What is Azure Data Explorer? Important factors of Azure Data Explorer

by francis babet Online

The biggest concern faced by someone who is into building a data-based DevOps is the scale of data which is being collected. It is generally seen that logs from many millions of users keep adding up. This also holds good for other huge data sources and even the internet of things. In today‚Äôs world terabytes of data is being generated and you must ensure quick grasp and understanding of the information the data is pointing at. Traditionally the databases were not of much use as you had to run the data via an extract, extract, transform and then load it before you start exploring the same. This has increased the importance of tools which are used for handling huge amounts of data and not only the analytical systems. Such tools are also found to provide data for training which is required for building the machine-learning models. 

What is Azure Data Explorer?

Azure Data Explorer is a tool for browsing through data, making temporary queries and thus finally bringing the data into a central store. When you intend to work on a cloud-scale it will mean that huge amounts of data will be generated which is quite hard to analyze with the help of traditional tools. Azure Data Explorer is one more example of Microsoft presenting its own internal tools to its customers. It is the scale of data which is being created which has pushed Microsoft to make new tools both for managing the huge amount of data and also many large data centers. Azure Data Explorer thus brings all these tools together and presents a final tool which brings all these in one single tool which can both work with the log files and streaming of data. This necessitates it as a tool for establishing huge and distributed applications either on-premises or in the cloud. 

Azure Data Explorer provides the capability for analysis of humungous amounts of data, the creation of BI dashboards and other reports which assist you in visualizing your data. It is basically a highly fast service for both indexing and querying which is helpful in building real and complicated solutions for huge amounts of data which comes from applications, websites, etc. It can establish a connection with Power BI which is another solution for business analysis. It allows for data visualization and sharing of results with your organization. Many methods of connecting with the Power BI allows interactive analysis of the organizational data like tracking and presenting trends.

Important factors of Azure Data Explorer

Custom Engine for Query: Azure Data Explorer is basically a custom query engine which has a query language of its own which is designed for working with huge amount of data along with a combination of both structured and unstructured data from both the sources.

  • Read-Only Tool: It is basically a read-only tool and is specifically used for working with both logs and column stores.
  • Architecture: It has a distributed architecture which lets it work outside the application flow.
  • Speculative Data Analysis: It allows for speculative data analysis which can tell the code build by you, optimizes for all queries and also helps in building new models which can ultimately be integrated with your machine-learning platform.
  • Low-Latency Ingestion: Many ingestion methods of data from devices like applications, servers, services for particular use cases, are supported by Azure.
  • Fast Read-only query: Azure can query a massive amount of both structured and unstructured data, helps in searching specific text terms, the location of events and can also undertake calculations on structured data. It also continues to refine your queries unless the analysis is complete.
  • Time-Series: Azure is capable of both creation and analysis of many thousands of time-series in seconds. It also comprises native support for creating, manipulating and also analysis of more than one time series.
  • Managed Data Service: Azure automatically scales the data analytics service for meeting your demands. It has no upfront costs or termination fee.  It lays stress on data and not infrastructure.
  • Queries are Cost-Effective: Azure lets you ask many questions without any additional costs. You need to pay hourly and not by the query. Additionally, even storage costs can also be controlled. You may get a database which can automatically put data on the table, but you have the flexibility to pick a retention policy which will, in turn, be based on how long you will want to store your data.
  • Built-in Analytics: Azure Data Explorer serves as a data service for Azure Monitor, Azure Time Series Insights, Windows Defender Advanced Threat Protection etc. It also supports Azure Resource Manager Service endpoints, REST-API etc.

Azure Data Explorer Use Cases:

  • Data Science: Data from Azure Data Explorer is usually visualized by the data scientists by making use of the KQL magic. Data scientists also exchange Python code with KQL queries for experimenting and training machine learning models.
  • Data Analytics: KQL magic is used for querying, analysing and thereby visualizing data.
  • Business Reviews: KQL magic can also be used is also used for reviews of business and products.
  • Security Analysis: Data can be both analysed and visualized by querying data from Azure Data Explorer and making use of the Python ecosystem.


Thus, Azure Data Explorer is a truly revolutionary product which is typically designed for data exploration. You can also find latent insights by the streaming of data. It is endowed with an intuitive language for querying which helps in finding answers in randomly changing data. It can also be used for exploring new possibilities with the data in a cost-effective manner as its focus is on insights and not on infrastructure. Thus, it is easy to use and completely managed data analytics service. It helps in simplifying analytics from all types of streaming data.

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About francis babet Junior   Online

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Joined APSense since, July 26th, 2015, From United States.

Created on Mar 8th 2019 04:53. Viewed 518 times.


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