DBMS > Microsoft Azure Data Explorer vs. SiriDB vs. SpatiaLite
System Properties Comparison Microsoft Azure Data Explorer vs. SiriDB vs. SpatiaLite
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|Editorial information provided by DB-Engines
|Microsoft Azure Data Explorer Xexclude from comparison
|SiriDB Xexclude from comparison
|SpatiaLite Xexclude from comparison
|Fully managed big data interactive analytics platform
|Open Source Time Series DBMS
|Spatial extension of SQLite
|Primary database model
|Relational DBMS column oriented
|Time Series DBMS
|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
|cloud service with continuous releases
|5.0.0, August 2020
|License Commercial or Open Source
|Open Source MIT License
|Open Source MPL 1.1, GPL v2.0 or LGPL v2.1
|Cloud-based only Only available as a cloud service
|DBaaS offerings (sponsored links) Database as a Service
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|Server operating systems
|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
|yes Numeric data
|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.
|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
|Server-side scripts Stored procedures
|Yes, possible languages: KQL, Python, R
|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
|Spark connector (open source): github.com/Azure/azure-kusto-spark
|Consistency concepts Methods to ensure consistency in a distributed system
|Foreign keys Referential integrity
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data
|Concurrency Support for concurrent manipulation of data
|Durability Support for making data persistent
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.
|User concepts Access control
|Azure Active Directory Authentication
|simple rights management via user accounts
|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...
|Kusto Query Language (innovative query language, optimized for high performance data...
|Typical application scenarios
|IoT applications IoT devices generate billions of sensor readings. Normalizing and...
|Microsoft, DocuSign, Taboola, Bosch, Siemens Healthineers, Bühler, Ecolab, Zoomd
|Azure Data Explorer is the data service for Azure Monitor, Azure Time Series Insights,...
|Licensing and pricing models
|An Azure Data Explorer cluster is a pair of engine and data management clusters which...
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|Microsoft Azure Data Explorer
|DB-Engines blog posts
Spatial database management systems Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog Data Explorer processes unlabeled visual data, boosting creation of production-ready AI models Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview Why the Azure community should start planning for Microsoft Fabric today provided by Google News SiriDB tijdreeks database analyseert time series data vanuit elke bron provided by Google News
Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog
Data Explorer processes unlabeled visual data, boosting creation of production-ready AI models
Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview
Why the Azure community should start planning for Microsoft Fabric today
provided by Google News
SiriDB tijdreeks database analyseert time series data vanuit elke bron
provided by Google News
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