DBMS > CrateDB vs. Datastax Enterprise vs. IBM Db2 Event Store vs. Microsoft Access vs. Vertica
System Properties Comparison CrateDB vs. Datastax Enterprise vs. IBM Db2 Event Store vs. Microsoft Access vs. Vertica
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Name | CrateDB Xexclude from comparison | Datastax Enterprise Xexclude from comparison | IBM Db2 Event Store Xexclude from comparison | Microsoft Access Xexclude from comparison | Vertica OpenText™ Vertica™ Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | Distributed Database based on Lucene | 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. | Distributed Event Store optimized for Internet of Things use cases | Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.) | 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 | Document store Spatial DBMS Search engine Time Series DBMS Vector DBMS | Wide column store | Event Store Time Series DBMS | Relational DBMS | Relational DBMS Column oriented | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Relational DBMS | Document store Graph DBMS Spatial DBMS Search engine Vector DBMS | Spatial DBMS Time Series DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | cratedb.com | www.datastax.com/products/datastax-enterprise | www.ibm.com/products/db2-event-store | www.microsoft.com/en-us/microsoft-365/access | www.vertica.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | cratedb.com/docs | docs.datastax.com | www.ibm.com/docs/en/db2-event-store | developer.microsoft.com/en-us/access | vertica.com/documentation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Crate | DataStax | IBM | Microsoft | OpenText previously Micro Focus and Hewlett Packard | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2013 | 2011 | 2017 | 1992 | 2005 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | 6.8, April 2020 | 2.0 | 1902 (16.0.11328.20222), March 2019 | 12.0.3, January 2023 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | Open Source | commercial | commercial free developer edition available | commercial Bundled with Microsoft Office | commercial Limited community edition free | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | no | no | no | no on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform. | 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Implementation language | Java | Java | C and C++ | C++ | C++ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support | Linux OS X | Linux Linux, macOS, Windows for the developer addition | Windows Not a real database server, but making use of DLLs | Linux | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | Flexible Schema (defined schema, partial schema, schema free) | schema-free | yes | 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 | yes | 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. | no | no | no | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | yes | no | yes | No Indexes Required. Different internal optimization strategy, but same functionality included. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | yes, but no triggers and constraints, and PostgreSQL compatibility | SQL-like DML and DDL statements (CQL); Spark SQL | yes through the embedded Spark runtime | yes but not compliant to any SQL standard | Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | ADO.NET JDBC ODBC PostgreSQL wire protocol Prometheus Remote Read/Write RESTful HTTP API | Proprietary protocol CQL (Cassandra Query Language) TinkerPop Gremlin with DSE Graph | ADO.NET DB2 Connect JDBC ODBC RESTful HTTP API | ADO.NET DAO ODBC OLE DB | ADO.NET JDBC Kafka Connector ODBC RESTful HTTP API Spark Connector vSQL character-based, interactive, front-end utility | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | .NET Erlang Go community maintained client Java JavaScript (Node.js) community maintained client Perl community maintained client PHP Python R Ruby community maintained client Scala community maintained client | C C# C++ Java JavaScript (Node.js) PHP Python Ruby | C C# C++ Cobol Delphi Fortran Go Java JavaScript (Node.js) Perl PHP Python R Ruby Scala Visual Basic | C C# C++ Delphi Java (JDBC-ODBC) VBA Visual Basic.NET | C# C++ Go Java JavaScript (Node.js) Perl PHP Python R | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | user defined functions (Javascript) | no | yes | yes since Access 2010 using the ACE-engine | yes, PostgreSQL PL/pgSQL, with minor differences | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | no | yes | no | yes since Access 2010 using the ACE-engine | yes, called Custom Alerts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | Sharding | Sharding no "single point of failure" | Sharding | none | horizontal partitioning, hierarchical partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | Configurable replication on table/partition-level | configurable replication factor, datacenter aware, advanced replication for edge computing | Active-active shard replication | none | 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 | no | yes | no | no | no Bi-directional Spark integration | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency Read-after-write consistency on record level | Immediate Consistency Tunable Consistency consistency level can be individually decided with each write operation | Eventual Consistency | Immediate Consistency | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | no | no | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no unique row identifiers can be used for implementing an optimistic concurrency control strategy | no Atomicity and isolation are supported for single operations | no | ACID but no files for transaction logging | ACID | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | No - written data is immutable | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage | yes but no files for transaction logging | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | no | yes | yes | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | rights management via user accounts | Access rights for users can be defined per object | fine grained access rights according to SQL-standard | no a simple user-level security was built in till version Access 2003 | fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CrateDB | Datastax Enterprise | IBM Db2 Event Store | Microsoft Access | Vertica OpenText™ Vertica™ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Specific characteristics | The enterprise database for time series, documents, and vectors. Distributed - Native... » more | DataStax Enterprise is scale-out data infrastructure for enterprises that need to... » more | Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competitive advantages | Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling... » more | Supporting the following application requirements: Zero downtime - Built on Apache... » more | Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typical application scenarios | IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics... » more | Applications that must be massively and linearly scalable with 100% uptime and able... » more | Communication and network analytics, Embedded analytics, Fraud monitoring and Risk... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Key customers | Across all continents, CrateDB is used by companies of all sizes to meet the most... » more | Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and... » more | Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Market metrics | The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®... » more | Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Licensing and pricing models | See CrateDB pricing > » more | Annual subscription » more | Cost-based models and subscription-based models are both available. One license is... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CrateDB | Datastax Enterprise | IBM Db2 Event Store | Microsoft Access | Vertica OpenText™ Vertica™ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | MS Access drops in DB-Engines Ranking Microsoft SQL Server regained rank 2 in the DB-Engines popularity ranking New DB-Engines Ranking shows the popularity of database management systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Recent citations in the news | CrateDB Appoints Sergey Gerasimenko as New CTO CrateDB Announces Availability of CrateDB on Google Cloud Marketplace CrateDB Partners with HiveMQ to Deliver a Seamless Data Management Architecture for IoT How We Designed CrateDB as a Realtime SQL DBMS for the Internet of Things Crate.io unboxes clustered SQL CrateDB, decamps to California provided by Google News | 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 | Advancements in streaming data storage, real-time analysis and machine learning IBM Builds New Ultra-Fast Platform for Hoovering Up and Analyzing Data from Anywhere How IBM Is Turning Db2 into an 'AI Database' Best cloud databases of 2022 Why a robust data management strategy is essential today | IBM HDM provided by Google News | Abusing Microsoft Access "Linked Table" Feature to Perform NTLM Forced Authentication Attacks Hackers Exploit Microsoft Access Feature to Steal Windows User’s NTLM Tokens MS access program to increase awareness and independence of those living with MS and disability After installing Navisworks, Office 2016 (32-bit) applications stopped launching How to Connect MS Access to MySQL via ODBC Driver provided by Google News | OCI Object Storage Completes Technical Validation of Vertica in Eon Mode MapR Hadoop Upgrade Runs HP Vertica Vertica by OpenText and Anritsu Sign New Deal for Next-Gen Architecture and 5G Network Capabilities Vertica: Big Data Analytics On-Premises, in the Cloud, or on Hadoop Stonebraker Seeks to Invert the Computing Paradigm with DBOS provided by Google News |
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