Line edits

Author: lootle1

learn-pr/azure/intro-to-azure-data-explorer/5-knowledge-check.yml

@@ -51,7 +51,7 @@ quiz:
     choices:
     - content: "Control commands can be used for maintenance and policy tasks."
       isCorrect: true
-      explanation: "Control commands include: The creation of new clusters or databases, data connections, auto scaling, cluster configurations, entities, metadata objects, and security policies."
+      explanation: "Control commands include the creation of new clusters or databases, data connections, auto scaling, cluster configurations, entities, metadata objects, and security policies."
     - content: "A query."
       isCorrect: false
       explanation: "Queries are used for interactive data analytics."

learn-pr/azure/intro-to-azure-data-explorer/includes/1-introduction.md

@@ -1,28 +1,28 @@
-Daily operations and interactions with customers create a constant flow of data. This world of big data is growing steadily, and so is the need to store, process, and analyze the data in a timely and cost-efficient way.  Big data requires large amounts of scalable storage space. Because huge volumes of data flow in at high velocity from various sources, the ability to identify and respond to meaningful events is key. Additionally, data is generated in various formats: structured/semi-structured data and free text, as well as images and videos. In order to find correlations between these different data flows, businesses invest significant time and money into parsing, processing, and storing this data. A robust end-to-end data analytics system that can manage your huge, complex data and run advanced analytics is essential to make data-driven business decisions.  What tool can help you manage this vast array of data types, work flows, and visualizations?
+Daily operations and interactions with customers create a constant flow of data. This world of big data is growing steadily, and so is the need to store, process, and analyze the data in a timely and cost-efficient way.  Big data requires large amounts of scalable storage space. Because huge volumes of data flow in at high velocity from various sources, the ability to identify and respond to meaningful events is key. Additionally, data is generated in various formats like structured and semi-structured data and free text, as well as images and videos. In order to find correlations between these different data flows, businesses invest significant time and money into parsing, processing, and storing this data. A robust end-to-end data analytics system that can manage your huge, complex data and run advanced analytics is essential to make data-driven business decisions.  What tool can help you manage this vast array of data types, work flows, and visualizations?
 
 > [!VIDEO https://learn-video.azurefd.net/vod/player?id=02355c8a-a1f6-4740-ac9a-8d5c876e7ea7]
 
-Azure Data Explorer is a fully managed, high-performance, big data analytics platform. Azure Data Explorer can take all this varied data, and then ingest, process, and store it. You can use Azure Data Explorer for near real-time queries and advanced analytics, as well as for more advanced features such as geospatial analytics, alerting, dashboarding, and business analytics.
+Azure Data Explorer is a fully managed, high-performance, and big data analytics platform. Azure Data Explorer can take all this varied data, and then ingest, process, and store it. You can use Azure Data Explorer for near real-time queries and advanced analytics, as well as for more advanced features such as geospatial analytics, alerting, dashboarding, and business analytics.
 
 ## Example scenario
 
 Imagine you work at a clothing company that is a large chain of brick-and-mortar stores that's expanding into e-commerce. You're about to launch your end of year sale targeting several international audiences. You want to see how your campaign impacts sales, inventory, and logistics. You have a large volume of data flowing in different formats, and need to figure out a way to make sense of this data and use it to make good business decisions.
 
 Different divisions across the company are going to use the collected data to inform their strategic and day-to-day decisions on operations, marketing, and customer relations. They plan to use Azure Data Explorer to ingest various data types into a single collection comprised of:
 
-- **structured data**, such as internal operations systems. 
-- **semi-structured data**, such as marketing clickstream data.
-- **unstructured data**, such as social media feeds.
+- **Structured data**: Such as internal operations systems.
+- **Semi-structured data**: Such as marketing clickstream data.
+- **Unstructured data**: Such as social media feeds.
 
-Then each division can use data analysis and visualization to make data-driven decisions about the campaign.
+Then, each division can use data analysis and visualization to make data-driven decisions about the campaign.
 
 ## What will we be doing?
 
-Analyzing the capabilities of Azure Data Explorer to help you decide when to use it:
+Analyzing the capabilities of Azure Data Explorer to help you decide when to use it, answering:
 
 - What are the strengths of Azure Data Explorer and the Kusto Query Language?
 - How do you work with the service?
-- What types of data can you analyze and where can the data come from?
+- What types of data can you analyze, and where can the data come from?
 - How can you organize, display, or make the results of your queries actionable?
 
 ## What is the main goal?

learn-pr/azure/intro-to-azure-data-explorer/includes/2-what-is-azure-data-explorer.md

@@ -2,9 +2,9 @@ Let's start by defining the service and taking a tour of the core features of Az
 
 ## What is Azure Data Explorer?
 
-Azure Data Explorer is a big data analytics platform that makes it easy to analyze high volumes of data in near real time. Allowing you to extract key insights, spot patterns and trends, and create forecasting models.
+Azure Data Explorer is a big data analytics platform that makes it easy to analyze high volumes of data in near real time. It allows you to extract key insights, spot patterns and trends, and create forecasting models.
 
