info
Google Groups no longer supports new Usenet posts or subscriptions. Historical content remains viewable.
Dismiss
Azure Data Explorer [VERIFIED]
2 views
Skip to first unread message
Lucille Minasian
Jan 26, 2024, 8:14:40 AMJan 26
to
Azure Data Explorer is a fully managed, high-performance, big data analytics platform that makes it easy to analyze high volumes of data in near real time. The Azure Data Explorer toolbox gives you an end-to-end solution for data ingestion, query, visualization, and management.
azure data explorer
DOWNLOAD https://lpoms.com/2xwdIF
By analyzing structured, semi-structured, and unstructured data across time series, and by using Machine Learning, Azure Data Explorer makes it simple to extract key insights, spot patterns and trends, and create forecasting models. Azure Data Explorer uses a traditional relational model, organizing data into tables with strongly-typed schemas. Tables are stored within databases, and a cluster can manage multiple databases. Azure Data Explorer is scalable, secure, robust, and enterprise-ready, and is useful for log analytics, time series analytics, IoT, and general-purpose exploratory analytics.
Azure Data Explorer is ideal for enabling interactive analytics capabilities over high velocity, diverse raw data. Use the following decision tree to help you decide if Azure Data Explorer is right for you:
With Azure Data Explorer, you can ingest terabytes of data in minutes via queued ingestion or streaming ingestion. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data. Ingest your data in different formats and structures, flowing from various pipelines and sources.
The ingestion wizard makes the data ingestion process easy, fast, and intuitive. The Azure Data Explorer web UI provides an intuitive and guided experience that helps you ramp-up quickly to start ingesting data, creating database tables, and mapping structures. It enables one time or a continuous ingestion from various sources and in various data formats. Table mappings and schema are auto suggested and easy to modify.
Create database: Create a cluster and then create one or more databases in that cluster. Each Azure Data Explorer cluster can hold up to 10,000 databases and each database up to 10,000 tables. The data in each table is stored in data shards also called "extents". All data is automatically indexed and partitioned based on the ingestion time. This means you can store a lot of varied data and because of the way it's stored, you get fast access to querying it. Quickstart: Create an Azure Data Explorer cluster and database
Ingest data: Load data into database tables so that you can run queries against it. Azure Data Explorer supports several ingestion methods, each with its own target scenarios. These methods include ingestion tools, connectors and plugins to diverse services, managed pipelines, programmatic ingestion using SDKs, and direct access to ingestion. Get started with the ingestion wizard.
Query database: Azure Data Explorer uses the Kusto Query Language, which 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, and more) perspective. The language also natively supports advanced analytics.
Use the web application to run, review, and share queries and results. You can also send queries programmatically (using an SDK) or to a REST API endpoint. If you're familiar with SQL, get started with the SQL to Kusto cheat sheet. Quickstart: Query data in Azure Data Explorer web UI
Visualize results: Use different visual displays of your data in the native Azure Data Explorer Dashboards. You can also display your results using connectors to some of the leading visualization services, such as Power BI and Grafana. Azure Data Explorer also has ODBC and JDBC connector support to tools such as Tableau and Sisense.
Elastically scale to terabytes of data in minutes. Azure Data Explorer offers fast, low-latency ingestion with linear scaling that supports up to 12 Mbps per core. Apply a wide range of data ingestion methods from devices, applications, servers, and services to your specific use cases.
Focus on data instead of infrastructure. This powerful, fully managed data analytics service automatically scales to meet your demands. Control costs by paying only for what you need, with no upfront costs or termination fees. Take advantage of global availability for massive scalability.
Ask unlimited questions without skyrocketing costs; you pay by the hour, not by the query. You also control your storage costs. Get the best of a persistent database to automatically add data to the table, but with the flexibility to choose a retention policy based on how long you want to store the data. For persistent storage at commodity pricing, write data to Azure Blob Storage for future use.
Build your own solution with interactive big data analytics built in. Azure Data Explorer is the data exploration service for Azure Monitor, Azure Time Series Insights, and Microsoft Defender . It supports REST API, MS-TDS, and Azure Resource Manager service endpoints and several client libraries.
IoT devices generate billions of sensor readings. Normalizing and aggregating data typically requires multiple technologies, which slows analysis, complicates maintenance, and leads to reliability issues. Azure Data Explorer facilitates remote monitoring of manufacturing equipment, vehicles, and systems that continuously cycle through operations. Derive insights from large volumes of telemetry to ensure high performance and optimize machine quality. Use the data for notifications and to feed analysis tools to diagnose and treat problems.
Organizations are rapidly adopting software as a service (SaaS) applications for business transformation. Embed Azure Data Explorer in SaaS applications to ingest and analyze petabytes of data in real time. With this data, developers monitor service and improve application performance, while business users discover user trends, create personalized experiences, and develop new revenue streams
Azure Data Explorer provides a web experience that enables you to connect to your Azure Data Explorer clusters and write, run, and share Kusto Query Language (KQL) commands and queries. The web experience is available in the Azure portal and as a stand-alone web application, the Azure Data Explorer web UI. In this quickstart, you'll learn how to query data in the stand-alone Azure Data Explorer web UI.
