Merge pull request #1 from databrickslabs/main

Added links to examples in the readme (databrickslabs#171)

README.md

@@ -13,7 +13,7 @@ An extension to the [Apache Spark](https://spark.apache.org/) framework that all
 
 ## Why Mosaic?
 
-Mosaic was created to simplify the implementation of scalable geospatial data pipelines by bunding together common Open Source geospatial libraries via Apache Spark, with a set of [examples and best practices](notebooks/examples) for common geospatial use cases.
+Mosaic was created to simplify the implementation of scalable geospatial data pipelines by bounding together common Open Source geospatial libraries via Apache Spark, with a set of [examples and best practices](#examples) for common geospatial use cases.
 
 
 ## What does it provide?
@@ -43,6 +43,10 @@ Image1: Mosaic logical design.
 
 Create a Databricks cluster running __Databricks Runtime 10.0__ (or later).
 
+### Documentation
+
+Check out the [documentation pages](https://databrickslabs.github.io/mosaic/).
+
 ### Python
 
 Install [databricks-mosaic](https://pypi.org/project/databricks-mosaic/)
@@ -101,6 +105,16 @@ val mosaicContext = MosaicContext.build(H3, ESRI)
 mosaicContext.register(spark)
 ```
 
+
+## Examples
+
+| Example | Description | Links |
+| --- | --- | --- |
+| __Quick Start__ | Example of performing spatial point-in-polygon joins on the NYC Taxi dataset | [python](/notebooks/examples/python/QuickstartNotebook.py), [scala](notebooks/examples/scala/QuickstartNotebook.scala), [R](notebooks/examples/R/QuickstartNotebook.r), [SQL](notebooks/examples/sql/QuickstartNotebook.sql) | 
+| Open Street Maps | Ingesting and processing with Delta Live Tables the Open Street Maps dataset to extract buildings polygons and calculate aggregation statistics over H3 indexes | [python](notebooks/examples/python/OpenStreetMaps) |
+
+You can import those examples in Databricks workspace using [these instructions](https://docs.databricks.com/notebooks/notebooks-manage.html#import-a-notebook).
+
 ## Ecosystem
 Mosaic is intended to augment the existing system and unlock the potential by integrating spark, delta and 3rd party frameworks into the Lakehouse architecture.