-The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management. These tools allow you to analyze structured, semi-structured, and unstructured data across time series, and apply Machine Learning.
+The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management. These tools allow you to analyze structured, semi-structured, and unstructured data across time series and apply Machine Learning.
 
 Azure Data Explorer is fully managed, scalable, secure, robust, and enterprise-ready. It's useful for log analytics, time series analytics, IoT, and general-purpose exploratory analytics.
 
@@ -14,9 +14,9 @@ If you remember our example clothing company, they have many types of data comin
 
 **Production** analyzes their product logs to manage their inventory and make manufacturing decisions. Geospatial analytics informs these decisions, which are used to identify geographical areas of high-performing ads and anticipate inventory.
 
-The company's warehouses are outfitted with IoT devices, some of which are used by **security** to manage warehouse entry/exit logs, while others are used by operations to monitor the environment inside the warehouse. Individual stores use time series analytics to identify sales anomalies and predict future inventory events. 
+The company's warehouses are outfitted with IoT devices, some of which are used by **security** to manage warehouse entry/exit logs. Others are used by operations to monitor the environment inside the warehouse. Individual stores use time series analytics to identify sales anomalies and predict future inventory events.
 
-The global **marketing** team uses clickstream data (also a form of log analytics) to optimize and scan online both ad campaigns and the customer funnel, while the customer success department uses text search to analyze user feedback on social media.
+The global **marketing** team uses clickstream data (also a form of log analytics) to optimize and scan online ad campaigns and the customer funnel. The customer success department uses text search to analyze user feedback on social media.
 
 Every minute of the day, a company decision is being made based on data flowing into Azure Data Explorer.
 
@@ -30,19 +30,19 @@ Azure Data Explorer can ingest terabytes of data in minutes in batch or streamin
 
 ### User-friendly query language
 
-Azure Data explorer uses the Kusto Query Language (KQL), an open-source language initially invented by the team. The language is simple to understand and learn, and highly productive. You can use simple operators and advanced analytics.
+Azure Data explorer uses the Kusto Query Language (KQL), an open-source language initially invented by the team. The language is simple to understand and learn, and it's highly productive. You can use simple operators and advanced analytics.
 
 ### Advanced analytics
 
-Azure Data Explorer has a large set of functions for time series analysis. Including, adding and subtracting time series, filtering, regression, seasonality detection, geospatial analysis, anomaly detection, scanning, and forecasting. Time series functions are optimized for processing thousands of time series in seconds. Pattern detection is made easy with cluster plugins that can diagnose anomalies and do root cause analysis. You can also extend Azure Data Explorer capabilities by embedding python code in KQL queries.
+Azure Data Explorer has a large set of functions for time series analysis. Functions includes adding and subtracting time series, filtering, regression, seasonality detection, geospatial analysis, anomaly detection, scanning, and forecasting. Time series functions are optimized for processing thousands of time series in seconds. Pattern detection is made easy with cluster plugins that can diagnose anomalies and do root cause analysis. You can also extend Azure Data Explorer capabilities by embedding python code in KQL queries.
 
 ### Easy-to-use wizard
 
 The ingestion wizard makes the data ingestion process easy, fast, and intuitive. The web UI provides an intuitive and guided experience that helps customers ramp-up quickly to start ingesting data, creating database tables, and mapping structures. It enables a one time or continuous ingestion from various sources in various data formats. Table mappings and schema are auto suggested and easy to modify.
 
 ### Versatile data visualization
 
-Data visualization helps you gain important insights. Azure Data Explorer offers built-in visualization and dashboarding out of the box, with support for various charts and visualizations. It has native integration with Power BI, native connectors for Grafana, Kibana and Databricks, ODBC support for Tableau, Sisense, Qlik and more.
+Data visualization helps you gain important insights. Azure Data Explorer offers built-in visualization and dashboarding out of the box, with support for various charts and visualizations. It has native integration with Power BI, native connectors for Grafana, Kibana and Databricks, ODBC support for Tableau, Sisense, Qlik, and more.
 
 ### Automatic ingest, process, and export
 

learn-pr/azure/intro-to-azure-data-explorer/includes/3-how-azure-data-explorer-works.md

@@ -18,18 +18,20 @@ Each Azure Data Explorer **cluster** can hold up to 10,000 **databases** and eac
 
 The logical structure of a **database** is similar to many other relational databases. An Azure Data Explorer database can contain:
 
-- **Tables:** Made up of a set of columns. Each column has one of nine different data types.
-- **External tables:** Tables whose underlying storage is in other locations such as Azure Data Lake.
+- **Tables**: Made up of a set of columns. Each column has one of nine different data types.
+- **External tables**: Tables whose underlying storage is in other locations such as Azure Data Lake.
 
