Update concepts-data-flow-performance.md

Author: kromerm

articles/data-factory/concepts-data-flow-performance.md

@@ -6,7 +6,7 @@ ms.topic: conceptual
 ms.author: makromer
 ms.service: data-factory
 ms.custom: seo-lt-2019
-ms.date: 04/27/2020
+ms.date: 05/21/2020
 ---
 
 # Mapping data flows performance and tuning guide
@@ -36,7 +36,7 @@ While designing mapping data flows, you can unit test each transformation by cli
 
 An Integration Runtime with more cores increases the number of nodes in the Spark compute environments and provides more processing power to read, write, and transform your data. ADF Data Flows utilizes Spark for the compute engine. The Spark environment works very well on memory-optimized resources.
 * Try a **Compute Optimized** cluster if you want your processing rate to be higher than your input rate.
-* Try a **Memory Optimized** cluster if you want to cache more data in memory. Memory optimized has a higher price-point per core than Compute Optimized, but will likely result in faster transformation speeds.
+* Try a **Memory Optimized** cluster if you want to cache more data in memory. Memory optimized has a higher price-point per core than Compute Optimized, but will likely result in faster transformation speeds. If you experience out of memory errors when execution your data flows, switch to a memory optimized Azure IR configuration.
 
 ![New IR](media/data-flow/ir-new.png "New IR")