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To clean and analyze data with SQL, first profile the records, then define explicit rules for missing values, invalid values, and duplicates. Preview any proposed corrections before changing data, and use database constraints to prevent known invalid values from being written again. The examples below use PostgreSQL; check your database’s documentation before relying on syntax or edge-case behavior in another engine.
How do I clean and analyze data with SQL?
Work from inspection to decisions to enforcement. SQL can reveal anomalies and apply a rule you have chosen, but it cannot decide what counts as a valid value or which duplicate record is authoritative. Keep the original data recoverable while you establish those definitions.
- Identify the database and table grain. Confirm which database engine and version you use, what one row represents, and which columns should identify a record.
- Inspect representative rows and column types. Learn how values are stored and look for inconsistent formats or unexpected values before writing cleanup logic.
- Profile the data. Measure total rows, NULL counts, distinct values, and candidate duplicate keys. Treat these as signals to investigate, not proof that a value is wrong.
- Write the rules. Specify required fields, valid ranges, and how to select a canonical record when several rows share a key.
- Preview candidate changes with SELECT. Check exactly which rows would be corrected or excluded and why.
- Apply reviewed changes with a recovery plan. Use an appropriate backup or transaction plan before modifying source data.
- Validate afterward. Compare before-and-after counts and run checks for the rules you intended to enforce.
This sequence is a practical recommendation, not a workflow tested against a live database. Its central safeguard is to separate anomaly detection from the business decision and from the permanent rule.
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In PostgreSQL, NULL represents an absent or unknown value; it is not interchangeable with zero or an empty string. Decide what a missing value means for each column before filling, excluding, or aggregating it.
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Counts answer different questions
COUNT(*) counts selected rows, while COUNT(column_name) counts only rows where that column is not NULL. Use the former to count records and the latter to count records with a known value. PostgreSQL’s built-in aggregate functions generally ignore NULL inputs, so an aggregate over a column may summarize fewer values than the total number of rows.
SUM over no rows is NULL
In PostgreSQL, SUM over no selected rows returns NULL, not zero. If zero is the intended display or downstream value, state that choice explicitly with COALESCE, for example COALESCE(SUM(amount), 0). This changes the result to the fallback value; it does not establish that the underlying data contained zero-valued records.
Check missingness before adding a constraint
A PostgreSQL CHECK constraint passes when its expression evaluates to either true or NULL. Consequently, a check such as CHECK (quantity > 0) does not reject a NULL quantity. If the value must be present as well as positive, combine the rule with NOT NULL.
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How do I find and resolve duplicate records?
First define what makes two records duplicates: identical rows, or rows sharing a business key such as an account identifier. Then decide which record to keep. A duplicate key can reveal a problem, but it does not by itself say which row is correct.
Identifying repeated output is not record selection
PostgreSQL SELECT DISTINCT removes repeated rows from the query result. It does not delete records from the table, nor does it choose a canonical source record when rows differ in other columns.
PostgreSQL’s DISTINCT ON returns one row for each matching group. Without an ORDER BY that determines which row comes first, the chosen row is unpredictable. To make selection meaningful, order by the grouping key and a tie-breaker that expresses your rule—for example, keeping the latest timestamp, provided that is the agreed business rule.
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Separate detection, selection, and deletion
- Detect: identify keys with multiple rows and inspect the records in each group.
- Select: define a deterministic rule for the canonical record, including how ties are resolved.
- Change: preview the affected records before deleting or updating anything, then retain a recovery path.
Do not treat DISTINCT as a substitute for this policy. It changes query output; it does not establish which stored record should survive.
How can constraints prevent invalid data?
In PostgreSQL, constraints express rules at the schema level so that future writes can be rejected when they violate a correctly specified rule. They complement query-time cleanup: queries help inspect and analyze existing records, while constraints guard later writes.
NOT NULL: requires a value to be present.CHECK: requires a condition to pass, subject to the NULL behavior described above.UNIQUE: prevents duplicate constrained values under PostgreSQL’s default NULL behavior.- Primary key: identifies rows with a unique key.
- Foreign key: enforces a relationship to referenced rows.
PostgreSQL’s default UNIQUE behavior permits multiple rows with NULL in a constrained column because NULL values are treated as distinct for this purpose. If a field must both be unique and present, pair UNIQUE with NOT NULL. Confirm the intended rule before applying a constraint; a technically valid constraint can still encode the wrong business policy.
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Why does SQL query order matter for analysis?
PostgreSQL documents SELECT processing as a sequence that includes filtering, grouping and aggregate computation, result expressions, duplicate elimination, ordering, and limiting. That sequence affects what a query’s result means. For example, filtering rows before grouping changes which records contribute to an aggregate; limiting an ordered result changes which records are returned, not the underlying table.
When combining cleanup and analysis, state the stages deliberately: decide which records qualify, aggregate the intended population, then sort or limit the result. If a result depends on which row comes first, provide enough ordering to make that choice deterministic.
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Order-sensitive aggregates
Some aggregates depend on input order. PostgreSQL recommends specifying the input order when the order of the aggregate’s output matters; otherwise, do not assume that records will be combined in the desired sequence.
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What should I verify for another SQL database?
The behavior described here is grounded in official PostgreSQL documentation: PostgreSQL 18 documentation for constraints and SELECT behavior, and PostgreSQL 17 documentation for aggregate details. It does not establish that SQL Server, MySQL, SQLite, or another database behaves identically. Check the documentation for your engine and version for syntax and edge cases, especially NULL handling, unique constraints, duplicate selection, and aggregate behavior.
Use SQL to make data rules visible and repeatable: profile first, make decisions explicit, preview changes, and enforce well-defined rules where the database supports them. The correctness of the result still depends on choosing rules that fit the data’s purpose.
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