Chore/docs updates (#577)

* Update wording about multiple topics processing

* Mention connectors APIs in README

Author: daniil-quix

README.md

@@ -13,11 +13,13 @@
 Quix Streams is a cloud-native library for processing data in Kafka using pure Python. It’s designed to give you the power of a distributed system in a lightweight library by combining Kafka's low-level scalability and resiliency features with an easy-to-use Python interface (to ease newcomers to stream processing). 
 
 It has the following benefits:
+
 - Streaming DataFrame API (similar to pandas DataFrame) for tabular data transformations.
 - Custom stateful operations via a state object.
 - Custom reducing and aggregating over tumbling and hopping time windows.
 - Exactly-once processing semantics via Kafka transactions.
-- Pure Python with no need for a server-side engine.  
+- Sources and Sinks APIs to connect external data to Kafka.
+- Pure Python with no need for a server-side engine.
 
 Use Quix Streams to build simple Kafka producer/consumer applications or leverage stream processing to build complex event-driven systems, real-time data pipelines and AI/ML products.
 

docs/consuming-multiple-topics.md

@@ -84,11 +84,12 @@ As a reminder, state is ultimately tied to a given topic (and thus its `SDF`).
 
 ### Multiple Topics: NOT parallel
 
-Though multiple `SDF`s are involved with multiple topics, they do not run in parallel: 
-it simply subscribes the `Application`s underlying consumer to all the `SDF`'s topics.
+Though multiple `StreamingDataFrame`s are involved with multiple topics, they do not run _in parallel_:
 
-So, more topics being processed will directly affect the processing time of each given 
-topic.
+- The `Application` instance always has a single consumer, which reads messages one-by-one from multiple topics.
+- After the message is consumed from the topic, it is routed to the corresponding `StreamingDataFrame` responsible for the processing of this topic. 
+
+Processing multiple topics directly affects the throughput for each topic because more messages will be processed using the same resources. 
 
 
 ## Upcoming Features