chore: finalize doc for 0.7 release (#472)

Signed-off-by: Vigith Maurice <vigith@gmail.com>

README.md

@@ -37,7 +37,8 @@ full-featured stream processing platforms.
 
 ## Roadmap
 
-- Data aggregation (e.g. group-by)
+- User Defined Transformer at Source for better deserialization and filter for cost reduction (v0.8)
+- Multi partitioned edges for higher throughput (v0.9)
 
 ## Resources
 

docs/README.md

@@ -28,7 +28,8 @@ stream processing platforms.
 
 ## Roadmap
 
-- Data aggregation (e.g. group-by)
+- User Defined Transformer at Source for better deserialization and filter for cost reduction (v0.8)
+- Multi partitioned edges for higher throughput (v0.9)
 
 ## Getting Started
 

docs/quick-start.md

@@ -122,8 +122,14 @@ The pipeline can be deleted by
 kubectl delete -f https://raw.githubusercontent.com/numaproj/numaflow/stable/test/e2e/testdata/even-odd.yaml
 ```
 
+## A pipeline with reduce (aggregation)
+
+[Reduce Examples](user-guide/user-defined-functions/reduce/examples.md)
+
 ## What's Next
 
 Try more examples in the [`examples`](https://github.com/numaproj/numaflow/tree/main/examples) directory.
 
-After exploring how Numaflow pipeline run, you can check what data [Sources](./user-guide/sources/generator.md) and [Sinks](./user-guide/sinks/kafka.md) Numaflow supports out of the box, or learn how to write [User Defined Functions](user-guide/user-defined-functions/map/map.md).
+After exploring how Numaflow pipeline run, you can check what data [Sources](./user-guide/sources/generator.md) 
+and [Sinks](./user-guide/sinks/kafka.md) Numaflow supports out of the box, or learn how to write 
+[User Defined Functions](user-guide/user-defined-functions/user-defined-functions.md).

docs/user-guide/user-defined-functions/reduce/examples.md

@@ -1,5 +1,7 @@
 # Reduce Examples
 
+Please read [reduce](./reduce.md) to get the best out of these examples.
+
 ## Prerequisites
 
 Install the ISB
@@ -139,4 +141,4 @@ and 10, the output from the first reduce vertex will be 25 (5 * 5) and 50 (5 * 1
 to the next non-keyed reduce vertex with the fixed window duration of 10s. This being a non-keyed, it will
 combine the inputs and produce the output of 150(25 * 2 + 50 * 2), which will be passed to the reduce
 vertex with a sliding window of duration 60s and with the slide duration of 10s. Hence the final output
-will be 900(150 * 6).
+will be 900(150 * 6).

docs/user-guide/user-defined-functions/reduce/reduce.md

@@ -1,5 +1,7 @@
 # Reduce UDF
 
+## Overview 
+
 Reduce is one of the most commonly used abstractions in a stream processing pipeline to define 
 aggregation functions on a stream of data. It is the reduce feature that helps us solve problems like 
 "performs a summary operation(such as counting the number of occurrence of a key, yielding user login 
@@ -9,6 +11,7 @@ bounding condition is "time", eg, "number of users logged in per minute". So whi
 unbounded stream of data, we need a way to group elements into finite chunks using time. To build these
 chunks the reduce function is applied to the set of records produced using the concept of [windowing](./windowing/windowing.md).
 
+## Reduce Pseudo code
 Unlike in _map_ vertex where only an element is given to user-defined function, in _reduce_ since
 there is a group of elements, an iterator is passed to the reduce function. The following is a generic
 outlook of a reduce function. I have written the pseudo-code using the accumulator to show that very
@@ -30,6 +33,8 @@ def reduceFn(key: str, datums: Iterator[Datum], md: Metadata) -> Messages:
     return Messages(Message.to_vtx(key, acumulator.result()))
 ```
 
+## Specification
+
 The structure for defining a reduce vertex is as follows.
 ```yaml
 - name: my-reduce-udf

docs/user-guide/user-defined-functions/reduce/windowing/fixed.md

@@ -1,5 +1,7 @@
 # Fixed
 
+## Overview
+
 Fixed windows (sometimes called tumbling windows) are defined by a static window size, e.g. 30 second
 windows, one minute windows, etc. They are generally aligned, i.e. every window applies across all
 the data for the corresponding period of time. It has a fixed size measured in time and does not
@@ -24,7 +26,7 @@ vertices:
 NOTE: A duration string is a possibly signed sequence of decimal numbers, each with optional fraction
 and a unit suffix, such as "300ms", "1.5h" or "2h45m". Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h".
 
-## Length
+### Length
 
 The `length` is the window size of the fixed window.
 

docs/user-guide/user-defined-functions/reduce/windowing/sliding.md

@@ -1,5 +1,7 @@
 # Sliding
 
+## Overview
+
 Sliding windows are similar to Fixed windows, the size of the windows is measured in time and is fixed.
 The important difference from the Fixed window is the fact that it allows an element to be present in
 more than one window. The additional window slide parameter controls how frequently a sliding window
@@ -22,11 +24,11 @@ vertices:
 NOTE: A duration string is a possibly signed sequence of decimal numbers, each with optional fraction
 and a unit suffix, such as "300ms", "1.5h" or "2h45m". Valid time units are "ns", "us" (or "µs"), "ms", "s", "m", "h".
 
-## Length
+### Length
 
 The `length` is the window size of the fixed window.
 
-## Slide
+### Slide
 
 `slide` is the slide parameter that controls the frequency at which the sliding window is created.
 

docs/user-guide/user-defined-functions/reduce/windowing/windowing.md

@@ -1,5 +1,7 @@
 # Windowing
 
+## Overview
+
 In the world of data processing on an unbounded stream, Windowing is a concept
 of grouping data using temporal boundaries. We use event-time to discover
 temporal boundaries on an unbounded, infinite stream and [Watermark](../../../watermarks.md) to ensure