diff --git a/advocacy_docs/edb-postgres-ai/analytics/index.mdx b/advocacy_docs/edb-postgres-ai/analytics/index.mdx
index b1e90b64587..8357cae8cd8 100644
--- a/advocacy_docs/edb-postgres-ai/analytics/index.mdx
+++ b/advocacy_docs/edb-postgres-ai/analytics/index.mdx
@@ -1,19 +1,47 @@
 ---
-title: Lakehouse analytics
-navTitle: Lakehouse analytics
+title: Analytics Hub
+navTitle: Analytics Hub
 indexCards: simple
 iconName: Improve
-description: How EDB Postgres Lakehouse extends the power of Postgres by adding a vectorized query engine and separating storage from compute, to handle analytical workloads.  
-directoryDefaults:
-  displayBanner: "Notice: Since EDB Hosted services have been removed from the Cloud Service, Lakehouse capabilities are now only available as part of the EDB Postgres AI Hybrid Control Plane, which is currently in tech preview."
+description: Gathering together all the information about Analytics, from Tiered Tables in PGD to Catalogs and HCP  
 navigation:
 - concepts
-- quick_start
-- external_tables
-- how_to_lakehouse_sync
+- storagelocations
+- catalogs
+- analytics_engine
+- managed_lakehouse
+- pgd_tiered_tables
+
 - reference
 ---
 
+EDB delivers Analytics capabilities for Postgres, enabling you to run analytical queries over large datasets and more. 
+And EDB Analytics lets you do it all in the Postgres ecosystem wherever you need it.
+
+## Concepts
+
+- **Why?**: The need for analytics in Postgres arises from the growing demand for data-driven decision-making and the need to analyze large datasets efficiently.
+- **Analytics Engine**: A vectorized SQL query engine that executes analytical queries over columnar data in object storage, built on Apache DataFusion and optimized for performance.
+- **Lakehouse**: A data architecture that combines the best of data lakes and data warehouses, allowing you to store and analyze data in a single platform.
+- **Storage locations**: The physical or logical locations where data is stored, such as S3 buckets or on-premises storage systems.
+- **Catalog**: A metadata repository that stores information about the data stored in a Lakehouse, including table definitions, schemas, and data locations.
+- **Tiered Tables**: A feature of EDB Postgres Distributed (PGD) that allows you to store data in different storage locations based on its usage patterns, optimizing performance and cost.
+
+## Use cases
+
+- *Read-only analytics without a Catalog*
+  - "I need to run analytical queries over S3 delta tables and I want to use Postgres"
+  - "I need to run analytical queries over Iceberg data and I want to use Postgres"
+- *Read-write analytics without a Catalog*
+  - "I need to offload data to S3 while keeping it available for analytics"
+  - "I need to offload tables data to S3 while keeping it available for queries and analytics"
+- *Read-write analytics with a Catalog*
+  - "I want to read and write data to a Catalog"
+- *Lakehouse read-only analytics*
+  - "I need a Managed Lakehouse to read and analyze data stored as Delta Tables, Iceberg or a Catalog"
+
+
+<!-- 
 EDB Postgres Lakehouse extends the power of Postgres to analytical workloads,
 by adding a vectorized query engine and separating storage from compute. Building
 a data Lakehouse has never been easier: just use Postgres.
@@ -52,26 +80,4 @@ Postgres, while still falling back to native execution when necessary.
 Postgres Lakehouse is optimized to query "Lakehouse Tables" in object storage,
 extending the power of open source database to open table formats. Currently,
 it supports querying "Delta Tables" stored according to the Delta Lake protocol.
-
-## Lakehouse Sync
-
-You can sync your own data from tables in transactional sources (initially, EDB
-Postgres® AI Cloud Service databases) into Lakehouse Tables in Storage Locations
-(initially, managed locations in S3 object storage).
-
-## Fully managed service
-
-You can launch Postgres Lakehouse nodes using the EDB Postgres AI Cloud
-Service (formerly EDB BigAnimal). Point a Lakehouse node at a storage bucket
-with some Delta Tables in it, and get results of analytical (OLAP) queries in
-less time than if you queried the same data in a transactional Postgres database.
-
-Postgres Lakehouse nodes are available now for customers using
-EDB Postgres AI - Hosted environments on AWS, and will be rolling out
-to additional cloud environments soon.
-
-## Try it today
-
-It's easy to start using Postgres Lakehouse. Provision a Lakehouse node in five
-minutes, and start querying pre-loaded benchmark data like TPC-H, TPC-DS,
-Clickbench, and the 1 Billion Row challenge.
+ -->
diff --git a/src/pages/index.js b/src/pages/index.js
index 0b455c4443e..6eaebdabb17 100644
--- a/src/pages/index.js
+++ b/src/pages/index.js
@@ -266,6 +266,31 @@ const Page = () => {
                 PostgreSQL
               </BannerCardLink>
             </BannerSubCard>
+            <BannerSubCard
+              iconName={iconNames.IMPROVE}
+              headingText="Analytics Accelerator"
+              to="/edb-postgres-ai/analytics"
+            >
+              <BannerCardLink to="/edb-postgres-ai/analytics/concepts/">
+                Concepts
+              </BannerCardLink>
+
+              <BannerCardLink to="/edb-postgres-ai/analytics/quickstart/">
+                Quick Start
+              </BannerCardLink>
+
+              <BannerCardLink to="/edb-postgres-ai/analytics/use_cases/">
+                Use Cases
+              </BannerCardLink>
+
+              <BannerCardLink to="/edb-postgres-ai/analytics/catalogs/">
+                Catalogs
+              </BannerCardLink>
+
+              <BannerCardLink to="/edb-postgres-ai/analytics/reference/">
+                Reference
+              </BannerCardLink>
+            </BannerSubCard>
 
             <BannerSubCard
               iconName={iconNames.BRAIN_CIRCUIT}
@@ -288,6 +313,7 @@ const Page = () => {
                 PGvector
               </BannerCardLink>
             </BannerSubCard>
+
             <BannerSubCard
               iconName={iconNames.CONTROL}
               headingText="Console"
@@ -348,28 +374,6 @@ const Page = () => {
               </BannerCardLink>
             </BannerSubCard>
 
-            <BannerSubCard
-              iconName={iconNames.IMPROVE}
-              headingText="Lakehouse Analytics"
-              to="/edb-postgres-ai/analytics"
-            >
-              <BannerCardLink to="/edb-postgres-ai/analytics/concepts/">
-                Concepts
-              </BannerCardLink>
-
-              <BannerCardLink to="/edb-postgres-ai/analytics/quick_start/">
-                Quick Start
-              </BannerCardLink>
-
-              <BannerCardLink to="/edb-postgres-ai/analytics/external_tables/">
-                External Tables
-              </BannerCardLink>
-
-              <BannerCardLink to="/edb-postgres-ai/analytics/reference/">
-                Reference
-              </BannerCardLink>
-            </BannerSubCard>
-
             <BannerWideCard>
               <BannerWideCardLink
                 className="col-md-6"