Top 425 Databases Compared

Compare & Find the Perfect Database For Your Project.

Industries:All Finance Telecommunications Healthcare Retail

Use Cases:All Real-Time Analytics Data Storage Data Warehousing Fraud Detection

Database Types:All Distributed Relational Document Analytical

Query Languages:All SQL NoSQL REST SPARQL

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Database	Strengths	Weaknesses	Type	Visits	GH
Redis // 2009	In-memory data store, High performance, Flexible data structures, Simple and powerful API	Limited durability, Single-threaded structure	In-Memory, Key-Value	706.2k	67.1k
Prometheus // 2012	Powerful querying, Flexible, Robust alerting	Limited long-term storage, Basic UI	Time Series	233.5k	55.8k
etcd // 2013	High availability, Consistent, Reliable	Limited to key-value storage, Not suited for large datasets	Key-Value, Distributed	16.2k	47.9k
Meilisearch // 2019	Real-time search capabilities, Easy integration with various platforms	Limited advanced query functionalities, Focus on text search primarily	Search Engine	16.8k	47.5k
Apache Spark // 2014	Fast processing, Scalability, Wide language support	Memory consumption, Complexity	Analytical, Distributed, Streaming	5.8m	40.0k
ClickHouse // 2016	Fast queries, Efficient storage, Columnar storage	Limited transaction support, Complex configuration	Analytical, Columnar, Distributed	233.4k	37.8k
TiDB // 2016	Horizontal scalability, Strong consistency, High availability, MySQL compatibility	Complex architecture, Relatively new community support	Relational, NewSQL, Distributed	163.5k	37.3k
LevelDB // 2011	High read/write performance, Simple and lightweight, Optimized for fast storage	Limited to key-value storage, Not a relational database, No built-in replication	Key-Value, Embedded	0.0	36.6k
Milvus // 2019	Open-source vector database, Efficient for similarity search, Supports large-scale data	Limited to specific use cases, Complexity in high-dimensional data handling	Machine Learning, Vector DBMS	90.7k	30.8k
CockroachDB // 2015	Distributed SQL, Strong consistency, High availability and reliability	Relatively new technology, Complex to set up	Relational, Distributed, NewSQL	96.1k	30.2k
InfluxDB // 2013	Optimized for time series data, High-performance writes and queries	Limited SQL support, Vertical scaling limitations	Time Series	147.8k	29.0k
RocksDB // 2013	High performance for write-heavy workloads, Optimized for fast storage environments	Complex API, Lack of built-in replication	Key-Value, Embedded	12.9k	28.7k
SurrealDB // 2021	Highly scalable, Multi-model database, Supports SQL	Relatively new in the market, Limited community support	Document, Graph, Relational	12.5k	27.5k
RethinkDB // 2009	Real-time changes to query results, JSON document storage	Limited active development, Not as popular as other NoSQL options	Document, Distributed	2.8k	26.8k
MongoDB // 2009	Document-oriented, Scalable, Flexible schema	Consistency model, Memory usage	Document, NoSQL	2.9m	26.4k
Dragonfly // 2022	High throughput, Low latency	Early stage, Limited documentation	In-Memory, Key-Value	99.7k	25.9k
DuckDB // 2018	Lightweight and fast, In-memory analytics	Limited scalability, Single-node only	Analytical, Columnar	40.3k	24.4k
Apache Flink // 2011	Highly scalable, Real-time data processing, Fault-tolerant	Complexity in setup and management, Steeper learning curve	Streaming, Distributed	5.8m	24.1k
TDengine // 2018	Time-series optimized, Lightweight and efficient, Built-in clustering	Limited support for complex queries, Smaller user community	Time Series, Distributed	2.4k	23.4k
Typesense // 2018	Fast and Relevant Search, Easy to Use API	Limited Scalability, Development Community	Search Engine	28.1k	21.2k
Qdrant // 2020	High-performance vector search, Easy to use, Open source	Relatively new with limited ecosystem, Limited query capabilities	Vector DBMS	27.0k	20.7k
Dgraph // 2017	Graph-based data model, High throughput, Scalable architecture	Steeper learning curve, Fewer integrations	Graph, Distributed	21.3k	20.4k
Vitess // 2011	Scalability, Efficiency with MySQL, Cloud-native, High availability	Complex setup, Limited support for non-MySQL databases	Distributed, Relational	15.1k	18.7k
Dolt // 2019	Git-like version control for data, Facilitates collaboration and branching	Relatively new with limited adoption, Potential performance issues with very large datasets	Relational, Distributed	30.2k	18.0k
TimescaleDB // 2018	Excellent time-series support, Built on PostgreSQL	Requires PostgreSQL knowledge, Limited features compared to specialized DBMS	Relational, Time Series	146.3k	17.9k
Valkey // 2024	High availability, Low latency, Rich data structures, Open-source licensing	Emerging community support, Developing documentation	In-Memory, Key-Value, Distributed	19.0k	17.4k
PouchDB // 2012	Offline capabilities, Synchronizes with CouchDB, JavaScript based	Limited scalability, Single-node architecture	Document, Embedded	16.0k	16.9k
