DBMS > Ignite vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL vs. Warp 10
System Properties Comparison Ignite vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL vs. Warp 10
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| Name | Ignite Xexclude from comparison | Microsoft Azure Data Explorer Xexclude from comparison | PostgreSQL Xexclude from comparison | Spark SQL Xexclude from comparison | Warp 10 Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Description | Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale. | Fully managed big data interactive analytics platform | Widely used open source RDBMS  Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL | Spark SQL is a component on top of 'Spark Core' for structured data processing | TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primary database model | Key-value store Relational DBMS | 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. | Relational DBMS | 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)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 engineTime Series DBMS see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis | Document store Graph DBMS with Apache AgeSpatial DBMS Vector DBMS with pgvector extension | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Website | ignite.apache.org | azure.microsoft.com/services/data-explorer | www.postgresql.org | spark.apache.org/sql | www.warp10.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technical documentation | apacheignite.readme.io/docs | docs.microsoft.com/en-us/azure/data-explorer | www.postgresql.org/docs | spark.apache.org/docs/latest/sql-programming-guide.html | www.warp10.io/content/02_Getting_started | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Developer | Apache Software Foundation | Microsoft | PostgreSQL Global Development Group www.postgresql.org/developer | Apache Software Foundation | SenX | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Initial release | 2015 | 2019 | 1989 1989: Postgres, 1996: PostgreSQL | 2014 | 2015 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current release | Apache Ignite 2.6 | cloud service with continuous releases | 16.3, May 2024 | 3.5.0 ( 2.13), September 2023 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| License Commercial or Open Source | Open Source Apache 2.0 | commercial | Open Source BSD | Open Source Apache 2.0 | Open Source Apache License 2.0 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cloud-based only Only available as a cloud service | no | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Implementation language | C++, Java, .Net | C | Scala | Java | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server operating systems | Linux OS X Solaris Windows | hosted | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | Linux OS X Windows | Linux OS X Windows | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data scheme | yes | Fixed schema with schema-less datatypes (dynamic) | yes | yes | schema-free | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typing predefined data types such as float or date | yes | yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | yes | 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 | yes specific XML-type available, but no XML query functionality. | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary indexes | yes | all fields are automatically indexed | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | ANSI-99 for query and DML statements, subset of DDL | Kusto Query Language (KQL), SQL subset | yes standard with numerous extensions | SQL-like DML and DDL statements | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs and other access methods | HDFS API Hibernate JCache JDBC ODBC Proprietary protocol RESTful HTTP API Spring Data | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | ADO.NET JDBC native C library ODBC streaming API for large objects | JDBC ODBC | HTTP API Jupyter WebSocket | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Supported programming languages | C# C++ Java PHP Python Ruby Scala | .Net Go Java JavaScript (Node.js) PowerShell Python R | .Net C C++ Delphi Java JDBCJavaScript (Node.js) Perl PHP Python Tcl | Java Python R Scala | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-side scripts Stored procedures | yes (compute grid and cache interceptors can be used instead) | Yes, possible languages: KQL, Python, R | user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc. | no | yes WarpScript | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | yes (cache interceptors and events) | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitioning methods Methods for storing different data on different nodes | Sharding | Sharding Implicit feature of the cloud service | partitioning by range, list and (since PostgreSQL 11) by hash | yes, utilizing Spark Core | Sharding based on HBase | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replication methods Methods for redundantly storing data on multiple nodes | yes (replicated cache) | 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 | none | selectable replication factor based on HBase | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Offers an API for user-defined Map/Reduce methods | yes (compute grid and hadoop accelerator) | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency | Eventual Consistency Immediate Consistency | Immediate Consistency | Immediate Consistency based on HBase | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Foreign keys Referential integrity | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | ACID | no | ACID | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Support for making data persistent | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | no | no | no | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| User concepts Access control | Security Hooks for custom implementations | Azure Active Directory Authentication | fine grained access rights according to SQL-standard | no | Mandatory use of cryptographic tokens, containing fine-grained authorizations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Ignite | Microsoft Azure Data Explorer | PostgreSQL | Spark SQL | Warp 10 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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