diff --git a/data_platform_with_deep_indexed_data_and_staking_reports.md b/data_platform_with_deep_indexed_data_and_staking_reports.md
index eafffacc871..5f74a7a7382 100644
--- a/data_platform_with_deep_indexed_data_and_staking_reports.md
+++ b/data_platform_with_deep_indexed_data_and_staking_reports.md
@@ -92,7 +92,7 @@ UI module consist of forking existing BI tool with the ability:
 We also share sli/slo for indexer and our experience showed that currently it is the best solution according to data quality and flexible enough to support custom needs like staking. |
 |  | orchestration | airflow | python | flexible, popular ETL orchestration |
 | DWH | raw layer + domain layer | bigQuery | cloud sql | current solution is DWH agnostic but we are using bigQuery |
-| UI | forked BI tool | forking Superset | - backend: python
+| UI | forked BI tool | forking [Superset](https://github.com/apache/superset) | - backend: python
 - frontend: rest | - auth in the box
 - already has all functions to write, save, execute query and build dashboards
 - open-source
@@ -183,14 +183,14 @@ Current solution from [P2P.org](http://P2P.org) has already implemented:
 
 - mbelt indexer as open-source solution
 - private Polkadot datasets
-- private Superset without public user’s access and public auth
+- private [Superset](https://github.com/apache/superset) without public user’s access and public auth
 - dashboard with sli/slo for ETL of the platform
 
 We suggest proposal with 1 milestone to:
 
 1. copy Mbelt data (raw indexed data) to public
 2. provide public dataset about staking (example user’s domain data/reports)
-3. fork Superset for public usage
+3. fork [Superset](https://github.com/apache/superset) for public usage
 4. provide staking dashboards (example user’s domain data/reports)
 5. wrapping to end-2-end open-source solution: indexer deploy + orchestration + DWH connector + BI tool
 
@@ -215,7 +215,7 @@ We suggest proposal with 1 milestone to:
 | 0d. | Docker | We will provide a Dockerfile(s) that can be used to run indexer locally, set up data base (DWH), and run BI. |
 | 1. | Develop the ETL component | The result of developing ETL component contains the docker with Mbelt (indexer) and Airflow with DAGs to write indexer’s output into DWH (data base). |
 | 2. | Publish DWH data | Our goal for publishing data is: providing raw (indexer’s data) data into public bigQ project with the ability to query and use this data for public users. The chains are Polkadot, Kusama, EVM parachains. We would also provide reports about staking on top of raw data in chains above. The additional result is SLI/SLO dashboard of ETL process to provide more transparency for users. |
-| 3. | Forking Superset | Forking Superset will allow us to modify sign-in page to provide new users to join. We will make some changes on UI side to improve user’s experience such as: Main page modifications to make it dash-oriented, Dashboard tab & Chart tab modification to provide user-friendly filters, SQL lab tab modification to make it more useful to search for sources, User’s settings modification to exclude useless bottoms  |
+| 3. | Forking [Superset](https://github.com/apache/superset) | Forking Superset will allow us to modify sign-in page to provide new users to join. We will make some changes on UI side to improve user’s experience such as: Main page modifications to make it dash-oriented, Dashboard tab & Chart tab modification to provide user-friendly filters, SQL lab tab modification to make it more useful to search for sources, User’s settings modification to exclude useless bottoms  |
 
 
 ## **Future plans**