Copy readme in index

README.md

@@ -21,15 +21,15 @@ in both realtime and batch jobs.
 ## Key Features
 
 
-* Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
-* Optimized for analytics, it uses micro-batching (instead of processing records one by one)
-* Similar to [incremental][3], it  updates nodes in a dag incrementally
-* Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
-  * **Stream:** ephemeral micro-batches of events (cleared after every cycle)
-  * **State:** durable state derived from streams
-* Clear separation between the business logic and the IO. 
+- Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
+- Optimized for analytics, it uses micro-batching (instead of processing records one by one)
+- Similar to [incremental][3], it  updates nodes in a dag incrementally
+- Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
+    * **Stream**: ephemeral micro-batches of events (cleared after every cycle)
+    * **State**: durable state derived from streams
+- Clear separation between the business logic and the IO. 
   So the same dag can be used in real time mode, replay mode or can be easily tested.
-* Functional interface: no inheritance or decorator required
+- Functional interface: no inheritance or decorator required
 
 
 [1]: https://github.com/tradewelltech/beavers

docs/index.md

@@ -1,4 +1,3 @@
-
 # Beavers
 
 [Beavers][1] is a python library for stream processing, optimized for analytics. 
@@ -10,15 +9,21 @@ in both realtime and batch jobs.
 ## Key Features
 
 
-* Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
-* Optimized for analytics, it uses micro-batching (instead of processing records one by one)
-* Similar to [incremental][3], it  updates nodes in a dag incrementally
-* Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
-  * **Stream:** ephemeral micro-batches of events (cleared after every cycle)
-  * **State:** durable state derived from streams
-* Clear separation between the business logic and the IO. 
+- Works in **real time** (eg: reading from kafka) and **replay mode** (eg: reading from parquet)
+- Optimized for analytics, it uses micro-batching (instead of processing records one by one)
+- Similar to [incremental][3], it  updates nodes in a dag incrementally
+- Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
+    * **Stream**: ephemeral micro-batches of events (cleared after every cycle)
+    * **State**: durable state derived from streams
+- Clear separation between the business logic and the IO. 
   So the same dag can be used in real time mode, replay mode or can be easily tested.
-* Functional interface: no inheritance or decorator required
+- Functional interface: no inheritance or decorator required
+
+
+[1]: https://github.com/tradewelltech/beavers
+[2]: https://www.tradewelltech.co/
+[3]: https://github.com/janestreet/incremental
+[4]: https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/
 
 
 [1]: https://github.com/tradewelltech/beavers

mkdocs.yml

@@ -32,6 +32,7 @@ plugins:
             show_source: false
 
 markdown_extensions:
+  - def_list
   - pymdownx.inlinehilite
   - pymdownx.superfences
   - pymdownx.snippets: