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Microsoft Azure Data Explorer System Properties
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|Editorial information provided by DB-Engines|
|Name||Microsoft Azure Data Explorer|
|Description||Fully managed big data interactive analytics platform|
|Primary database model||Relational DBMS column oriented|
|Secondary database models||Document store If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell|
Event Store this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Search engine support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis
|Current release||cloud service with continuous releases|
|License Commercial or Open Source||commercial|
|Cloud-based only Only available as a cloud service||yes|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems||hosted|
|Data scheme||Fixed schema with schema-less datatypes (dynamic)|
|Typing predefined data types such as float or date||yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||yes|
|Secondary indexes||all fields are automatically indexed|
|SQL Support of SQL||Kusto Query Language (KQL), SQL subset|
|APIs and other access methods||Microsoft SQL Server communication protocol (MS-TDS)|
RESTful HTTP API
|Supported programming languages||.Net|
|Server-side scripts Stored procedures||Yes, possible languages: KQL, Python, R|
|Triggers||yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy|
|Partitioning methods Methods for storing different data on different nodes||Sharding Implicit feature of the cloud service|
|Replication methods Methods for redundantly storing data on multiple nodes||yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.|
|MapReduce Offers an API for user-defined Map/Reduce methods||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Eventual Consistency|
|Foreign keys Referential integrity||no|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||no|
|Concurrency Support for concurrent manipulation of data||yes|
|Durability Support for making data persistent||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||no|
|User concepts Access control||Azure Active Directory Authentication|
|More information provided by the system vendor|
|Microsoft Azure Data Explorer|
Azure Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. It helps you handle the many data streams emitted by modern software, so you can collect, store, and analyze data. Azure Data Explorer is ideal for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data is used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities. Azure Data Explorer makes it simple to ingest this data and enables you to perform complex ad hoc queries on the data in seconds.
|Typical application scenarios|
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.
Big data logging
Publishers, ad networks, gaming sites, and other web-based businesses rely on large volumes of log data to spot trends, patterns, or anomalies in near-real time. With data exploration, discover insights that help you gain new customers, increase user engagement, and monetize traffic. Use this instant analysis of billions of lines of log data to generate personalized recommendations to visitors, predict which results and content suggestions they’re most likely to click on, and learn which content will lead to new subscriptions and purchases.
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. Developers are using this data to monitor service and improve application performance, while business users are discovering user trends, creating personalized experiences, and developing new revenue streams.
Microsoft, DocuSign, Taboola, Bosch, Siemens Healthineers, Bühler, Ecolab, Zoomd
Azure Data Explorer is the data service for Azure Monitor, Azure Time Series Insights, and Windows Defender Advanced Threat Protection and many more. It's running on hundreds of thousands of Azure cores.
Related products and services
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|Microsoft Azure Data Explorer|
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