DBMS > Microsoft Azure Data Explorer vs. PostgreSQL vs. Solr vs. TimesTen vs. Vertica
System Properties Comparison Microsoft Azure Data Explorer vs. PostgreSQL vs. Solr vs. TimesTen vs. Vertica
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Name | Microsoft Azure Data Explorer Xexclude from comparison | PostgreSQL Xexclude from comparison | Solr Xexclude from comparison | TimesTen Xexclude from comparison | Vertica OpenText™ Vertica™ Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | Fully managed big data interactive analytics platform | Widely used open source RDBMS Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL | A widely used distributed, scalable search engine based on Apache Lucene | In-Memory RDBMS compatible to Oracle | Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Relational DBMS column oriented | Relational DBMS with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module. | Search engine | Relational DBMS | 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) Spatial DBMS 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 | Document store Graph DBMS with Apache Age Spatial DBMS Vector DBMS with pgvector extension | Spatial DBMS | Spatial DBMS Time Series DBMS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | azure.microsoft.com/services/data-explorer | www.postgresql.org | solr.apache.org | www.oracle.com/database/technologies/related/timesten.html | www.vertica.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | docs.microsoft.com/en-us/azure/data-explorer | www.postgresql.org/docs | solr.apache.org/resources.html | docs.oracle.com/database/timesten-18.1 | vertica.com/documentation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Microsoft | PostgreSQL Global Development Group www.postgresql.org/developer | Apache Software Foundation | Oracle, TimesTen Performance Software, HP originally founded in HP Labs it was acquired by Oracle in 2005 | OpenText previously Micro Focus and Hewlett Packard | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2019 | 1989 1989: Postgres, 1996: PostgreSQL | 2006 | 1998 | 2005 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | cloud service with continuous releases | 16.3, May 2024 | 9.6.0, April 2024 | 11 Release 2 (11.2.2.8.0) | 12.0.3, January 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | commercial | Open Source BSD | Open Source Apache Version 2 | commercial | commercial Limited community edition free | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | yes | no | no | no | no on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Implementation language | C | Java | C++ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | hosted | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | All OS with a Java VM runs as a servlet in servlet container (e.g. Tomcat, Jetty is included) | AIX HP-UX Linux OS X Solaris SPARC/x86 Windows | Linux | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | Fixed schema with schema-less datatypes (dynamic) | yes | yes Dynamic Fields enables on-the-fly addition of new fields | yes | Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | yes supports customizable data types and automatic typing | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | yes specific XML-type available, but no XML query functionality. | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | all fields are automatically indexed | yes | yes All search fields are automatically indexed | yes | No Indexes Required. Different internal optimization strategy, but same functionality included. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | Kusto Query Language (KQL), SQL subset | yes standard with numerous extensions | Solr Parallel SQL Interface | yes | Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | ADO.NET JDBC native C library ODBC streaming API for large objects | Java API RESTful HTTP/JSON API | JDBC ODBC ODP.NET Oracle Call Interface (OCI) | ADO.NET JDBC Kafka Connector ODBC RESTful HTTP API Spark Connector vSQL character-based, interactive, front-end utility | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | .Net Go Java JavaScript (Node.js) PowerShell Python R | .Net C C++ Delphi Java JDBC JavaScript (Node.js) Perl PHP Python Tcl | .Net Erlang Java JavaScript any language that supports sockets and either XML or JSON Perl PHP Python Ruby Scala | C C++ Java PL/SQL | C# C++ Go Java JavaScript (Node.js) Perl PHP Python R | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | Yes, possible languages: KQL, Python, R | user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc. | Java plugins | PL/SQL | yes, PostgreSQL PL/pgSQL, with minor differences | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes | yes User configurable commands triggered on index changes | no | yes, called Custom Alerts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Sharding Implicit feature of the cloud service | partitioning by range, list and (since PostgreSQL 11) by hash | Sharding | none | horizontal partitioning, hierarchical partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | Source-replica replication other methods possible by using 3rd party extensions | yes | Multi-source replication Source-replica replication | Multi-source replication One, or more copies of data replicated across nodes, or object-store used for repository. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce | no | no Bi-directional Spark integration | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency Immediate Consistency | Immediate Consistency | Eventual Consistency | Immediate Consistency or Eventual Consistency depending on configuration | Immediate Consistency | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | yes | no | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no | ACID | optimistic locking | ACID | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes | yes by means of logfiles and checkpoints | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | no | no | yes | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | Azure Active Directory Authentication | fine grained access rights according to SQL-standard | yes | fine grained access rights according to SQL-standard | fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Microsoft Azure Data Explorer | PostgreSQL | Solr | TimesTen | Vertica OpenText™ Vertica™ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Specific characteristics | Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competitive advantages | Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typical application scenarios | Communication and network analytics, Embedded analytics, Fraud monitoring and Risk... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Key customers | Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Licensing and pricing models | Cost-based models and subscription-based models are both available. One license is... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Microsoft Azure Data Explorer | PostgreSQL | Solr | TimesTen | Vertica OpenText™ Vertica™ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | PostgreSQL is the DBMS of the Year 2023 Snowflake is the DBMS of the Year 2022, defending the title from last year Snowflake is the DBMS of the Year 2021 | Elasticsearch replaced Solr as the most popular search engine Enterprise Search Engines almost double their popularity in the last 12 months The DB-Engines ranking includes now search engines | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Conferences, events and webinars | Monitoring PostgreSQL with Redgate Monitor | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Recent citations in the news | Azure Data Explorer: Log and telemetry analytics benchmark Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog Controlling costs in Azure Data Explorer using down-sampling and aggregation Individually great, collectively unmatched: Announcing updates to 3 great Azure Data Services Log and Telemetry Analytics Performance Benchmark provided by Google News | Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda | Amazon ... SolarWinds Unveils Enhanced Database Performance Analyzer with Advanced PostgreSQL Support General availability: Microsoft Entra ID integration with Azure Cosmos DB for PostgreSQL | Azure updates ServiceNow trades MariaDB for RaptorDB (PostgreSQL) Best of Makers: PostgreSQL Takes a New Turn provided by Google News | (SOLR) Technical Data SOLR-led walkout demands better conditions for Compass workers SOLR hosts teach-in of labor movements at Northwestern SearchStax Launches Serverless Solr Service to Accelerate Cloud-Native Application Development SOLR hosts May Day amid ongoing contract negotiations provided by Google News | Oracle starts peddling Exalytics in-memory appliance provided by Google News | Stonebraker Seeks to Invert the Computing Paradigm with DBOS How Embedded Analytics Help ISVs Overcome Challenges OpenText expands enterprise portfolio with AI and Micro Focus integrations Postgres pioneer Michael Stonebraker promises to upend the database once more OpenText integrates Micro Focus tech through Cloud Editions 23.3 provided by Google News |
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