PostgreSQL // 1996	Open-source, Extensible, Strong support for advanced queries	Complex configuration, Performance tuning can be complex	Relational, Object-Oriented, Document	1.5m	16.3k
Presto // 2012	Distributed SQL query engine, Query across diverse data sources	Not a full database solution, Requires configuration	Distributed, Analytical	31.6k	16.1k
Chroma // 2022	Optimized for handling vector data, Real-time processing capabilities	New technology with a smaller community, Limited integrations compared to established systems	Vector DBMS	0	15.5k
QuestDB // 2019	High-performance for time-series data, SQL compatibility, Fast ingestion	Limited ecosystem, Relatively newer database	Time Series, Relational	32.5k	14.6k
FoundationDB // 2012	ACID transactions, Fault tolerance, Scalability	Limited to key-value data model, Complex configuration	Distributed, Key-Value	7.4k	14.6k
Badger // 2017	High performance, Efficient key-value storage engine	Key-value store specific limitations, Limited to embedded scenarios	Key-Value, Embedded	21.3k	14.0k
ScyllaDB // 2015	Extremely fast, Compatible with Apache Cassandra, Low latency	Limited built-in query language, Requires managing infrastructure	Distributed, Wide Column	69.4k	13.6k
ArangoDB // 2011	Multi-model capabilities, Flexible data modeling, High performance	Complexity in setup, Learning curve for AQL	Distributed, Document, Graph	16.6k	13.6k
Memcached // 2003	High-performance, Distributed, Simple design	No persistence, No redundancy, Limited querying capabilities	In-Memory, Key-Value	13.6k	13.6k
Apache Druid // 2011	Sub-second OLAP queries, Real-time analytics, Scalable columnar storage	Complexity in deployment and configurations, Learning curve for query optimization	Analytical, Columnar, Distributed	5.8m	13.5k
Neo4j // 2007	Efficient for graph-based queries, Supports ACID transactions, Good visualization tools	Not suitable for very large datasets, Steep learning curve for complex queries	Graph	290.3k	13.4k
SQL.JS // 2013	Runs entirely in the browser, No server setup required, Supports SQL standard	Limited storage capabilities, Dependent on browser resources	Relational, Embedded	727	12.8k
Apache Doris // 2017	Highly scalable, Real-time analytics oriented	Relatively new, Smaller community	Analytical, Columnar	5.8m	12.8k
VictoriaMetrics // 2018	Time-series optimizations, Scalability, Open-source	Narrow focus on time-series data, Limited community compared to Prometheus	Time Series	30.2k	12.4k
Weaviate // 2018	Built-in machine learning, Vector-based similarity searches	Limited support for complex queries, Relatively new technology	Vector DBMS	70.2k	11.5k
KeyDB // 2019	High-performance, Multi-threaded, Compatible with Redis	Relatively new with a smaller community, Potential compatibility issues with Redis extensions	In-Memory, Key-Value	9.5k	11.5k
MySQL // 1995	Open-source, Wide adoption, Reliable	Limited scalability for large data volumes	Relational	3.2m	10.9k
NebulaGraph // 2019	High performance on graph data, Horizontal scalability	Relatively new with a growing community, Complex to deploy and manage for beginners	Graph	10.8k	10.8k
Citus // 2011	Distributed SQL, Scalable PostgreSQL, Performance for big data	Requires PostgreSQL expertise, Complex query optimization	Distributed, Relational	9.7k	10.6k
Trino // 2012	Highly scalable, Low latency query execution, Supports multiple data sources	Memory intensive, Complex configuration	Distributed, Analytical	35.7k	10.5k
Microsoft SQL Server // 1989	Integration with Microsoft products, Business intelligence capabilities	Runs best on Windows platforms, License costs	Relational, In-Memory	723.2m	10.1k
OpenSearch // 2021	Open source, Scalable, Real-time search and analytics	Relatively new, Less enterprise support compared to Elasticsearch	Search Engine, Distributed	99.1k	9.8k
Manticore Search // 2017	High-performance full-text search, Real-time synchronization with SQL databases, Open-source and community-driven	Limited non-search capabilities, Smaller community compared to other search engines	Search Engine	5.0k	9.1k
YugabyteDB // 2017	High availability, Horizontal scalability, Open source	Relatively new, less mature, Smaller community compared to older databases	Distributed, NewSQL	37.6k	9.0k
StarRocks // 2020	Fast query performance, Unified data model, Scalability	Relatively new software	Analytical, Relational, Distributed	51.9k	9.0k
Apache Cassandra // 2008	High availability, Linear scalability, Fault tolerant	Complexity of operation and maintenance, Limited query language	Distributed, Wide Column	5.8m	8.9k
Immudb // 2019	Immutable, Cryptographically verifiable	Relatively new, Limited ecosystem	Blockchain, Distributed, In-Memory	1.8k	8.6k
LiteDB // 2016	Single-file database, Lightweight and fast, No SQL server required	Limited to C# ecosystem, Not suitable for very large scale applications	Document, Embedded	3.4k	8.6k
OceanBase // 2010	High availability, Strong consistency, Horizontal scalability	Complex setup, Limited community support	Distributed, NewSQL	82.9k	8.4k