 ## Get to know the general workflow
 
-Generally speaking, when you interact with Azure Data Explorer, you go through the following workflow: First you ingest your data to get it in the system. Then, you analyze your data. Next, you visualize the results of your analysis. At any time, you may also engage with the data management features. This work with Azure Data Explorer is done through interaction with the cluster. You can access these resources either in the Web UI or by using SDKs.
+Generally speaking, when you interact with Azure Data Explorer you go through the following workflow. First, you ingest your data to get it in the system. Next, you analyze your data. Finally, you visualize the results of your analysis.
+
+At any time, you may also engage with the data management features. This work with Azure Data Explorer is done through interaction with the cluster. You can access these resources either in the Web UI or by using SDKs.
 
 ### How do I get my data into Azure Data Explorer?
 
 Data ingestion is the process used to load data records from one or more sources into a table in Azure Data Explorer. Further data manipulation includes matching schema, organizing, indexing, encoding, and compressing the data. The Data Manager then commits the data ingest to the engine, where it's available for query.
 
-In addition to the native Web UI wizard, there are various ingestion tools available. Including the managed pipelines, Event Grid, IoT Hub, and Azure Data Factory. You can use connectors and plugins such as the Logstash plugin, Kafka connector, Power Automate, and Apache Spark connector. You can also use programmatic ingestion using SDKs, or LightIngest.
+In addition to the native Web UI wizard, there are various ingestion tools available including the managed pipelines, Event Grid, IoT Hub, and Azure Data Factory. You can use connectors and plugins such as the Logstash plugin, Kafka connector, Power Automate, and Apache Spark connector. You can also use programmatic ingestion using SDKs, or LightIngest.
 
 Data can be ingested in two modes: **Batching** or **Streaming**. Batching ingestion is optimized for high ingestion throughput and fast query results. Streaming ingestion allows near real-time latency for small sets of data per table.
 
@@ -39,14 +41,16 @@ Azure Data Explorer uses the proprietary Kusto Query Language (KQL) to analyze d
 
 ### How does the Kusto Query Language work?
 
-Kusto Query Language is an expressive, intuitive, and highly productive query language. It offers a smooth transition from simple one-liners to complex data processing scripts, and supports querying structured, semi-structured, and unstructured (text search) data. There's a wide variety of query language operators and functions (aggregation, filtering, time series functions, geospatial functions, joins, unions, and more) in the language. KQL supports cross-cluster and cross-database queries, and is feature rich from a parsing (json, XML etc.) perspective. In addition, the language natively supports advanced analytics.
+Kusto Query Language is an expressive, intuitive, and highly productive query language. It offers a smooth transition from simple one-liners to complex data processing scripts, and supports querying structured, semi-structured, and unstructured (text search) data. There's a wide variety of query language operators and functions (aggregation, filtering, time series functions, geospatial functions, joins, unions, and more) in the language. KQL supports cross-cluster and cross-database queries, and it's feature rich from a parsing (json, XML etc.) perspective. In addition, the language natively supports advanced analytics.
 
 ### How can I display my query results?
 
-The Azure Data Explorer Web UI was designed with big data in mind, enabling you to run queries and build dashboards. It supports a display of up to 500-K records and thousands of columns. It's highly scalable and rich with functionality that helps you draw quick insights from your data. You can also use different visual displays of your data in your Azure Data Explorer Dashboards. You can also display your results using native connectors to some of the leading visualization services available today, such as Power BI and Grafana. Azure Data Explorer also has ODBC and JDBC connector support to tools such as Tableau and Qlik.
+The Azure Data Explorer Web UI was designed with big data in mind, enabling you to run queries and build dashboards. It supports a display of up to 500-K records and thousands of columns. It's highly scalable and rich with functionality that helps you draw quick insights from your data.
+
+You can also use different visual displays of your data in your Azure Data Explorer Dashboards. You can display your results using native connectors to some of the leading visualization services available today, such as Power BI and Grafana. Azure Data Explorer has ODBC and JDBC connector support to tools such as Tableau and Qlik.
 
 ### How do I manage my data?
 
-Admins want to perform various maintenance and policy tasks on their Azure Data Explorer clusters, and Control commands give them the ability to do so. Using Control commands, they can create new clusters or databases, establish data connections, perform auto scaling, and adjust cluster configurations. They can also control and modify entities, metadata objects, managing permissions, and security policies. In addition, they can modify materialized views (continually updated filtered views of other tables), functions (stored functions and user-defined functions), and the update policy (functions that are triggered following ingestion).
+Admins want to perform various maintenance and policy tasks on their Azure Data Explorer clusters, and Control commands give them the ability to do so. Using Control commands, they can create new clusters or databases, establish data connections, perform auto scaling, and adjust cluster configurations. They can control and modify entities, metadata objects, managing permissions, and security policies. In addition, they can modify materialized views (continually updated filtered views of other tables), functions (stored functions and user-defined functions), and the update policy (functions that are triggered following ingestion).
 
 Control commands are run directly on the engine using the WebUI, the Azure portal, various query tools, or one of the Azure Data Explorer SDKs.