acrolinx

Author: djpmsft

articles/data-factory/wrangling-data-flow-functions.md

@@ -1,6 +1,6 @@
 ---
 title: Wrangling data flow transformation functions in Azure Data Factory | Microsoft Docs
-description: An overview of available wrangling data flow fucntions in Azure Data Factory
+description: An overview of available wrangling data flow functions in Azure Data Factory
 author: djpmsft
 ms.author: daperlov
 ms.reviewer: gamal
@@ -13,9 +13,9 @@ ms.date: 11/01/2019
 
 Wrangling data flow in Azure Data Factory allows you to do code-free agile data preparation and wrangling at cloud scale. Wrangling data flow integrates with [Power Query Online](https://docs.microsoft.com/powerquery-m/power-query-m-reference) and makes Power Query M functions available for data wrangling via spark execution. 
 
-Currently not all Power Query M functions are supported for data wrangling despite being available during authoring. While building your wrangling data flows, you will be prompted with the following error message if a function is not supported:
+Currently not all Power Query M functions are supported for data wrangling despite being available during authoring. While building your wrangling data flows, you'll be prompted with the following error message if a function isn't supported:
 
-`The wrangling data flow is invalid. Expression.Error: The transformation logic is not supported. Please try a simpler expression`
+`The wrangling data flow is invalid. Expression.Error: The transformation logic isn't supported. Please try a simpler expression`
 
 Below is a list of supported Power Query M functions.
 
@@ -44,7 +44,7 @@ The following M functions add or transform columns: [Table.AddColumn](https://do
 * Numeric arithmetic
 * Text concatenation
 * Date andTime Arithmetic (Arithmetic operators, [Date.AddDays](https://docs.microsoft.com/powerquery-m/date-adddays), [Date.AddMonths](https://docs.microsoft.com/powerquery-m/date-addmonths), [Date.AddQuarters](https://docs.microsoft.com/powerquery-m/date-addquarters), [Date.AddWeeks](https://docs.microsoft.com/powerquery-m/date-addweeks), [Date.AddYears](https://docs.microsoft.com/powerquery-m/date-addyears))
-* Durations can be used for date and time arithmetic, but must be transformed into some other type before being written to a sink (Arithmetic operators, [#duration](https://docs.microsoft.com/powerquery-m/sharpduration), [Duration.Days](https://docs.microsoft.com/powerquery-m/duration-days), [Duration.Hours](https://docs.microsoft.com/powerquery-m/duration-hours), [Duration.Minutes](https://docs.microsoft.com/powerquery-m/duration-minutes), [Duration.Seconds](https://docs.microsoft.com/powerquery-m/duration-seconds), [Duration.TotalDays](https://docs.microsoft.com/powerquery-m/duration-totaldays), [Duration.TotalHours](https://docs.microsoft.com/powerquery-m/duration-totalhours), [Duration.TotalMinutes](https://docs.microsoft.com/powerquery-m/duration-totalminutes), [Duration.TotalSeconds](https://docs.microsoft.com/powerquery-m/duration-totalseconds))    
+* Durations can be used for date and time arithmetic, but must be transformed into another type before written to a sink (Arithmetic operators, [#duration](https://docs.microsoft.com/powerquery-m/sharpduration), [Duration.Days](https://docs.microsoft.com/powerquery-m/duration-days), [Duration.Hours](https://docs.microsoft.com/powerquery-m/duration-hours), [Duration.Minutes](https://docs.microsoft.com/powerquery-m/duration-minutes), [Duration.Seconds](https://docs.microsoft.com/powerquery-m/duration-seconds), [Duration.TotalDays](https://docs.microsoft.com/powerquery-m/duration-totaldays), [Duration.TotalHours](https://docs.microsoft.com/powerquery-m/duration-totalhours), [Duration.TotalMinutes](https://docs.microsoft.com/powerquery-m/duration-totalminutes), [Duration.TotalSeconds](https://docs.microsoft.com/powerquery-m/duration-totalseconds))    
 * Most standard, scientific, and trigonometric numeric functions (All functions under [Operations](https://docs.microsoft.com/powerquery-m/number-functions#operations), [Rounding](https://docs.microsoft.com/powerquery-m/number-functions#rounding), and [Trigonometry](https://docs.microsoft.com/powerquery-m/number-functions#trigonometry) *except* Number.Factorial, Number.Permutations, and Number.Combinations)
 * Replacement ([Replacer.ReplaceText](https://docs.microsoft.com/powerquery-m/replacer-replacetext), [Replacer.ReplaceValue](https://docs.microsoft.com/powerquery-m/replacer-replacevalue), [Text.Replace](https://docs.microsoft.com/powerquery-m/text-replace), [Text.Remove](https://docs.microsoft.com/powerquery-m/text-remove))
 * Positional text extraction ([Text.PositionOf](https://docs.microsoft.com/powerquery-m/text-positionof), [Text.Length](https://docs.microsoft.com/powerquery-m/text-length), [Text.Start](https://docs.microsoft.com/powerquery-m/text-start), [Text.End](https://docs.microsoft.com/powerquery-m/text-end), [Text.Middle](https://docs.microsoft.com/powerquery-m/text-middle), [Text.ReplaceRange](https://docs.microsoft.com/powerquery-m/text-replacerange), [Text.RemoveRange](https://docs.microsoft.com/powerquery-m/text-removerange))
@@ -64,9 +64,9 @@ Merging/Joining tables
     (Table.ExpandTableColumn, not supported in any other context).
 * The M function
     [Table.Join](https://docs.microsoft.com/powerquery-m/table-join) can
-    be manually written directly to avoid the need for an additional expansion
+    be written directly to avoid the need for an additional expansion
     step, but the user must ensure that there are no duplicate column names
-    amongst the joined tables (because ADF does not support them).
+    among the joined tables
 * Supported Join Kinds:
     [Inner](https://docs.microsoft.com/powerquery-m/joinkind-inner),
     [LeftOuter](https://docs.microsoft.com/powerquery-m/joinkind-leftouter),
@@ -110,7 +110,7 @@ Keep and Remove Top, Keep Range (corresponding M functions,
 
