diff --git a/docs/scalardb-samples/scalardb-analytics-spark-sample/README.mdx b/docs/scalardb-samples/scalardb-analytics-spark-sample/README.mdx
index 0b59ba06..f0b5c28e 100644
--- a/docs/scalardb-samples/scalardb-analytics-spark-sample/README.mdx
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@@ -8,7 +8,7 @@ tags:
 
 import WarningLicenseKeyContact from '/src/components/en-us/_warning-license-key-contact.mdx';
 
-This tutorial describes how to run analytical queries on sample data by using ScalarDB Analytics. The source code is available at https://github.com/scalar-labs/scalardb-samples/scalardb-analytics-spark-sample.
+This tutorial describes how to run analytical queries on sample data by using ScalarDB Analytics. The source code is available at [https://github.com/scalar-labs/scalardb-samples/tree/main/scalardb-analytics-spark-sample](https://github.com/scalar-labs/scalardb-samples/tree/main/scalardb-analytics-spark-sample).
 
 ScalarDB Analytics in its current version leverages Apache Spark as its execution engine. It provides a unified view of ScalarDB-managed and non-ScalarDB-managed data sources by using a Spark custom catalog. By using ScalarDB Analytics, you can treat tables from these data sources as native Spark tables. This allows you to execute arbitrary Spark SQL queries seamlessly. For example, you can join a table stored in Cassandra with a table in PostgreSQL to perform cross-database analysis with ease.