BoltDB // 2013	Lightweight, Embedded	Limited scalability, Single-reader limitation	Key-Value, Embedded	1.1m	8.3k
Deep Lake // 2020	Optimized for AI and ML, Efficient data versioning	Complexity in integration, Niche domain focus	Machine Learning, Vector DBMS	28.9k	8.2k
Databend // 2021	High-performance OLAP, Elastic scalability	Feature maturity, Community size	Analytical, Distributed	0	7.9k
RisingWave // 2021	Real-time analytics, Scalability	Nascent ecosystem, Limited user documentation	Streaming, NewSQL	34.5k	7.1k
AlaSQL // 2014	Lightweight and fast, Browser-based data processing, Flexible and SQL-like	Not suitable for large datasets, Limited to JavaScript environments	In-Memory	0.0	7.0k
Lovefield // 2015	Client-side database, Supports SQL-like queries in JavaScript, Optimized for web applications	Limited to client-side usage, No longer actively maintained	Relational, In-Memory	0.0	6.8k
LokiJS // 2014	In-memory database, Lightweight, Fast	Limited scalability, No built-in persistence	In-Memory	0	6.8k
SQLite // 2000	Serverless, Lightweight, Broadly supported	Limited to single-user access, Not suitable for high write loads	Relational, Embedded	487.7k	6.7k
CouchDB // 2005	Easy replication, Schema-free JSON documents, High availability	Not designed for complex queries, Slower than some NoSQL databases	Document, Distributed	5.8m	6.3k
IBM Cloudant // 2014	Highly scalable, Managed cloud service, Fully integrated with IBM Cloud	Limited offline support, Smaller ecosystem compared to other NoSQL databases	Document, Distributed	13.4m	6.3k
Hazelcast // 2008	Distributed in-memory data grid, High performance and availability	Complex cluster management, Potential JVM memory limits	In-Memory, Distributed	49.2k	6.2k
Vespa // 2017	Scalable search and recommendation engine, Real-time data processing, Open source	Niche market, Requires specialized knowledge	Distributed, Search Engine	5.1k	5.8k
MariaDB // 2009	Open-source, MySQL compatibility, Robust community support	Lesser enterprise adoption compared to MySQL, Feature differences with MySQL	Relational	176.4k	5.7k
Apache IoTDB // 2018	Highly efficient for time series data, Supports complex analytics, Integrated with IoT ecosystems	Limited support for transactional workloads, Relatively new and evolving	Time Series	5.8m	5.6k
Apache Hive // 2010	Batch processing, Integration with Hadoop ecosystem, SQL-like querying	Not suited for real-time analytics, Higher latency	Distributed, Relational	5.8m	5.6k
Apache Pinot // 2014	Real-time analytics, High query performance, Scalable	Complex setup, Relatively steep learning curve	Distributed	5.8m	5.5k
JanusGraph // 2017	Scalable graph data storage, Open source, Supports a variety of backends	Complex setup, Requires integration with other tools for full functionality	Graph, Distributed	1.7k	5.3k
EventStoreDB // 2012	Strong event sourcing features, Efficient stream processing	Requires expertise in event-driven architectures, Limited traditional RDBMS support	Event Stores, Streaming	9.8k	5.3k
Apache HBase // 2008	Scalability, Strong consistency, Integrates with Hadoop	Complex configuration, Requires Hadoop	Wide Column, Distributed	5.8m	5.2k
OpenTSDB // 2011	Scalable time series database, Strong community support, Highly optimized for large-scale data	Complex setup, Limited querying capabilities compared to SQL databases	Time Series	1.1k	5.0k
MapDB // 2011	In-memory, Embedded storage	Limited functionality, No built-in networking	Embedded, In-Memory, Key-Value	770	4.9k
Apache Ignite // 2014	High-performance in-memory computing, Distributed systems support, SQL compatibility, Scalability	Complex setup and configuration, Requires JVM environment	Distributed, In-Memory, Machine Learning	5.8m	4.8k
M3DB // 2016	Highly scalable, Optimized for time series data, High availability	Steep learning curve, Complex setup	Time Series, Distributed	1	4.8k
OrientDB // 2010	Multi-model capabilities, Highly flexible schema support, Open-source	Complex setup and maintenance, Performance can degrade with complex queries	Graph, Document	2.7k	4.8k
Marqo // 2022	Focus on vector search, Real-time machine learning capabilities, Works well with structured and unstructured data	Limited features compared to more mature systems, Primarily focuses on search use cases	Search Engine, Vector DBMS, Machine Learning	46.6k	4.6k
ObjectBox // 2017	High performance for embedded databases, Efficient object-oriented storage	Limited cross-platform support, Smaller community compared to other DBMS	Embedded, Object-Oriented	1.6k	4.4k
H2 // 2005	Lightweight, Embedded support, Fast	Limited scalability, In-memory by default	Relational, Embedded	61.6k	4.2k
CrateDB // 2014	Scalable distributed SQL database, Handles time-series data efficiently, Native full-text search capabilities	Limited support for complex joins, Relatively new with possible growing pains	Distributed, Relational, Time Series	304	4.1k
LedisDB // 2014	In-memory, Key-Value store, Simplified interface	Limited to key-value use cases, Lacks advanced features	Key-Value, In-Memory	0.0	4.1k