When you first open the web UI, in the Query page, you should see a connection to the help cluster. The examples in this quickstart use the StormEvents table in the Samples database of the help cluster.
Azure Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. Explore your data from end-to-end in the Azure Data Explorer web application, starting with data ingestion, running queries, and ultimately building dashboards.
A dashboard is a collection of tiles, optionally organized in pages, where each tile has an underlying query and a visual representation. Using the web UI, you can natively export Kusto Query Language (KQL) queries to a dashboard as visuals and later modify their underlying queries and visual formatting as needed. In addition to ease of data exploration, this fully integrated Azure Data Explorer dashboard experience provides improved query and visualization performance.
Pages are optional containers for tiles. You can use pages to organize tiles into logical groups, such as by data source or by subject area. You can also use pages to create a dashboard with multiple views, such as a dashboard with a drillthrough from a summary page to a details page.
However, database editors might want to limit the minimum refresh rate that any viewer can set so as to reduce the cluster load. When the minimum refresh rate is set, database users can't set a refresh rate lower than the minimum.
You can definitely continue to use Logstash and leverage the Logstash Azure Data Explorer plugin ( -us/azure/data-explorer/ingest-data-logstash) to send the data.But you can also use a Telegraf agent with the Azure Data Explorer output plugin ( -data-explorer-blog/new-azure-data-explorer-output-plugin-for-telegraf-enables-sql/ba-p/2829444) or FluentBit ( -us/updates/fluent-bit-connector-for-azure-storage-to-support-azure-data-explorer/)
You can also add another hop and leverage Azure Event Hub to aggregate the data. Most syslog listeners can send data to event hubs via the Kafka head it exposes. Then Azure Data Explorer can listen on the Event Hub.
It is recommended that you use a volume to save the Grafana data in. Otherwise if you remove the docker container, you will lose all your Grafana data (dashboards, users etc.). You can create a volume with the Docker Volume Driver for Azure File Storage.
The AAD application that you created above needs to be given viewer access to your Azure Data Explorer database (in this example the database is called Grafana). This is done using the dot command add. The argument for .add contains both the client and tenant id separated by a semicolon:
The trace format option can be used to display appropriately formatted data using the built in trace visualization. To use this visualization, data must be presented following the schema that is defined here. The schema contains the logs, serviceTags, and tags fields which are expected to be JSON objects. These fields will be converted to the expected data structure provided the schema in ADX matches the below:
Instead of hard-coding things like server, application and sensor name in your metric queries you can use variables in their place. Variables are shown as dropdown select boxes at the top of the dashboard. These dropdowns make it easy to change the data being displayed in your dashboard.
f5d0e4f075
azure data explorer
DOWNLOAD https://lpoms.com/2xwdIF
By analyzing structured, semi-structured, and unstructured data across time series, and by using Machine Learning, Azure Data Explorer makes it simple to extract key insights, spot patterns and trends, and create forecasting models. Azure Data Explorer uses a traditional relational model, organizing data into tables with strongly-typed schemas. Tables are stored within databases, and a cluster can manage multiple databases. Azure Data Explorer is scalable, secure, robust, and enterprise-ready, and is useful for log analytics, time series analytics, IoT, and general-purpose exploratory analytics.
Azure Data Explorer is ideal for enabling interactive analytics capabilities over high velocity, diverse raw data. Use the following decision tree to help you decide if Azure Data Explorer is right for you:
With Azure Data Explorer, you can ingest terabytes of data in minutes via queued ingestion or streaming ingestion. You can query petabytes of data, with results returned within milliseconds to seconds. Azure Data Explorer provides high velocity (millions of events per second), low latency (seconds), and linear scale ingestion of raw data. Ingest your data in different formats and structures, flowing from various pipelines and sources.
The ingestion wizard makes the data ingestion process easy, fast, and intuitive. The Azure Data Explorer web UI provides an intuitive and guided experience that helps you ramp-up quickly to start ingesting data, creating database tables, and mapping structures. It enables one time or a continuous ingestion from various sources and in various data formats. Table mappings and schema are auto suggested and easy to modify.
Create database: Create a cluster and then create one or more databases in that cluster. Each Azure Data Explorer cluster can hold up to 10,000 databases and each database up to 10,000 tables. The data in each table is stored in data shards also called "extents". All data is automatically indexed and partitioned based on the ingestion time. This means you can store a lot of varied data and because of the way it's stored, you get fast access to querying it. Quickstart: Create an Azure Data Explorer cluster and database
Ingest data: Load data into database tables so that you can run queries against it. Azure Data Explorer supports several ingestion methods, each with its own target scenarios. These methods include ingestion tools, connectors and plugins to diverse services, managed pipelines, programmatic ingestion using SDKs, and direct access to ingestion. Get started with the ingestion wizard.
Query database: Azure Data Explorer uses the Kusto Query Language, which 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, and more) perspective. The language also natively supports advanced analytics.
