your checks repo with descriptions, sample queries, expected results, and run schedules. Include notes on any exceptions or known data quirks.

What’s the best way to show data checks to stakeholders?

Prepare a lightweight dashboard or a daily email with summarized results pass/fail, counts, notable anomalies and a link to the full query results.

How often should data checks run in a typical project?

A practical cadence is nightly checks for ETL-driven data plus weekly deeper profiling and monthly audits, but adjust to your data freshness needs and regulatory requirements.

Final notes

The simplest way to check data in SQL Server is usually a mix of a quick visual sample plus a handful of targeted validations. Start with the fastest, most repeatable checks, then gradually expand to cover more complex rules and cross-environment comparisons. With a small, well-structured set of checks, you’ll catch most issues early, reduce debugging time, and keep your data pipelines healthy and trustworthy.

Sources:

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