diff --git a/README.md b/README.md
index 0a51c9319..06a47e822 100644
--- a/README.md
+++ b/README.md
@@ -51,11 +51,10 @@ Check the [step by step guide](https://docs.zingg.ai/zingg/stepbystep) for more
 
 ## Connectors
 
-Zingg connects, reads and writes to most on-premise and cloud data sources. Zingg runs on any private or cloud based Spark service. 
-
+Zingg connects, reads and writes to most on-premise and cloud data sources. Zingg runs on any private or cloud based Spark service.
 ![zinggConnectors](assets/zinggOSS.png)
 
-Zingg can read and write to Snowflake, Cassandra, S3, Azure, Elastic, major RDBMS and any Spark supported data sources. Zingg also works with all major file formats like Parquet, Avro, JSON, XLSX, CSV, TSV etc. This is done through the Zingg [pipe](docs/pipes.md) abstraction.  
+Zingg can read and write to Snowflake, Cassandra, S3, Azure, Elastic, major RDBMS and any Spark supported data sources. Zingg also works with all major file formats like Parquet, Avro, JSON, XLSX, CSV, TSV etc. This is done through the Zingg [pipe](docs/dataSourcesAndSinks/pipes.md) abstraction.  
 
 ## Key Zingg Concepts
 
@@ -63,8 +62,7 @@ Zingg learns 2 models on the data.
 
 1. Blocking Model
 
-One fundamental problem with scaling data mastering is that the number of comparisons increase quadratically as the number of input record increases. 
-
+One fundamental problem with scaling data mastering is that the number of comparisons increase quadratically as the number of input record increases.
 ![Data Mastering At Scale](/assets/fuzzymatchingcomparisons.jpg)