diff --git a/README.md b/README.md
index 1ab6d13..13d7f7e 100644
--- a/README.md
+++ b/README.md
@@ -4,12 +4,11 @@ ETL process to:
  + Extract data about 273 Currencies exchange rates from a public API  
  + Organize, transform and store data in two tables (Dollar and Euro based rates) in a SQLite DB
  + Generate a Customized Excel report (decision-making tool)
- + Write unit tests to ensure data quality and availability
  + Orchestrate a job with Airflow to recurrently run all steps
 
 # Step 1: ETL
 
-Data will be Extract from a public API https://github.com/fawazahmed0/currency-api. The API return a daily updated json with all the exchange rates for the for the selected base currency and date.
+Data will be Extract from a public API https://github.com/fawazahmed0/exchange-api/tree/main. The API return a daily updated json with all the exchange rates for the for the selected base currency and date.
 
 The script [update_currency_exchange.py](update_currency_exchange.py) is responsible for the whole ETL process. In short, this script contains functions to:
     
@@ -34,23 +33,14 @@ it's easier to show than to describe:
 ![png](readme_files/report_print.PNG)
 
 
-# Step 3: Unit Tests
+# Step 3: Orchestrate a Job/ Run the Pipeline
 
-Although I'm calling this the "step 3" (just to keep the logical order) it will be the first step to be executed in the pipeline. 
+There are two ways to run the pipeline responsible for all stages of the process: 
 
-The scrip [test_api_input.py](test_api_input.py) you will run a few tests and save a txt log file in the folder [log](log). Tests are checking:
-+ API connection 
-+ DB connection 
-+ API latest date (test if API is being updated)
-+ Quantity of currencies the API returned, test id there are new currencies available
+The first and simplest is through the script [main.py](main.py) , running it from the CLI or from Docker [run.sh](run.sh) will execute all the steps in the pipeline.
 
-# Step 4: Orchestrate a Job/ Run the Pipeline
+The second is option is to orchestrate a job with Airflow. the DAG [dag_currency_exchange_etl.py](src/airflow/dag_currency_exchange_etl.py) will also run all the steps in the pipeline, it will only be necessary to have an active Airflow server. 
 
-There are two ways to run the pipeline responsible for the three stages of the process (Unit Tests, ETL, Excel report) 
-
-The first and simplest is through the script [main.py](main.py) , running it from the CLI will execute all the steps in the pipeline.
-
-The second is option is to orchestrate a job with Airflow. the DAG [dag_currency_exchange_etl.py](dag_currency_exchange_etl.py) will also run all the steps in the pipeline, it will only be necessary to have an active Airflow server.
 
 # Usage 
 
@@ -67,7 +57,37 @@ Linting:
 ./run_linting.sh 
 ```
 
-Tests:
+Unit tests:
 ```shell
 ./run_unit_tests.sh 
-```
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+```
+Integration tests:
+```shell
+./run_integration_tests.sh 
+```
+
+
+# Structure
+
+```bash
+├── coverage
+├── readme_files
+├── src
+│   ├── airflow
+│   ├── database
+│   ├── modules
+│   └── reports
+└── tests
+    ├── integration
+    └── unit
+        └── sample_data
+ ```
+
+- `coverage` (not present in Github) is created when you run unit tests, and contains HTML for the code coverage. Specifically, open `coverage/index.html` to view the code coverage. 100% coverage is required.
+- [readme_files](readme_files) Images used in readme.
+- [src](src) Contains the application code.
+- [src/airflow](src/airflow/) DAG file to run the app through Airflow.
+- [src/database](src/database) SQLLite DB.
+- [src/modules](src/pipeline/) Modules to run the ETL pipeline and generate the Excel report.
+- [src/reports](src/reports/) Contains the generated Excel reports.
+- [tests](tests) unit tests, integration tests and data samples.
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