{"id":6767,"date":"2026-07-12T02:08:34","date_gmt":"2026-07-12T10:08:34","guid":{"rendered":"https:\/\/www.gudusoft.com\/vertica-data-lineage\/"},"modified":"2026-07-12T02:08:34","modified_gmt":"2026-07-12T10:08:34","slug":"vertica-data-lineage","status":"publish","type":"page","link":"https:\/\/www.gudusoft.com\/pt\/vertica-data-lineage\/","title":{"rendered":"Vertica Data Lineage: Column-Level Lineage from Projections and SQL"},"content":{"rendered":"

linhagem de dados Vertica<\/strong> is the column-level map of how data flows through your Vertica SQL: which source columns feed each target table, projection, and report, and which joins, filters, aggregates, and analytic functions transform the data along the way. Gudu SQLFlow<\/a> builds that map automatically with a dedicated Vertica dialect parser: it reads your SQL statically, resolves every column reference, and renders an interactive lineage diagram without ever touching the rows in your tables.<\/p>\n\n\n\n

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Experimente agora:<\/strong> paste any Vertica query into the visualizador de linhagem SQL online gratuito<\/a>, select the Vertica dialect, and get a column-level lineage diagram in seconds.<\/p>\n\n<\/div><\/div>\n\n\n\n

Why Vertica lineage is a SQL parsing problem<\/h2>\n\n\n\n

Vertica is unusual among analytical databases in how much of its physical design is expressed as SQL. A projection, Vertica’s core storage structure, is defined by a CREATE PROJECTION ... AS SELECT<\/code> statement. A flattened table denormalizes dimension attributes through DEFAULT USING<\/code> e SET USING<\/code> column expressions that are themselves queries against other tables. Gap-filling and interpolation over event data happens in the TIMESERIES<\/code> clause, right inside a SELECIONAR<\/code>.<\/p>\n\n\n\n

That design has a consequence for lineage: if a tool can genuinely parse Vertica’s dialect, it can recover the data flow: not just through your ETL scripts and views, but through the storage layer itself. If it can’t, it silently drops exactly the statements that make a Vertica estate a Vertica estate. Generic ANSI-grammar parsers choke on TIMESERIES<\/code>, treat projection definitions as noise, and never see the query hidden inside a SET USING<\/code> expression.<\/p>\n\n\n\n

SQLFlow ships a dialect-specific Vertica parser, one of 39 dialect parsers<\/a>, each with its own grammar rather than a shared ANSI approximation. It handles projections defined by SELECTs, flattened-table expressions, and TIMESERIES<\/code> clauses as first-class SQL, and it resolves column references through CTEs, subqueries, views, and SELECIONE *<\/code> expans\u00e3o.<\/p>\n\n\n\n

Column-level lineage from a real Vertica statement<\/h2>\n\n\n\n

Consider a typical Vertica pattern: an INSERIR ... SELECIONAR<\/code> that aggregates fact data and ranks it with an analytic function.<\/p>\n\n\n\n

INSERT INTO mart.region_daily_rank\n    (sale_date, region_name, total_amount, region_rank)\nSELECT\n    s.sale_date,\n    r.region_name,\n    SUM(s.amount)                                            AS total_amount,\n    RANK() OVER (PARTITION BY s.sale_date\n                 ORDER BY SUM(s.amount) DESC)                AS region_rank\nFROM   fact.sales   s\nJOIN   dim.regions  r ON s.region_id = r.region_id\nWHERE  s.sale_date >= '2026-01-01'\nGROUP  BY s.sale_date, r.region_name;<\/code><\/pre>\n\n\n\n
For every output column, SQLFlow identifies the source columns that feed it and the functions they pass through:<\/p>\n\n\n\n