 ## Known unsupported functionality
 
-Below are functions that are not supported. This list isn't exhaustive and is subject to change.
+Below are functions that aren't supported. This list isn't exhaustive and is subject to change.
 * Merge columns (can be achieved with AddColumn)
 * Split column
 * Append queries

articles/data-factory/wrangling-data-flow-overview.md

@@ -11,27 +11,27 @@ ms.date: 11/01/2019
 
 # What are wrangling data flows?
 
-Organizations need to do data preparation and wrangling for accurate analysis of complex data that continues to grow every day. Data preparation is also required so that organizations can use the data effectively in various business processes and reduce the time to value.
+Organizations need to do data preparation and wrangling for accurate analysis of complex data that continues to grow every day. Data preparation is required so that organizations can use the data in various business processes and reduce the time to value.
 
-Wrangling data flows in Azure Data Factory allow you to do code-free data preparation at cloud scale iteratively. Wrangling data flows integrate with [Power Query Online](https://docs.microsoft.com/power-query/) and makes Power Query M functions available for data wrangling at cloud scale via spark execution.
+Wrangling data flows in Azure Data Factory allow you to do code-free data preparation at cloud scale iteratively. Wrangling data flows integrate with [Power Query Online](https://docs.microsoft.com/power-query/) and makes Power Query M functions available for data factory users.
 
-Wrangling data flow translates M generated by the Power Query Online Mashup Editor into spark code for cloud scale execution and provides best in class monitoring experience.
+Wrangling data flow translates M generated by the Power Query Online Mashup Editor into spark code for cloud scale execution.
 
 Wrangling data flows are especially useful for data engineers or 'citizen data integrators'.
 
 ## Use cases
 
 ### Fast interactive data exploration and preparation
 
-Multiple data engineers and citizen data integrators can interactively explore and prepare datasets at cloud scale. With the rise of volume, variety and velocity of data in data lakes, sometimes you need to explore and prepare a data set or are asked to create a new dataset. For example, you may need to create a dataset that 'has all customer demographic info for new customers since 2017'. You aren't mapping to a known target. You're exploring, wrangling, and prepping datasets to meet a requirement before publishing it in the lake. These are often used for less formal analytics scenarios. The prepped datasets can be used for doing transformations and machine learning operations downstream.
+Multiple data engineers and citizen data integrators can interactively explore and prepare datasets at cloud scale. With the rise of volume, variety and velocity of data in data lakes, users need an effective way to explore and prepare data sets. For example, you may need to create a dataset that 'has all customer demographic info for new customers since 2017'. You aren't mapping to a known target. You're exploring, wrangling, and prepping datasets to meet a requirement before publishing it in the lake. Wrangling data flows are often used for less formal analytics scenarios. The prepped datasets can be used for doing transformations and machine learning operations downstream.
 
 ### Code-free agile data preparation
 
-Citizen data integrators spend more than 60% of their time looking for and preparing data. They're looking to do it in a code free manner to improve operational productivity. Allowing citizen data integrators to enrich, shape, and publish data using known tools like Power Query Online in a scalable manner drastically improves their productivity. Wrangling data flow in Azure Data Factory enables the familiar Power Query Online mashup editor to allow citizen data integrators to fix errors quickly, standardize data, and produce high quality data to support business decision makers.
+Citizen data integrators spend more than 60% of their time looking for and preparing data. They're looking to do it in a code free manner to improve operational productivity. Allowing citizen data integrators to enrich, shape, and publish data using known tools like Power Query Online in a scalable manner drastically improves their productivity. Wrangling data flow in Azure Data Factory enables the familiar Power Query Online mashup editor to allow citizen data integrators to fix errors quickly, standardize data, and produce high-quality data to support business decisions.
 
 ### Data Validation
 
-Visually scan your data in a code-free manner to remove any outliers, anomalies
+Visually scan your data in a code-free manner to remove any outliers, anomalies,
 and conform it to a shape for fast analytics.
 
 ## Next steps