updates

docs/developer-guide/index.md

@@ -801,7 +801,7 @@ We provide utility functions to simplify working with Metadata in [com.salesforc
 
 DataReaders define how data should be loaded into the workflow. They load and process raw data to produce the Dataframe used by the workflow. DataReaders are tied to a specific data source with the type of the raw loaded data (for example the AVRO schema or a case class describing the columns in a CSV).
 
-There are three types of DataReaders. [Simple DataReaders](/Developer-Guide/#datareaders) just load the data and return a DataFrame with one row for each row of data read. [Aggregate DataReaders](/Developer-Guide#aggregate-data-readers) will group the data by the entity (the thing you are scoring) key and combine values (with or without time filters) based on the aggregation function associated with each feature definition. For example aggregate readers can be used to compute features like total spend from a list of transactions. [Conditional DataReaders](Developer-Guide/#conditional-data-readers) are like aggregate readers but they allow an daynamic time cuttoff for each row that depends on fullfilment of a user defined condition. For example conditional readers can be used to compute features like total spend before a user becomes a member. These readers can be combined to [join](/examples/Time-Series-Aggregates-and-Joins.html) multiple datasources.
+There are three types of DataReaders. [Simple DataReaders](/Developer-Guide/#datareaders) just load the data and return a DataFrame with one row for each row of data read. [Aggregate DataReaders](/Developer-Guide#aggregate-data-readers) will group the data by the entity (the thing you are scoring) key and combine values (with or without time filters) based on the aggregation function associated with each feature definition. For example aggregate readers can be used to compute features like total spend from a list of transactions. [Conditional DataReaders](/Developer-Guide/#conditional-data-readers) are like aggregate readers but they allow an dynamic time cuttoff for each row that depends on fullfilment of a user defined condition. For example conditional readers can be used to compute features like total spend before a user becomes a member. These readers can be combined to [join](/examples/Time-Series-Aggregates-and-Joins.html) multiple datasources.
 
 A constructor object provides shortcuts for defining most commonly used data readers. Defiing a data reader requires specifying the type of the data being read and the key for the data (the entity being scored).