DBMS > Datastax Enterprise vs. IRONdb vs. Microsoft Azure Data Explorer vs. RDF4J vs. SingleStore
System Properties Comparison Datastax Enterprise vs. IRONdb vs. Microsoft Azure Data Explorer vs. RDF4J vs. SingleStore
Editorial information provided by DB-Engines | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Name | Datastax Enterprise Xexclude from comparison | IRONdb Xexclude from comparison | Microsoft Azure Data Explorer Xexclude from comparison | RDF4J formerly known as Sesame Xexclude from comparison | SingleStore former name was MemSQL Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform. | A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity | Fully managed big data interactive analytics platform | RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage. | MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Wide column store | Time Series DBMS | Relational DBMS column oriented | RDF store | Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Document store Graph DBMS Spatial DBMS Search engine Vector DBMS | 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 Spatial DBMS Time Series DBMS Vector DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | www.datastax.com/products/datastax-enterprise | www.circonus.com/solutions/time-series-database/ | azure.microsoft.com/services/data-explorer | rdf4j.org | www.singlestore.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | docs.datastax.com | docs.circonus.com/irondb/category/getting-started | docs.microsoft.com/en-us/azure/data-explorer | rdf4j.org/documentation | docs.singlestore.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | DataStax | Circonus LLC. | Microsoft | Since 2016 officially forked into an Eclipse project, former developer was Aduna Software. | SingleStore Inc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2011 | 2017 | 2019 | 2004 | 2013 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | 6.8, April 2020 | V0.10.20, January 2018 | cloud service with continuous releases | 8.5, January 2024 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | commercial | commercial | commercial | Open Source Eclipse Distribution License (EDL), v1.0. | commercial free developer edition available | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing. | SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Implementation language | Java | C and C++ | Java | C++, Go | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | Linux OS X | Linux | hosted | Linux OS X Unix Windows | Linux 64 bit version required | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | schema-free | schema-free | Fixed schema with schema-less datatypes (dynamic) | yes RDF Schemas | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes | yes text, numeric, histograms | yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | 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. | no | no | yes | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | no | all fields are automatically indexed | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | SQL-like DML and DDL statements (CQL); Spark SQL | SQL-like query language (Circonus Analytics Query Language: CAQL) | Kusto Query Language (KQL), SQL subset | no | yes but no triggers and foreign keys | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | Proprietary protocol CQL (Cassandra Query Language) TinkerPop Gremlin with DSE Graph | HTTP API | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | Java API RIO RDF Input/Output Sail API SeRQL Sesame RDF Query Language Sesame REST HTTP Protocol SPARQL | Cluster Management API as HTTP Rest and CLI HTTP API JDBC MongoDB API ODBC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | C C# C++ Java JavaScript (Node.js) PHP Python Ruby | .Net C C++ Clojure Erlang Go Haskell Java JavaScript JavaScript (Node.js) Lisp Lua Perl PHP Python R Ruby Rust Scala | .Net Go Java JavaScript (Node.js) PowerShell Python R | Java PHP Python | Bash C C# Java JavaScript (Node.js) Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | no | yes, in Lua | Yes, possible languages: KQL, Python, R | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | yes | no | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Sharding no "single point of failure" | Automatic, metric affinity per node | Sharding Implicit feature of the cloud service | none | Sharding hash partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | configurable replication factor, datacenter aware, advanced replication for edge computing | configurable replication factor, datacenter aware | yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant. | none | Source-replica replication stores two copies of each physical data partition on two separate nodes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | yes | no | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | no can define user-defined aggregate functions for map-reduce-style calculations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency Tunable Consistency consistency level can be individually decided with each write operation | Immediate consistency per node, eventual consistency across nodes | Eventual Consistency Immediate Consistency | Immediate Consistency | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | no | no | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no Atomicity and isolation are supported for single operations | no | no | ACID Isolation support depends on the API used | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes, multi-version concurrency control (MVCC) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes | yes in-memory storage is supported as well | yes All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | no | no | yes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | Access rights for users can be defined per object | no | Azure Active Directory Authentication | no | Fine grained access control via users, groups and roles | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Datastax Enterprise | IRONdb | Microsoft Azure Data Explorer | RDF4J formerly known as Sesame | SingleStore former name was MemSQL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Specific characteristics | DataStax Enterprise is scale-out data infrastructure for enterprises that need to... » more | SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competitive advantages | Supporting the following application requirements: Zero downtime - Built on Apache... » more | SingleStore’s competitive advantages include: Easy and Simplified Architecture with... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typical application scenarios | Applications that must be massively and linearly scalable with 100% uptime and able... » more | Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Key customers | Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and... » more | IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Market metrics | Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top... » more | Customers in various industries worldwide including US and International Industry... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Licensing and pricing models | Annual subscription » more | F ree Tier and Enterprise Edition » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Related products and servicesWe invite representatives of vendors of related products to contact us for presenting information about their offerings here. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Datastax Enterprise | IRONdb | Microsoft Azure Data Explorer | RDF4J formerly known as Sesame | SingleStore former name was MemSQL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | Turbocharge Your Application Development Using WebAssembly With SingleStoreDB Cloud-Based Analytics With SingleStoreDB SingleStore: The Increasing Momentum of Multi-Model Database Systems DataStax and LlamaIndex Partner to Make Building RAG Applications Easier than Ever for GenAI Developers DataStax Introduces Enhanced RAG Capabilities Through Astra DB and NVIDIA Tech DataStax Rolls Out Vector Search for Astra DB to Support Gen AI DataStax adds vector search to boost support for generative AI workloads DataStax goes vector searching with Astra DB – Blocks and Files provided by Google News Application observability firm Apica buys telemetry data startup Circonus and adds more funding Apica gets $6 million in funding and buys Circonus - provided by Google News Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog Azure Data Explorer: Log and telemetry analytics benchmark Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview provided by Google News GraphDB Goes Open Source Ontotext's GraphDB 8.10 Makes Knowledge Graph Experience Faster and Richer provided by Google News SingleStore CEO sees little future for purpose-built vector databases SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development SingleStore adds indexed vector search to Pro Max release for faster AI work – Blocks and Files Leveraging SingleStoreDB Cloud Private Connectivity Using AWS PrivateLink | Amazon Web Services Announcing watsonx.ai and SingleStore for generative AI applications provided by Google News |
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