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@@ -54,76 +54,9 @@ There are a few environment variables that need to be set so that the applicatio
 
 # Pipeline architectures
 
-## Celery-based pipeline
-![Celery-based pipeline architecture](/images/celery_pipeline.png "Celery-based pipeline architecture")
-
 ## Thread-based pipeline
 ![Thread-based pipeline architecture](/images/thread_pipeline.png "Thread-based pipeline architecture")
 
-# How to use the celery-based pipeline
-
-## 1. Start RabbitMQ locally (Optional)
-
-Set up a local instance of RabbitMQ using Docker:
-
-```bash
-docker pull rabbitmq:3-management
-docker run --rm -it -p 15672:15672 -p 5672:5672  rabbitmq:3-management
-```
-
-The rabbitMQ management interface can be access on the url http://localhost:15672
-
-
-NOTE: If RabbitMQ is run locally, set the BROKER_URL env variable to amqp://guest:guest@localhost. The default username and password are uest.
-
-
-## 2. Start a celery worker
-
-Start a celery worker:
-
-```bash
-python -m celery -A main worker --loglevel=INFO -n ETLPipeline@%h
-```
-
-Alternatively, run the customisable `start_celery_worker.sh` script:
-
-```bash
-./start_celery_worker.sh
-```
-
-## 4. Run the ETL data pipeline
-
-Run one of the examples in the examples directory, e.g.:
-
-```bash
-python -u examples/example_celery.py
-```
-
-## 4. Monitor a Celery cluster with Flower (Optional)
-
-Install Flower using pip:
-
-```bash
-pip install flower
-```
-
-Launch the Flower server at specified port (default is 5555, so `--port=5555` can be ommited):
-
-```
-python -m celery -A main flower --port=5555
-```
-
-Alternatively, run Flower via docker:
-
-```
-docker run -p 5555:5555 mher/flower
-```
-
-Access Flower on the url http://localhost:5555/
-
-
-
-
 ## Filesystems
 
 ### Credentials to access the object store (.json file)