unifie

docs/get-started/overview.md

@@ -18,7 +18,7 @@ Opteryx is not intended to replace databases like SQL Server, MySQL, or Postgres
 2. **Cloud-Native Data Processing** - Opteryx is designed to run in low-cost, serverless environments like Google Cloud Run. This makes it a great choice for organizations that want to minimize infrastructure costs while still being able to query large datasets.
 3. **Data Warehousing** - Opteryx allows you to query data directly in the systems where it is stored, eliminating the need to duplicate data into a common store for analytics. This can save you the cost and effort of maintaining duplicates, and is a good option for data warehousing use cases.
 4. **Python-Based Data Processing** - Opteryx is written in Python and can be easily integrated into Python code, including Jupyter Notebooks. This makes it a good choice for data processing and analytics workflows that are already written in Python.
-5. **Consolidating Backend Systems** - Opteryx creates a common SQL-layer over multiple data platforms, allowing you to upgrade, migrate, or consolidate backend systems without changing any Opteryx code. This makes it a good choice for organizations that want to streamline their data infrastructure.
+5. **Unified Access to Backend Systems** - Opteryx creates a common SQL-layer over multiple data platforms, allowing you to upgrade, migrate, or consolidate backend systems without changing any Opteryx code. This makes it a good choice for organizations that want to streamline their data infrastructure.
 
 Opteryx supports accessing multiple database backends, each with their own authentication and access controls. This capability allows Opteryx to serve as a unified access tool for external databases, simplifying challenges such as shifting data schemas and inconsistent API behaviors.