Merge pull request #111178 from Virginia001/work01

Work01

Author: PRMerger15

articles/time-series-insights/time-series-insights-environment-planning.md

@@ -9,7 +9,7 @@ manager: cshankar
 ms.devlang: csharp
 ms.workload: big-data
 ms.topic: conceptual
-ms.date: 01/21/2020
+ms.date: 04/13/2020
 ms.custom: seodec18
 ---
 
@@ -25,7 +25,7 @@ This article describes how to plan your Azure Time Series Insights general avail
 
 ## Best practices
 
-To get started with Azure Time Series Insights, it’s best if you know how much data you expect to push by the minute and how long you need to store your data.  
+To get started with Azure Time Series Insights, it's best if you know how much data you expect to push by the minute and how long you need to store your data.  
 
 For more information about capacity and retention for both Time Series Insights SKUs, read [Time Series Insights pricing](https://azure.microsoft.com/pricing/details/time-series-insights/).
 
@@ -50,7 +50,7 @@ Azure Time Series Insights has two modes:
 * One mode optimizes for the most up-to-date data. It enforces a policy to **Purge old data** leaving recent data available with the instance. This mode is on, by default. 
 * The other optimizes data to remain below the configured retention limits. **Pause ingress** prevents new data from being ingressed when it's selected as the **Storage limit exceeded behavior**.
 
-You can adjust retention and toggle between the two modes on the environment’s configuration page in the Azure portal.
+You can adjust retention and toggle between the two modes on the environment's configuration page in the Azure portal.
 
 > [!IMPORTANT]
 > You can configure a maximum of 400 days of data retention in your Azure Time Series Insights GA environment.

articles/time-series-insights/time-series-insights-update-overview.md

@@ -8,7 +8,7 @@ ms.author: dpalled
 manager: cshankar
 ms.workload: big-data
 ms.topic: overview
-ms.date: 02/04/2020
+ms.date: 04/13/2020
 ms.custom: seodec18
 ---
 
@@ -50,16 +50,16 @@ A rich operational analytics platform combined with our interactive data explora
 
 * Multi-layered storage solution with warm and cold analytics support providing customers the option to route data between warm and cold for interactive analytics over warm data as well as operational intelligence over decades of historical data. 
 
-    *	A highly interactive warm analytics solution to perform frequent, and large number of queries over shorter time span data 
-    *	A scalable, performant, and cost optimized time series data lake based on Azure Storage allowing customers to trend years’ worth of time series data in seconds. 
+    *    A highly interactive warm analytics solution to perform frequent, and large number of queries over shorter time span data 
+    *    A scalable, performant, and cost optimized time series data lake based on Azure Storage allowing customers to trend years' worth of time series data in seconds. 
 
 * Semantic model support that describes the domain and metadata associated with the derived and raw signals from assets and devices.
 
 * Flexible analytics platform to store historical time series data in customer-owned Azure Storage account, thereby allowing customers to have ownership of their IoT data. Data is stored in open source Apache Parquet format that enables connectivity and interop across a variety of data scenarios including predictive analytics, machine learning, and other custom computations done using familiar technologies including Spark, Databricks, and Jupyter.
 
 * Rich analytics with enhanced query APIs and user experience that combines asset-based data insights with rich, ad hoc data analytics with support for interpolation, scalar and aggregate functions, categorical variables, scatter plots, and time shifting time series signals for in-depth analysis.
 
-*	Enterprise grade platform to support the scale, performance, security, and reliability needs of our enterprise IoT customers.
+*    Enterprise grade platform to support the scale, performance, security, and reliability needs of our enterprise IoT customers.
 
 * Extensibility and integration support for end-to-end analytics. Time Series Insights provides an extensible analytics platform for a variety of data scenarios. Time Series Insights Power BI connector enables customers to take the queries they do in Time Series Insights directly into Power BI to get unified view of their BI and time series analytics in a single pane of glass.