BigchainDB // 2017	High throughput, Decentralized and immutable, Focus on blockchain technology	Limited querying capabilities, Not suitable for high-frequency updates	Blockchain, Distributed	1.2k	4.0k
YDB // 2021	High scalability, Fault-tolerant	Relatively new, Limited community support	Distributed, Relational	6.7k	4.0k
TypeDB // 2016	Semantic modeling, Strong inference capabilities	Complex set-up, Limited third-party integration	Graph, Document	1.1k	3.8k
Apache Kylin // 2015	OLAP on Hadoop, Sub-second latency for big data	Complex setup and configuration, Depends on Hadoop ecosystem	Analytical, Distributed, Columnar	5.8m	3.7k
RavenDB // 2009	Easy to use with full ACID transaction support, Optimized for storing large volumes of documents	Limited ecosystem compared to more established databases, Smaller community	Document, Distributed	13.1k	3.6k
Tarantool // 2010	In-memory performance, Flexible data model	Limited ecosystem, Complex configuration	In-Memory, Distributed	4.3k	3.4k
FlockDB // 2010	High throughput for relationship-based data, Optimized for social networking applications	Limited functionality for complex queries, Not actively maintained	Graph, Distributed	0.0	3.3k
TerminusDB // 2019	Graph database capabilities, Version control for data, RDF and JSON-LD support	Limited third-party integrations, Smaller community support	Graph, Document	786	2.8k
Project Voldemort // 2009	Scalability, Resilience to node failures	Limited support for complex queries, Not suitable for transactional data	Key-Value, Distributed	262	2.6k
LMDB // 2011	High performance, Memory mapped, ACID compliance	Limited scalability, In-memory constraints	Embedded, In-Memory, Key-Value	943	2.6k
XTDB // 2019	Temporal database capabilities, Flexible schema	Requires in-depth understanding for complex queries, Limited out-of-the-box analytics features	Document, Streaming	586	2.6k
Skytable // 2021	High performance, Scalable, Multi-model	Relatively new, Limited community	Key-Value, Distributed, In-Memory	1	2.4k
GridDB // 2014	Time series data handling, High scalability, IoT optimized	Limited ecosystem, Less community support	Time Series, In-Memory, Key-Value	6.0k	2.4k
GemFire // 2002	Low latency, Real-time data caching, Distributed in-memory data grid	Complex setup, Enterprise pricing	In-Memory, Distributed	3.3m	2.3k
Geode // 2016	In-memory speed, High availability, Strong consistency	Complex setup, High memory usage	In-Memory, Distributed	5.8m	2.3k
Graph Engine // 2016	High-performance graph processing, Scalable, Supports distributed computing	Limited adoption, Complex implementation	Graph, Distributed, In-Memory	723.2m	2.2k
Ehcache // 2003	Java-based, Easy integration, Robust Caching	Limited to Java applications, Not a full-fledged database	In-Memory, Distributed	6.0k	2.0k
TinkerGraph // 2012	Lightweight, Part of Apache TinkerPop framework, Graph traversal language support	Limited scalability, Not suited for large datasets	Graph	5.8m	2.0k
Apache Sedona // 2012	Geospatial data processing, Scalability	Complex configuration, Requires integration with Apache Spark	Geospatial, Distributed, Streaming	5.8m	2.0k
Apache Drill // 2015	Schema-free SQL, High performance for large datasets, Support for multiple data sources	Complex configurations, Limited community	Analytical, Distributed	5.8m	1.9k
YTsaurus // 2022	Scalability, Open-source	Complex setup, Requires Kubernetes expertise	Distributed, Streaming	1.4k	1.9k
Sphinx // 2001	Open-source, High-performance full-text search	Requires additional setup for some features, Less widely adopted than other search engines	Search Engine	21.6k	1.8k
MatrixOne // 2021	High performance, Scalability, Flexible architecture	Relatively new, may have fewer community resources	NewSQL, Distributed, Relational	33	1.8k
PostGIS // 2001	Robust geospatial data support, Integrates with PostgreSQL	Complexity in learning, Database size management	Geospatial, Relational	82.5k	1.8k
KairosDB // 2012	Highly scalable, Optimized for time-series data, Open source	Limited built-in analytics capabilities, Requires third-party tools for visualization	Time Series, Distributed	0.0	1.7k
Elassandra // 2018	Combines Elasticsearch and Cassandra, Real-time search and analytics	Complex architecture, Requires deep technical knowledge to manage	Wide Column, Search Engine, Distributed	0	1.7k
CnosDB // 2022	Time series focused, High throughput	New entrant in market, Limited community support	Time Series, Distributed	1.8k	1.7k
OpenMLDB // 2020	Specifically designed for ML applications, High performance	Niche use case, Relatively new and evolving	Analytical, Streaming	1.6k	1.6k
NEventStore // 2010	Event sourcing, CQRS support, Modular design	Steep learning curve, Limited to event sourcing use cases	Event Stores	0.0	1.6k
Vald // 2020	Vector similarity search, Scalability	Young project, Limited documentation	Distributed, Vector DBMS	0	1.5k
CovenantSQL // 2018	Blockchain based, Decentralized, Secure data storage, Supports SQL queries	Performance can be slower due to blockchain consensus, Limited ecosystem compared to traditional SQL databases	Blockchain, Distributed, SQL	84	1.5k
