Add new tutorial

README.md

@@ -6,13 +6,13 @@ Zero/Low code based business model representation automation
 
 Kolle is for working on data models, data-contract, data quality, data profiling, and data linage instead of technical tooling.
 
-Today for business continuation, the business model needs to represent in many ways normalized form for transactional data, a time-series database for process mining, a knowledge graph for semantics search or link data, a data-vault or snowflake model for data warehouse, a streaming model for the real-time event and columnar storage for machine learning. To move or prepare the data and model for multiple types of consumption is not only expensive but has a lot of repetition costs for the team and technology setup. Automation needs to be in place to reduce repetition costs.
+Today for business continuation, the business model needs to represent in many ways - normalized form for transactional data, a time-series database for process mining, a knowledge graph for semantics search or link data, a data-vault or snowflake model for data warehouse, a streaming model for the real-time event and columnar storage for machine learning. To move or prepare the data and model for multiple types of consumption is not only expensive but has a lot of repetition costs for the team and technology setup. Automation needs to be in place to reduce repetition costs.
 
 There are many ways to start automation of data processing or data ingestion. Some starts with infrastructure or tooling or starts writing code immediately. But Kolle uses data model and data modeling is the first class citizen to this automation process.
 
 Kolle enables users to work on data models, data contracts, metadata, data quality, and data lineage. Users will spend 90% of their time focusing on business work instead of spending time on different sets of tooling. End to end data integration will be generated based on data model and data contract.
 
-It is just 5 to 10 min of work to create end-to-end integration between different types of producers and consumers. Follow example in doc folder.
+It is just 5 to 10 min of work to create end-to-end integration different types of producers and consumers.
 
 # Show me
 
@@ -66,7 +66,7 @@ End to end data integration from semi structure mangodb dataset to different typ
 
 # Quick start
 
-- Try online
+- Try in cloud
   [Kolle](https://kolle.metaheed.com).
 - Run locally
 
@@ -75,8 +75,14 @@ $ docker pull ghcr.io/metaheed/kolle
 $ docker run -it -p 3000:3000 --rm ghcr.io/metaheed/kolle
 ```
 
+- Run [Kolle Sandbox](https://github.com/metaheed/kolle-sandbox)
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 ## License
 
 Copyright © 2022-2023 Abdullah Mamun
 
 Distributed under the Apache License. See LICENSE.
+

doc/kg_automation.md

@@ -22,10 +22,8 @@ Consumer: Knowledge graph model
 2. Remove duplicate data from source data
 3. Data profiling on the source data
 4. Apply data contract for data quality i.e selection, typecast, enrichment, reference data integration, etc
-
-   5a. Good data will move to refined model
-
-   5b. Bad data will move to refined error model
+   4a. Good data will move to refined model
+   4b. Bad data will move to refined error model
 5. Data profiling on refined data
 6. Convert to the RDF model from refined model as the target model
 7. Visualize data as knowledge graph
@@ -40,3 +38,4 @@ Consumer: Knowledge graph model
 ### Show me
 
 [![Introduction](https://img.youtube.com/vi/VEIWuMkZzaI/0.jpg)](https://youtu.be/VEIWuMkZzaI)
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