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Most of our integrations, including Snowflake, BigQuery, Redshift, dbt, Looker, PowerBI, Airflow, etc, use the SQL parser to generate column-level lineage and usage statistics.</p><p>If you’re using a different database system for which we don’t support column-level lineage out of the box, but you do have a database query log available, the <a href="/docs/generated/ingestion/sources/sql-queries">SQL queries</a> connector can generate column-level lineage and table/column usage statistics from the query log.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="sdk-support">SDK Support<a href="#sdk-support" class="hash-link" aria-label="Direct link to SDK Support" title="Direct link to SDK Support"></a></h2><p>Our SDK provides a <a href="/docs/python-sdk/clients/graph-client#datahub.ingestion.graph.client.DataHubGraph.parse_sql_lineage"><code>DataHubGraph.parse_sql_lineage()</code></a> method for programmatically parsing SQL queries.</p><p>The resulting object contains a <code>sql_parsing_result.debug_info.confidence_score</code> field, which is a 0-1 value indicating the confidence of the parser.</p><p>There are also a number of utilities in the <code>datahub.sql_parsing</code> module. The <code>SqlParsingAggregator</code> is particularly useful, as it can also resolve lineage across temp tables and table renames/swaps.
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Note that these utilities are not officially part of the DataHub SDK and hence do not have the same level of stability and support as the rest of the SDK.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="capabilities">Capabilities<a href="#capabilities" class="hash-link" aria-label="Direct link to Capabilities" title="Direct link to Capabilities"></a></h2><h3 class="anchor anchorWithStickyNavbar_LWe7" id="supported">Supported<a href="#supported" class="hash-link" aria-label="Direct link to Supported" title="Direct link to Supported"></a></h3><ul><li>Table-level lineage for <code>SELECT</code>, <code>CREATE</code>, <code>INSERT</code>, <code>UPDATE</code>, <code>DELETE</code>, and <code>MERGE</code> statements</li><li>Column-level lineage for <code>SELECT</code> (including <code>SELECT INTO</code>), <code>CREATE VIEW</code>, <code>CREATE TABLE AS SELECT</code> (CTAS), <code>INSERT</code>, and <code>UPDATE</code> statements</li><li>Subqueries</li><li>CTEs</li><li><code>UNION ALL</code> constructs - will merge lineage across the clauses of the <code>UNION</code></li><li><code>SELECT *</code> and similar expressions will automatically be expanded with the table schemas registered in DataHub. This includes support for platform instances.</li><li>Automatic handling for systems where table and column names are case insensitive. Generally requires that <code>convert_urns_to_lowercase</code> is enabled when the corresponding table schemas were ingested into DataHub.<ul><li>Specifically, we'll do fuzzy matching against the table names and schemas to resolve the correct URNs. We do not support having multiple tables/columns that only differ in casing.</li></ul></li><li>For BigQuery, sharded table suffixes will automatically be normalized. For example, <code>proj.dataset.table_20230616</code> will be normalized to <code>proj.dataset.table_yyyymmdd</code>. This matches the behavior of our BigQuery ingestion connector, and hence will result in lineage linking up correctly.</li></ul><h3 class="anchor anchorWithStickyNavbar_LWe7" id="not-supported">Not supported<a href="#not-supported" class="hash-link" aria-label="Direct link to Not supported" title="Direct link to Not supported"></a></h3><ul><li>Scalar <code>UDFs</code> - We will generate lineage pointing at the columns that are inputs to the UDF, but will not be able to understand the UDF itself.</li><li>Table-valued functions, including tabular <code>UDFs</code></li><li><code>json_extract</code> and similar functions</li><li><code>UNNEST</code> - We will do a best-effort job, but cannot reliably generate column-level lineage in the presence of <code>UNNEST</code> constructs.</li><li>Structs - We will do a best-effort attempt to resolve struct subfields, but it is not guaranteed. This will only impact column-level lineage.<ul><li>This extends to things like dynamic table unpacking e.g. <code>SELECT IF (main.id is not null, main, extras).* FROM my_schema.main_users main FULL JOIN my_schema.external_users extras USING (id)</code> in BigQuery.</li></ul></li><li>Snowflake's multi-table inserts</li><li>Multi-statement SQL / SQL scripting</li></ul><h3 class="anchor anchorWithStickyNavbar_LWe7" id="limitations">Limitations<a href="#limitations" class="hash-link" aria-label="Direct link to Limitations" title="Direct link to Limitations"></a></h3><ul><li>We only support the 20+ SQL dialects supported by the underlying <a href="https://github.com/tobymao/sqlglot" target="_blank" rel="noopener noreferrer">sqlglot</a> library.</li><li>There's a few SQL syntaxes that we don't support yet, but intend to support in the future.<ul><li><code>INSERT INTO (col1_new, col2_new) SELECT col1_old, col2_old FROM ...</code>. We only support <code>INSERT INTO</code> statements that either (1) don't specify a column list, or (2) specify a column list that matches the columns in the <code>SELECT</code> clause.</li><li><code>MERGE INTO</code> statements - We don't generate column-level lineage for these.</li></ul></li><li>In cases where the table schema information
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