EJDB // 2020	Lightweight, Embedded, Cross-platform	Limited scalability, Single-threaded	Document, Embedded	9	1.4k
GeoMesa // 2013	Scalable geospatial processing, Integrates with big data tools, Handles spatial and spatiotemporal data	Complex setup, Limited support for certain geospatial queries	Geospatial, Distributed	580	1.4k
Kuzu // 2020	Graph processing, Optimized for complex queries, Flexible data model	Still emerging, Limited documentation	Graph	2.1k	1.4k
Comdb2 // 2018	High performance, Distributed transactions, Designed for cloud environments	Limited documentation, Smaller community	Relational	0.0	1.4k
Elasticsearch // 2010	Full-text search, Scalability, Real-time analytics	Complex configuration, Resource-intensive	Search Engine, Distributed	1.1m	1.3k
Firebird // 2000	Lightweight, Cross-platform, Strong SQL support	Smaller community, Fewer modern features	Relational, Embedded	48.6k	1.3k
Apache Solr // 2004	Full-text search capabilities, Highly scalable and distributed, Flexible and extensible	Complex configuration, Challenging to optimize for large datasets	Search Engine	5.8m	1.2k
Infinispan // 2009	Highly scalable, Rich data structures, Supports in-memory caching	Complex configuration, Requires Java environment, Can be resource-intensive	In-Memory, Distributed	2.4k	1.2k
Percona Server for MySQL // 2006	Enhanced performance, Increased security, Enterprise-grade features	Requires tuning for optimal performance, Community support	Relational	146.9k	1.2k
Apache Impala // 2013	High-performance SQL queries, Designed for big data, Integration with Hadoop ecosystem	Limited support for updates and deletes, Requires more manual configuration	Analytical, Distributed, In-Memory	5.8m	1.2k
Apache Jena // 2011	RDF and OWL support, Semantic web technologies integration	Limited to semantic web applications, Complex RDF and SPARQL setup	RDF Stores, Graph	5.8m	1.1k
openGemini // unknown	Open Source, Community Driven	Limited Features, Scalability Concerns	Time Series, Distributed	0	1.1k
Aerospike // 2009	High performance, Low latency, Strong consistency	Complex setup, Limited secondary index capabilities	Key-Value, Distributed	16.1k	1.1k
Apache Accumulo // 2011	Strong consistency and scalability, Cell-level security, Highly configurable	Complex setup and configuration, Steep learning curve	Distributed, Wide Column	5.8m	1.1k
Apache Phoenix // 2014	SQL interface over HBase, Integrates with Hadoop ecosystem, High performance	HBase dependency, Limited SQL support	Relational, Wide Column	5.8m	1.0k
Realm // 2011	Mobile-focused, Object-oriented, Offline-first	Not a full SQL replacement, Limited support for complex queries	Document, Embedded	1.6k	1.0k
RRDtool // 1999	Efficient time series data storage, Compact data footprint, Good for monitoring data	Limited functionality compared to modern databases, Complex configuration for beginners	Time Series	11.3k	1.0k
Tigris // 2022	Scalable, Multi-tenancy, Easy to use APIs	Relatively new, Limited community support	Document, Relational	7.1k	921
Blazegraph // 2006	Scalable graph database, Supports SPARQL queries, High-performance for RDF data	Limited support for complex analytics, Can be challenging to scale beyond certain limits	Graph, RDF Stores	347	898
Virtuoso // 1998	Supports multiple data models, Good RDF and SPARQL support	Complex setup, Performance variation	Relational, RDF Stores	12.3k	867
Heroic // 2015	Time series data management, Scalability, Open-source	Niche use case focus, Limited query language support	Time Series, Distributed	0	848
Xapian // 2000	Fast full-text search, Open source, Highly customizable	Complex setup for beginners, Limited built-in scalability	Search Engine	1.3k	805
Apache HAWQ // 2013	SQL-on-Hadoop, High-performance, Seamless scalability	Complex setup, Resource-heavy	Analytical, Relational	5.8m	696
BaseX // 2005	Efficient XML data processing, Native XML database, XQuery processing	Niche use case, Less mature compared to SQL databases	Native XML DBMS, Document	2.0k	693
ZODB // 1998	Object Persistence, Transparent Object Storage	Not Suitable for Large Datasets, Limited Tooling	Object-Oriented, Distributed	106	682
NCache // 2003	Scalability, Distributed caching, Focused on .NET applications	Primarily focused on Windows and .NET environments	In-Memory, Distributed	7.9k	650
Giraph // 2012	Highly scalable for graph processing, Integration with Hadoop ecosystems	Requires expertise in graph algorithms, Relatively complex setup	Graph, Distributed	5.8m	617
WhiteDB // 2011	In-memory database, Competitive read and write speed	Limited persistence, No cloud offering	In-Memory, Relational	43	608
ArcadeDB // 2021	Multi-model, Scalable, Easy integration	Still maturing, Limited third-party support	Graph, Document	261	499
Elliptics // 2009	Distributed, Fault-tolerant, Highly customizable	Complex setup, Steep learning curve	Distributed, Key-Value	0	497
BrightstarDB // 2011	RDF data model, Supports SPARQL	Niche market, Limited adoption	RDF Stores, Graph	0	458
TomP2P // 2010	Peer-to-peer architecture, Scalability, Decentralized	Complex setup, Potential latency issues	Distributed, Key-Value	0	442
eXist-db // 2000	Native XML database, Supports XQuery and XPath, Schema-less approach	Limited scalability compared to relational DBs, Complexity in managing large XML datasets	Document, Native XML DBMS	1.6k	429
Oracle Coherence // 2001	Strong in-memory capabilities, High scalability and reliability	Complex configuration, Higher cost of ownership	In-Memory, Distributed	15.8m	427
Warp 10 // 2014	High scalability for time series, Rich analytics features	Complex data model, Steep learning curve	Time Series, Distributed	47	388
MonetDB // 1993	High-performance analytic queries, Columnar storage, Excellent for data warehousing	Complex scalability, Smaller community support compared to major RDBMS	Columnar, Analytical	2.7k	383
RDF4J // 2004	Semantic Data Processing, Strong Community Support	Steep Learning Curve, Performance Bottlenecks	RDF Stores	369	365
Apache Derby // 2004	Lightweight, Pure Java implementation, Embeddable	Limited scalability, Not suitable for very large databases	Relational, Embedded	5.8m	346
Fluree // 2018	Blockchain-backed storage and query, ACID transactions, Immutable and versioned data	Relatively new with a smaller user base, Performance can be impacted by complex queries	Blockchain, Graph, RDF Stores	2.2k	340
Apache Jackrabbit // 2004	Highly flexible, Scales well for content repositories, Java API support	Complex configuration, Limited performance in high-load scenarios	Content Stores	5.8m	335
Sequoiadb // 2011	High performance, Supports hybrid data models, Flexibility in deployment	Limited global presence	Document, Search Engine	7.7k	326
4store // 2009	Optimized for RDF data, Scalable distributed database	Limited query language support, Outdated documentation	RDF Stores	0	291
Kyoto Tycoon // 2011	Lightweight, Fast key-value storage	Limited query capabilities, Not natively distributed	In-Memory, Key-Value	1.7k	276
Hibari // 2010	Strong consistency, Highly reliable	Limited adoption, Complex Erlang-based setup	Key-Value, Distributed	0.0	273
TigerGraph // 2012	Optimized for deep-link analytics, Highly scalable graph processing	Steep learning curve, Relatively limited community support	Graph, Distributed	9.6k	269
Cubrid // 2008	Open-source, High availability, Optimized for web services	Limited support outside of C, C++, and Java	Relational	11.1k	264
Hawkular Metrics // 2015	Time series data management, Integration with monitoring tools, Scalability	Part of larger ecosystem, Specific to monitoring use cases	Time Series, Distributed	33	234
ModeShape // 2009	Supports JCR API, Repository capabilities	Complex setup, Steep learning curve	Hierarchical, Document, Content Stores	164.1k	217
HyperGraphDB // 2006	Represent complex relationships, Highly flexible model	Niche use cases, Lacks mainstream adoption	Graph, RDF Stores	1	215
Percona Server for MongoDB // 2015	Enterprise features, Security enhancements, Open source, Improved scalability	Dependent on MongoDB updates, Niche community support	Document, Distributed	146.9k	212
ReductStore // 2021	Simplified time series data storage, Efficient data recall, Compact data formats	Limited to time-series data, Recently developed	Time Series, Event Stores	146	177
Tkrzw // 2019	Lightweight, Versatile, Highly efficient	Lack of advanced features, Smaller community base	Embedded, Key-Value	1.7k	177
EdgelessDB // 2020	Confidential computing, End-to-end encryption, High security	Higher overhead due to encryption, Potentially complex setup for non-security experts	Distributed, Relational	2.0k	170
Redland // 2000	Highly extensible, Supports various RDF formats	Limited scalability, Complex setup	RDF Stores	3	157
Scalaris // 2008	Scalable key-value store, Reliability, High availability	Limited to key-value operations, Smaller community support	Distributed, Key-Value	0	155
OrigoDB // unknown	In-Memory Performance, Simple API	Limited Scale for Large Deployments, Relativity New	In-Memory, Document	0	137
Tajo // 2013	High performance, Extensible architecture, Supports SQL standards	Limited community support, Not widely adopted	Analytical, Relational, Distributed	5.8m	135
YottaDB // 2017	Robust transaction support, Open-source	Limited to specific healthcare applications, Less community support	Embedded, Hierarchical	63	76
NosDB // 2015	Scalability, NoSQL capabilities	Limited ecosystem, Learning curve for new users	Document, Distributed	7.9k	44
Apache HugeGraph // 2018	Efficient graph processing capabilities, Supports large-scale graph traversal, Open-source and highly extensible	Limited documentation, Smaller community compared to other graph databases	Graph, RDF Stores	0.0	9
DataFS // 2017	Versioned data storage, Metadata management, Data integrity	Not optimized for high-speed transactions, Limited scalability compared to distributed databases	Distributed, Document	0	6
Oracle 1979	Robust performance, Comprehensive features, Strong security	High cost, Complexity	Relational, Document, In-Memory	15.8m	0
Snowflake 2014	Scalable data warehousing, Separation of compute and storage, Fully managed service	Higher cost for small data tasks, Vendor lock-in	Analytical	1.1m	0
IBM Db2 1983	ACID compliance, Multi-platform support, High availability features	Legacy technology, Steep learning curve	Relational	13.4m	0
Microsoft Access 1992	Easy to use, Integration with Microsoft Office, Rapid application development	Limited scalability, Windows-only platform	Relational	723.2m	0
Splunk 2003	Powerful search and analysis, Real-time monitoring, Scalability	Cost, Complexity for new users	Search Engine, Streaming	771.7k	0
Databricks 2013	Unified analytics, Collaboration, Scalable data processing	Complexity, High cost for larger deployments	Analytical, Machine Learning	1.3m	0
Microsoft Azure SQL Database 2010	Scalability, Integration with Microsoft ecosystem, Security features, High availability	Cost for high performance, Requires specific skill set for optimization	Relational, Distributed	723.2m	0
Amazon DynamoDB 2012	Fully managed, High scalability, Event-driven architecture, Strong and eventual consistency options	Complex pricing model, Query limitations compared to SQL	Document, Key-Value, Distributed	762.1m	0
Google BigQuery 2011	Serverless architecture, Fast, SQL-like queries, Integration with Google ecosystem, Scalability	Cost for large queries, Limited control over infrastructure	Columnar, Distributed, Analytical	6.4b	0
FileMaker 1985	Ease of use, Rapid application development, Cross-platform compatibility	Limited scalability, Less flexibility for complex queries	Relational	279.7k	0
SAP HANA 2010	Real-time analytics, In-memory data processing, Supports mixed workloads	High cost, Complexity in setup and configuration	Relational, In-Memory, Columnar	7.0m	0
Teradata 1979	Scalable data warehousing, High concurrency, Advanced analytics capabilities	High cost, Complex data modeling	Relational	132.9k	0
SAP Adaptive Server 1988	Strong transactional support, High performance for OLTP workloads, Comprehensive security features	High total cost of ownership, Legacy platform that may not integrate well with modern tools	Relational	7.0m	0
Microsoft Azure Cosmos DB 2017	Global distribution, Multi-model capabilities, High availability	Can be costly, Complex pricing model	Document, Graph, Key-Value, Columnar, Distributed	723.2m	0
Couchbase 2011	High performance, Flexibility with data models, Scalability, Strong mobile support with Couchbase Lite	Complex setup for beginners, Lacks built-in analytics support	Document, Key-Value, Distributed	62.6k	0
Informix 1981	High performance with OLTP workloads, Excellent support for time series data, Low administrative overhead	Smaller community support compared to others, Perceived as outdated by some developers	Relational, Time Series, Document	13.4m	0
Amazon Redshift 2012	High-performance data warehousing, Scalable architecture, Tight integration with AWS services	Cost can accumulate with large data sets, Latencies in certain analytical workloads	Columnar, Relational	762.1m	0
Firebase Realtime Database 2011	Real-time synchronization, Offline capabilities, Integrates well with other Firebase products	No native support for complex queries, Not suited for large datasets	Document, Distributed	6.4b	0
Vertica 2005	High performance for analytics, Columnar storage, Scalability	Complex licensing, Limited support for transactional workloads	Analytical, Columnar, Distributed	19.5k	0
dBASE 1980	Ease of use, Low resource requirements	Limited scalability, Older technology	Relational	4.0k	0
Amazon Aurora 2014	High availability, Scalable, Fully managed by AWS	Tied to AWS ecosystem, Potentially higher costs	Relational, Distributed	762.1m	0
Kdb 2000	High performance, Time-series data, Real-time analytics	Steep learning curve, Costly for large deployments	Time Series, Analytical	35.8k	0
Greenplum // 2005	Massively parallel processing, Scalable for big data, Open source	Complex setup, Heavy resource use	Analytical, Relational, Distributed	27.9k	0
Netezza 1999	High performance analytics, Simplicity of deployment	Cost, Vendor lock-in	Analytical, Relational	13.4m	0
Google Cloud Firestore 2019	Seamless integration with Firebase, Realtime updates, Scalability	Cost can escalate, Limited querying capabilities	Document, Distributed	6.4b	0
Algolia 2012	Fast search capabilities, Highly scalable, Easy integration	Limited to search use-cases, Pricing can be expensive for large-scale usage	Search Engine	429.1k	0
Oracle Essbase 1992	Strong OLAP capabilities, Robust data analytics	Complex implementation, Oracle licensing costs	Multivalue DBMS, In-Memory	15.8m	0
Microsoft Azure AI Search 2017	Integrated AI capabilities, Part of Azure ecosystem	Dependency on Azure environment, Cost considerations for large data sets	Search Engine	723.2m	0
Datastax Enterprise 2010	Highly scalable, Advanced security features, Multi-model	Higher cost, Complex deployment	Wide Column, Distributed	564.8k	0
