DBMS > ClickHouse vs. Datastax Enterprise vs. Microsoft Access vs. Oracle Berkeley DB vs. Weaviate
System Properties Comparison ClickHouse vs. Datastax Enterprise vs. Microsoft Access vs. Oracle Berkeley DB vs. Weaviate
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Name | ClickHouse Xexclude from comparison | Datastax Enterprise Xexclude from comparison | Microsoft Access Xexclude from comparison | Oracle Berkeley DB Xexclude from comparison | Weaviate Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering. | 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. | 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.) | Widely used in-process key-value store | An AI-native realtime vector database engine that integrates scalable machine learning models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Relational DBMS | Wide column store | Relational DBMS | Key-value store supports sorted and unsorted key sets Native XML DBMS in the Oracle Berkeley DB XML version | Vector DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Time Series DBMS | Document store Graph DBMS Spatial DBMS Search engine Vector DBMS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | clickhouse.com | www.datastax.com/products/datastax-enterprise | www.microsoft.com/en-us/microsoft-365/access | www.oracle.com/database/technologies/related/berkeleydb.html | github.com/weaviate/weaviate weaviate.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | clickhouse.com/docs | docs.datastax.com | developer.microsoft.com/en-us/access | docs.oracle.com/cd/E17076_05/html/index.html | weaviate.io/developers/weaviate | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Clickhouse Inc. | DataStax | Microsoft | Oracle originally developed by Sleepycat, which was acquired by Oracle | Weaviate B.V. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2016 | 2011 | 1992 | 1994 | 2019 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | v23.12.1.1368-stable, December 2023 | 6.8, April 2020 | 1902 (16.0.11328.20222), March 2019 | 18.1.40, May 2020 | 1.19, May 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | Open Source Apache 2.0 | commercial | commercial Bundled with Microsoft Office | Open Source commercial license available | Open Source commercial license available with Weaviate Enterprise | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | no | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Implementation language | C++ | Java | C++ | C, Java, C++ (depending on the Berkeley DB edition) | Go | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | FreeBSD Linux macOS | Linux OS X | Windows Not a real database server, but making use of DLLs | AIX Android FreeBSD iOS Linux OS X Solaris VxWorks Windows | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | yes | schema-free | yes | schema-free | yes, maps to GraphQL interface | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes | yes | yes | no | yes string, int, float, geo point, date, cross reference, fuzzy references | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 only with the Berkeley DB XML edition | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | yes | yes | yes | yes | yes all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | Close to ANSI SQL (SQL/JSON + extensions) | SQL-like DML and DDL statements (CQL); Spark SQL | yes but not compliant to any SQL standard | yes SQL interfaced based on SQLite is available | GraphQL is used as query language | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | gRPC HTTP REST JDBC MySQL wire protocol ODBC PostgreSQL wire protocol Proprietary protocol | Proprietary protocol CQL (Cassandra Query Language) TinkerPop Gremlin with DSE Graph | ADO.NET DAO ODBC OLE DB | GraphQL query language RESTful HTTP/JSON API | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | C# 3rd party library C++ Elixir 3rd party library Go 3rd party library Java 3rd party library JavaScript (Node.js) 3rd party library Kotlin 3rd party library Nim 3rd party library Perl 3rd party library PHP 3rd party library Python 3rd party library R 3rd party library Ruby 3rd party library Rust Scala 3rd party library | C C# C++ Java JavaScript (Node.js) PHP Python Ruby | C C# C++ Delphi Java (JDBC-ODBC) VBA Visual Basic.NET | .Net Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET others Third-party libraries to manipulate Berkeley DB files are available for many languages C C# C++ Java JavaScript (Node.js) 3rd party binding Perl Python Tcl | JavaScript / TypeScript Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | yes | no | yes since Access 2010 using the ACE-engine | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | no | yes | yes since Access 2010 using the ACE-engine | yes only for the SQL API | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | key based and custom | Sharding no "single point of failure" | none | none | Sharding | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages. | configurable replication factor, datacenter aware, advanced replication for edge computing | none | Source-replica replication | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | no | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency | Immediate Consistency Tunable Consistency consistency level can be individually decided with each write operation | Eventual Consistency | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | no | no Atomicity and isolation are supported for single operations | ACID but no files for transaction logging | ACID | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes but no files for transaction logging | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | yes | yes | yes | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication. | Access rights for users can be defined per object | no a simple user-level security was built in till version Access 2003 | no | API Keys OpenID Connect Discovery | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ClickHouse | Datastax Enterprise | Microsoft Access | Oracle Berkeley DB | Weaviate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Specific characteristics | DataStax Enterprise is scale-out data infrastructure for enterprises that need to... » more | Weaviate is an open source vector database that is robust, scalable, cloud-native,... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competitive advantages | Supporting the following application requirements: Zero downtime - Built on Apache... » more | Flexible deployment - Free, open source or fully-managed cloud vector database service... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typical application scenarios | Applications that must be massively and linearly scalable with 100% uptime and able... » more | As a database supporting the development of generative AI and semantic search applications... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Key customers | Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and... » more | All companies that have data. » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Market metrics | Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top... » more | As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Licensing and pricing models | Annual subscription » more | Weaviate is open-source, and free to use. Weaviate is also available as a fully managed... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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3rd parties | Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics. » more DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale. » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ClickHouse | Datastax Enterprise | Microsoft Access | Oracle Berkeley DB | Weaviate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | Weaviate, an ANN Database with CRUD support Why Clickhouse Should Be Your Next Database ClickHouse Cloud & Amazon S3 Express One Zone: Making a blazing fast analytical database even faster | Amazon ... ClickHouse Announces Launch of ClickPipes Can LLMs Replace Data Analysts? Getting Answers Using SQL ClickHouse Announces ClickPipes: A Continuous Data Ingestion Service for ClickHouse Cloud provided by Google News DataStax to Deliver High-performance RAG Solution with 20x Faster Embeddings and Indexing, at 80% Lower Cost ... DataStax and LlamaIndex Partner to Make Building RAG Applications Easier than Ever for GenAI Developers DataStax Announces Vector Search for DataStax Enterprise: Bringing the Power of Generative AI to Any Cloud, Hybrid ... DataStax Rolls Out Vector Search for Astra DB to Support Gen AI DataStax adds vector search to boost support for generative AI workloads provided by Google News MS access program to increase awareness and independence of those living with MS and disability Abusing Microsoft Access "Linked Table" Feature to Perform NTLM Forced Authentication Attacks Hackers Exploit Microsoft Access Feature to Steal Windows User’s NTLM Tokens After installing Navisworks, Office 2016 (32-bit) applications stopped launching ACCDE File (What It Is and How to Open One) provided by Google News Fedora Looking To Transition The RPM Database From Berkeley DB To SQLite Margo Seltzer Named ACM Athena Lecturer for Technical and Mentoring Contributions ACM recognizes far-reaching technical achievements with special awards Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag The importance of bitcoin nodes and how to start one provided by Google News Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ... Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases Foley Represents Cortical Ventures in $50M Series B Round for Weaviate Weaviate Raises $50 Million Series B Funding to Meet Soaring Demand for AI Native Vector Database Technology ... provided by Google News |
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