diff --git a/CHANGELOG.md b/CHANGELOG.md
index 63e9791..e6eee74 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,10 @@ All notable changes to this project will be documented in this file.
    - config holder removed from jobs
    - metadata_manager and feature_loader are now available arguments, depending on configuration
    - added @config decorator, similar use case to @datasource, for parsing configuration
+   - reworked Resolver + Added ModuleRegister
+     - datasources no longer just by importing, thus are no longer available for all jobs
+     - register_dependency_callable and register_dependency_module added to register datasources
+     - together, it's now possilbe to have 2 datasources with the same name, but different implementations for 2 jobs.
    #### TableReader
    - function signatures changed
      - until -> date_until
diff --git a/README.md b/README.md
index f179dc6..6264534 100644
--- a/README.md
+++ b/README.md
@@ -372,7 +372,7 @@ With that sorted out, we can now provide a quick example of the *rialto.jobs* mo
 ```python
 from pyspark.sql import DataFrame
 from rialto.common import TableReader
-from rialto.jobs.decorators import config_parser, job, datasource
+from rialto.jobs import config_parser, job, datasource
 from rialto.runner.config_loader import PipelineConfig
 from pydantic import BaseModel
 
@@ -419,7 +419,6 @@ If you want to disable versioning of your job (adding package VERSION column to
 def my_job(...):
     ...
 ```
-
 These parameters can be used separately, or combined.
 
 ### Notes & Rules
@@ -435,6 +434,32 @@ This can be useful in **model training**.
 Finally, remember, that your jobs are still just *Rialto Transformations* internally.
 Meaning that at the end of the day, you should always read some data, do some operations on it and either return a pyspark DataFrame, or not return anything and let the framework return the placeholder one.
 
+
+### Importing / Registering Datasources
+Datasources required for a job (or another datasource) can be defined in a different module. 
+To register your module as a datasource, you can use the following functions:
+
+```python3
+from rialto.jobs import register_dependency_callable, register_dependency_module
+import my_package.my_datasources as md
+import my_package.my_datasources_big as big_md
+
+# Register an entire dependency module
+register_dependency_module(md)
+
+# Register a single datasource from a bigger module
+register_dependency_callable(big_md.sample_datasource)
+
+@job
+def my_job(my_datasource, sample_datasource: DataFrame, ...):
+    ...
+```
+
+Each job/datasource can only resolve datasources it has defined as dependencies.
+
+**NOTE**: While ```register_dependency_module``` only registers a module as available dependencies, the ```register_dependency_callable``` actually brings the datasource into the targed module - and thus becomes available for export in the dependency chains.
+
+
 ### Testing
 One of the main advantages of the jobs module is simplification of unit tests for your transformations. Rialto provides following tools: