Update readme

README.md

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 # Project "Pretty Panda"
 
 ## Goal
-This repository is a collection of independent loosely coupled components to enable developers to efficiently prototype
-geospatial processing locally. Once the processing steps to create a new dataset work
-in the local environment, it should be straight-forward to bring them in a
-production environment for periodic batch execution.
+This repository is a playground to develop a collection of loosely coupled components
+which...
+1. enable developers to efficiently perform geospatial processing locally through a containerized environment.
+2. demonstrate how to interact with large amounts of static geospatial data in low cost blob storage.
+3. provide a framework to enable periodic batch processing of geospatial workflows.
 
-## Design requirements
-- Allow flexible and rapid local prototyping involving spatial processing.
-- Store third party datasets and processing results in an organized manner.
-- Allow for periodic batch re-processing of the data.
-- Leverage Google Cloud infrastructure if possible.
-- Low cost as a priority requirement.
+## Case study
+Open data from Switzerland will be used for demonstration purposes.
 
-## Components
-### Data store
-We leverage Cloud Storage as cost-efficient storage for geospatial data. We leverage cloud-native data formats where possible.
-
-### Processing environment
-Various processing capabilities are bundled using container technology. This enables consistency across development and production workflows.
-Priotity is given to the Python Geo-Ecosystem, followed by pyQGIS and R capabilities.
-
-### Local development
-We leverage Jupyterlab as a UI for development due to its integrated visualization capabilities and extensive plugin system.
-
-### Batch processing
-We leverage Google Batch to provide the infrastructure for batch runs.
-
-### Orchestration
-Use Cloud Scheduler to trigger the batch jobs.
+## Design guidelines
+- Optimize for cost efficiency for usecases where small to medium sized static, read-only datasets are produced through geospatial workflows through a low frequency batch process.
+- Leverage Google Cloud infrastructure where possible.
+- Allow multiple developers to efficiently perform exploratory analysis on stored datasets.