Use the web application to run, review, and share queries and results. You can also send queries programmatically (using an SDK) or to a REST API endpoint. If you're familiar with SQL, get started with the SQL to Kusto cheat sheet. Quickstart: Query data in Azure Data Explorer web UI
Visualize results: Use different visual displays of your data in the native Azure Data Explorer Dashboards. You can also display your results using connectors to some of the leading visualization services, such as Power BI and Grafana. Azure Data Explorer also has ODBC and JDBC connector support to tools such as Tableau and Sisense.
Elastically scale to terabytes of data in minutes. Azure Data Explorer offers fast, low-latency ingestion with linear scaling that supports up to 12 Mbps per core. Apply a wide range of data ingestion methods from devices, applications, servers, and services to your specific use cases.
Focus on data instead of infrastructure. This powerful, fully managed data analytics service automatically scales to meet your demands. Control costs by paying only for what you need, with no upfront costs or termination fees. Take advantage of global availability for massive scalability.
Ask unlimited questions without skyrocketing costs; you pay by the hour, not by the query. You also control your storage costs. Get the best of a persistent database to automatically add data to the table, but with the flexibility to choose a retention policy based on how long you want to store the data. For persistent storage at commodity pricing, write data to Azure Blob Storage for future use.
Build your own solution with interactive big data analytics built in. Azure Data Explorer is the data exploration service for Azure Monitor, Azure Time Series Insights, and Microsoft Defender . It supports REST API, MS-TDS, and Azure Resource Manager service endpoints and several client libraries.
IoT devices generate billions of sensor readings. Normalizing and aggregating data typically requires multiple technologies, which slows analysis, complicates maintenance, and leads to reliability issues. Azure Data Explorer facilitates remote monitoring of manufacturing equipment, vehicles, and systems that continuously cycle through operations. Derive insights from large volumes of telemetry to ensure high performance and optimize machine quality. Use the data for notifications and to feed analysis tools to diagnose and treat problems.
Organizations are rapidly adopting software as a service (SaaS) applications for business transformation. Embed Azure Data Explorer in SaaS applications to ingest and analyze petabytes of data in real time. With this data, developers monitor service and improve application performance, while business users discover user trends, create personalized experiences, and develop new revenue streams
Azure Data Explorer provides a web experience that enables you to connect to your Azure Data Explorer clusters and write, run, and share Kusto Query Language (KQL) commands and queries. The web experience is available in the Azure portal and as a stand-alone web application, the Azure Data Explorer web UI. In this quickstart, you'll learn how to query data in the stand-alone Azure Data Explorer web UI.
When you first open the web UI, in the Query page, you should see a connection to the help cluster. The examples in this quickstart use the StormEvents table in the Samples database of the help cluster.
Azure Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. Explore your data from end-to-end in the Azure Data Explorer web application, starting with data ingestion, running queries, and ultimately building dashboards.
A dashboard is a collection of tiles, optionally organized in pages, where each tile has an underlying query and a visual representation. Using the web UI, you can natively export Kusto Query Language (KQL) queries to a dashboard as visuals and later modify their underlying queries and visual formatting as needed. In addition to ease of data exploration, this fully integrated Azure Data Explorer dashboard experience provides improved query and visualization performance.
Pages are optional containers for tiles. You can use pages to organize tiles into logical groups, such as by data source or by subject area. You can also use pages to create a dashboard with multiple views, such as a dashboard with a drillthrough from a summary page to a details page.
However, database editors might want to limit the minimum refresh rate that any viewer can set so as to reduce the cluster load. When the minimum refresh rate is set, database users can't set a refresh rate lower than the minimum.
You can definitely continue to use Logstash and leverage the Logstash Azure Data Explorer plugin ( -us/azure/data-explorer/ingest-data-logstash) to send the data.But you can also use a Telegraf agent with the Azure Data Explorer output plugin ( -data-explorer-blog/new-azure-data-explorer-output-plugin-for-telegraf-enables-sql/ba-p/2829444) or FluentBit ( -us/updates/fluent-bit-connector-for-azure-storage-to-support-azure-data-explorer/)
You can also add another hop and leverage Azure Event Hub to aggregate the data. Most syslog listeners can send data to event hubs via the Kafka head it exposes. Then Azure Data Explorer can listen on the Event Hub.
It is recommended that you use a volume to save the Grafana data in. Otherwise if you remove the docker container, you will lose all your Grafana data (dashboards, users etc.). You can create a volume with the Docker Volume Driver for Azure File Storage.
The AAD application that you created above needs to be given viewer access to your Azure Data Explorer database (in this example the database is called Grafana). This is done using the dot command add. The argument for .add contains both the client and tenant id separated by a semicolon:
The trace format option can be used to display appropriately formatted data using the built in trace visualization. To use this visualization, data must be presented following the schema that is defined here. The schema contains the logs, serviceTags, and tags fields which are expected to be JSON objects. These fields will be converted to the expected data structure provided the schema in ADX matches the below:
Instead of hard-coding things like server, application and sensor name in your metric queries you can use variables in their place. Variables are shown as dropdown select boxes at the top of the dashboard. These dropdowns make it easy to change the data being displayed in your dashboard.
f5d0e4f075
0 new messages