diff --git a/docs/building_blocks/data_sources/data_velocity.md b/docs/building_blocks/data_sources/data_velocity.md
index fac4f48f6..213eea629 100644
--- a/docs/building_blocks/data_sources/data_velocity.md
+++ b/docs/building_blocks/data_sources/data_velocity.md
@@ -30,7 +30,7 @@ The choice of data processing velocity is pivotal in determining the kind of dat
 |**Example Data**| Social media feeds, stock market transactions, sensor readings, clickstream data, ad requests and responses. A credit card company aiming to detect fraudulent transactions in real-time benefits from streaming data. Real-time detection can prevent financial losses and protect customers from fraudulent activities.|
 |**Properties**| Stream processing handles data in real-time, making it highly dynamic. It's designed to support immediate updates and changes, making it ideal for use cases that require up-to-the-second insights.|
 |**Formats**| Data in stream processing is often in Protobuf, Avro, or other formats optimized for small footprint and fast serialization.|
-|**Databases**| Real-time databases include [Clickhouse](https://clickhouse.com/), [Redis](https://redis.com/), and [RethinkDB](https://rethinkdb.com/). There are also in-memory databases, such as [DuckDB](https://duckdb.org/) and [KuzuDB](https://kuzudb.com/), which can be used to create real-time dashboards. However, depending on the deployment strategy chosen, these databases may lose the data once the application is terminated.|
+|**Databases**| Real-time databases include [Clickhouse](https://clickhouse.com/), [Redis](https://redis.io/), and [RethinkDB](https://rethinkdb.com/). There are also in-memory databases, such as [DuckDB](https://duckdb.org/) and [KuzuDB](https://kuzudb.com/), which can be used to create real-time dashboards. However, depending on the deployment strategy chosen, these databases may lose the data once the application is terminated.|
 
 Most systems deployed in production at scale combine stream and batch processing. This enables you to leverage the immediacy of real-time updates and the depth of historical data. But reconciling stream and batch processes in a single system introduces trade-off decisions – trade-off decisions you must make to keep your data consistent across systems and across time.
 
diff --git a/docs/tools/vdb_table/data/redis.json b/docs/tools/vdb_table/data/redis.json
index bbd2279ad..6d2b94661 100644
--- a/docs/tools/vdb_table/data/redis.json
+++ b/docs/tools/vdb_table/data/redis.json
@@ -3,7 +3,7 @@
   "links": {
     "docs": "https://redis.io/docs/get-started/vector-database/",
     "github": "https://github.com/RediSearch/RediSearch",
-    "website": "https://redis.com/solutions/use-cases/vector-database/",
+    "website": "https://redis.io/solutions/vector-database/",
     "vendor_discussion": "https://github.com/superlinked/VectorHub/discussions/81",
     "poc_github": "https://github.com/adrianoamaral",
     "slug": "redis"
@@ -32,9 +32,9 @@
     "comment": ""
   },
   "hybrid_search": {
-    "support": "",
-    "source_url": "",
-    "comment": ""
+    "support": "full",
+    "source_url": "https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/02_hybrid_search.ipynb",
+    "comment": "Supported using the aggregation api."
   },
   "facets": {
     "support": "full",