Update data-explorer-overview.md

Author: JasonWHowell

articles/synapse-analytics/data-explorer/data-explorer-overview.md

@@ -24,12 +24,12 @@ To learn more, see the following video:
 
 ## What makes Azure Synapse Data Explorer unique?
 
-* **Easy ingestion** - Data Explorer offers built-in integrations for no-code/low-code, high-throughput data ingestion, and caching data from real-time sources. Data can be ingested from sources such as Event Hub, Kafka, Azure Data Lake, open source agents such as Fluentd/Fluent Bit, and a wide variety of cloud and on-premises data sources.
+* **Easy ingestion** - Data Explorer offers built-in integrations for no-code/low-code, high-throughput data ingestion, and caching data from real-time sources. Data can be ingested from sources such as Azure Event Hubs, Kafka, Azure Data Lake, open source agents such as Fluentd/Fluent Bit, and a wide variety of cloud and on-premises data sources.
 
-* **No complex data modeling** - With Data Explorer, there is no need to build complex data models and no need for complex scripting to transform data before it's consumed.
-* **No index maintenance** - There is no need for maintenance tasks to optimize data for query performance and no need for index maintenance. With Data Explorer, all raw data is available immediately, allowing you to run high-performance and high-concurrency queries on your streaming and persistent data. You can use these queries to build near real-time dashboards and alerts and connect operational analytics data with the rest of the data analytics platform.
+* **No complex data modeling** - With Data Explorer, there's no need to build complex data models and no need for complex scripting to transform data before it's consumed.
+* **No index maintenance** - There's no need for maintenance tasks to optimize data for query performance and no need for index maintenance. With Data Explorer, all raw data is available immediately, allowing you to run high-performance and high-concurrency queries on your streaming and persistent data. You can use these queries to build near real-time dashboards and alerts and connect operational analytics data with the rest of the data analytics platform.
 * **Democratizing data analytics** - Data Explorer democratizes self-service, big data analytics with the intuitive Kusto Query Language (KQL) that provides the expressiveness and power of SQL with the simplicity of Excel. KQL is highly optimized for exploring raw telemetry and time series data by leveraging Data Explorer's best-in-class text indexing technology for efficient free-text and regex search, and comprehensive parsing capabilities for querying traces\text data and JSON semi-structured data including arrays and nested structures. KQL offers advanced time series support for creating, manipulating, and analyzing multiple time series with in-engine Python execution support for model scoring.
-* **Proven technology at petabyte scale** - Data Explorer is a distributed system with compute and storage that can scale independently, enabling analytics on gigabytes or petabytes of data.
+* **Proven technology at petabyte scale** - Data Explorer is a distributed system with compute resources and storage that can scale independently, enabling analytics on gigabytes or petabytes of data.
 * **Integrated** - Azure Synapse Analytics provides interoperability across data between Data Explorer, Apache Spark, and SQL engines empowering data engineers, data scientists, and data analysts to easily, and securely, access and collaborate on the same data in the data lake.
 
 ## When to use Azure Synapse Data Explorer?
@@ -45,7 +45,7 @@ Use Data Explorer as a data platform for building near real-time log analytics a
 
 ## Data Explorer pool architecture
 
-Data Explorer pools implement a scale-out architecture by separating the compute and storage resources. This enables you to independently scale each resource and, for example, run multiple read-only computes on the same data. Data Explorer pools consist of a set of computes running the engine that is responsible for automatic indexing, compressing, caching, and serving distributed queries. They also have a second set of computes running the data management service responsible for background system jobs, and managed and queued data ingestion. All data is persisted on managed blob storage accounts using a compressed columnar format.
+Data Explorer pools implement a scale-out architecture by separating the compute and storage resources. This enables you to independently scale each resource and, for example, run multiple read-only computes on the same data. Data Explorer pools consist of a set of compute resources running the engine that is responsible for automatic indexing, compressing, caching, and serving distributed queries. They also have a second set of compute resources running the data management service responsible for background system jobs, and managed and queued data ingestion. All data is persisted on managed blob storage accounts using a compressed columnar format.
 
 Data Explorer pools support a rich ecosystem for ingesting data using connectors, SDKs, REST APIs, and other managed capabilities. It offers various ways to consume data for ad hoc queries, reports, dashboards, alerts, REST APIs, and SDKs.