Convert markdown links to JSX Link components in Flink and Spark tableData

- Converted [Hive] and [AWS Glue] markdown links to JSX Link components in Flink tableData
- Converted [Hive Metastore] and [AWS Glue] markdown links to JSX Link components in Spark tableData
- Added Link import to spark.mdx
- Links now properly render as clickable elements with blue-link styling

Author: Akshay-datazip

docs-iceberg-query-engine/flink.mdx

@@ -532,7 +532,11 @@ export const flinkTableData = {
         tooltip: "Support for all major catalog implementations"
       },
       details: { 
-        value: "[Hive](/docs/writers/iceberg/catalog/hive/), Hadoop, REST (incl. Nessie), [AWS Glue](/docs/writers/iceberg/catalog/glue/), JDBC, custom implementations",
+        value: (
+          <>
+            <Link to="/docs/writers/iceberg/catalog/hive/" className="blue-link">Hive</Link>, Hadoop, REST (incl. Nessie), <Link to="/docs/writers/iceberg/catalog/glue/" className="blue-link">AWS Glue</Link>, JDBC, custom implementations
+          </>
+        ),
         tooltip: "CREATE CATALOG statements with flexible catalog-type and catalog-impl options"
       },
       version: { value: "1.18+" }

docs-iceberg-query-engine/snowflake.mdx

@@ -480,7 +480,7 @@ export const snowflakeFeatures = [
     chip: "External Engine Access",
     description: (
       <>
-        UniForm exposes Snowflake tables through Iceberg-compatible REST catalog so external engines (Spark, <Link to="/iceberg/query-engine/trino" className="blue-link">Trino</Link>) can read them. Cross-cloud support via External Volumes
+        UniForm exposes Snowflake tables through Iceberg-compatible REST catalog so <Link to="/iceberg/query-engine/" className="blue-link">external engines</Link> (Spark, <Link to="/iceberg/query-engine/trino" className="blue-link">Trino</Link>) can read them. Cross-cloud support via External Volumes
       </>
     ),
     icon: <ArrowPathIcon className="w-6 h-6" />,
@@ -825,7 +825,11 @@ export const snowflakeUseCases = [
     description: "UniForm interoperability for diverse analytical tools",
     scenarios: [
       "Real-world example: A media company stores video analytics data in Snowflake Iceberg tables. Their data science team uses Snowflake SQL for business intelligence, while their ML engineers use Apache Spark (accessing via UniForm) for model training. Both teams work with the same data without ETL pipelines or data duplication, reducing costs and eliminating sync issues",
-      "Data sharing between Snowflake and [external engines (Spark, Trino)](/iceberg/query-engine/) without duplication",
+      (
+        <>
+          Data sharing between Snowflake and <Link to="/iceberg/query-engine/" className="blue-link">external engines (Spark, Trino)</Link> without duplication
+        </>
+      ),
       "Hybrid analytical architectures with multiple processing engines and tools",
       "Cross-cloud and cross-region data access scenarios with unified governance"
     ]

docs-iceberg-query-engine/spark.mdx

@@ -657,6 +657,7 @@ export const sparkUseCases = [
 
 
 import { QueryEngineLayout } from '@site/src/components/Iceberg/QueryEngineLayout';
+import Link from '@docusaurus/Link';
 import { 
   ServerStackIcon,
   // DatabaseIcon,
@@ -1171,7 +1172,11 @@ export const sparkTableData = {
         tooltip: "Support for all major catalog implementations"
       },
       details: { 
-        value: "[Hive Metastore](/docs/writers/iceberg/catalog/hive/), Hadoop warehouse, REST, [AWS Glue](/docs/writers/iceberg/catalog/glue/), JDBC, Nessie, custom plug-ins",
+        value: (
+          <>
+            <Link to="/docs/writers/iceberg/catalog/hive/" className="blue-link">Hive Metastore</Link>, Hadoop warehouse, REST, <Link to="/docs/writers/iceberg/catalog/glue/" className="blue-link">AWS Glue</Link>, JDBC, Nessie, custom plug-ins
+          </>
+        ),
         tooltip: "Most comprehensive catalog support in the ecosystem"
       },
       version: { value: "3.0+" }