Graphite // 2008	Efficient time series data storage, Easy integration with various tools	Lacks advanced analytics features, Limited support for large data volumes	Time Series	927	0
MarkLogic 2001	Enterprise-grade features, Strong data integration capabilities, Advanced security and data governance	High cost, Learning curve for developers	Document, Native XML DBMS	9.3k	0
SingleStore 2011	Fast analytics, Scalable, Operational and analytical workloads	High complexity for certain queries, Learning curve for database administrators	Relational, Columnar	43.0k	0
Google Cloud Datastore 2013	Scalable NoSQL database, Fully managed, Integration with other Google Cloud services	Vendor lock-in, Complexity in querying complex relationships	Document, Distributed	6.4b	0
Riak KV 2009	Highly available, Scalable	Complexity in setup, Not suitable for complex queries	Key-Value, Distributed	2.2k	0
Interbase 1984	Small footprint, High performance, Strong security features	Limited modern community support, Lacks some advanced features of larger databases	Relational, Embedded	357.4k	0
OpenEdge 1984	Scalable architecture, Comprehensive development tools, Multi-platform support	Proprietary system, Complex licensing model	Relational	363.4k	0
Ingres 1980	Enterprise-grade features, Robust security, High performance	Less community support compared to mainstream databases, Older technology	Relational	82.6k	0
Oracle NoSQL 2011	High performance, Auto-sharding, Integration with Oracle ecosystem	Complex management, Oracle licensing costs	Distributed, Document, Key-Value	15.8m	0
SAP SQL Anywhere 1992	Embedded database capabilities, Reliable sync technology, Low resource usage	Limited scalability compared to major databases, Slightly dated interface	Relational, Embedded	7.0m	0
Microsoft Azure Table Storage 2010	High availability, Massive scalability, Cost-effective	Limited query capabilities, No complex queries or joins	Distributed, Key-Value	723.2m	0
Pinecone 2020	Specialized for vector search, High accuracy and performance, Easy integration	Niche use cases, Limited general database capabilities	Vector DBMS, Machine Learning	128.3k	0
HyperSQL // 2001	Lightweight, In-memory capability, Standards compliance with SQL	Limited scalability for very large datasets, Limited feature set compared to larger RDBMS	Relational, In-Memory	2.6k	0
Microsoft Azure Data Explorer 2018	Real-time data analysis, Highly scalable, Integrated with Azure ecosystem	Complex setup for new users, Azure dependency	Analytical, Distributed, Streaming	723.2m	0
Google Cloud Bigtable 2015	Scalable NoSQL database, Real-time analytics, Managed service by Google Cloud	Limited to Google Cloud Platform, Complexity in schema design	Distributed, Wide Column	6.4b	0
GraphDB 2008	Semantic graph database, Supports RDF and linked data, Strong querying with SPARQL	Limited to graph-focused use cases, Complex RDF queries	RDF Stores, Graph	39.5k	0
InterSystems IRIS 2018	High performance, Integrated support for multiple data models, Strong interoperability	Complex licensing, Steeper learning curve for new users	Multivalue DBMS, Distributed	120.4k	0
Memgraph // 2018	Focus on real-time graph processing, High performance with in-memory technology	Limited adoption compared to major graph databases, Smaller community support	Graph, In-Memory	15.9k	0
Google Cloud Spanner 2012	Globally distributed with strong consistency, High availability and low latency	High cost, Limited control over infrastructure	Distributed, Relational, NewSQL	6.4b	0
DolphinDB 2015	High performance for time-series data, Powerful analytical capabilities	Niche use case focuses primarily on time-series, Less widespread adoption	Time Series, Distributed	619	0
Adabas 1969	High transaction throughput, Stability and maturity	Legacy system, Less flexible compared to modern databases	Hierarchical	306.8k	0
SAP IQ 1994	High performance for analytical queries, Compression capabilities, Strong support for business intelligence tools	Proprietary software, Complex setup and maintenance	Columnar, Relational	7.0m	0
UniData 1987	Rapid application development, Scalable business applications, Python language support, Security enhancements	Niche use cases, Difficult to integrate with non-Multivalue systems	Multivalue DBMS	101.4k	0
Coveo 2005	Advanced search capabilities, AI-powered relevance	Proprietary platform, Complex pricing model	Search Engine	64.7k	0
Amazon Neptune 2017	High scalability, Supports multiple graph models, Fully managed by AWS	AWS dependency, Complex pricing structure, Requires specific skill set	Graph, RDF Stores	762.1m	0
4D 1984	Comprehensive development platform, Integrated with web and mobile solutions, Easy to use for non-developers	Limited to small to medium applications, Less flexible compared to open-source solutions, Can be costly for large scale	Relational	38.0k	0
MaxDB // 1987	Enterprise-grade stability, SAP integration, Handles large volumes of data	Lesser known outside SAP ecosystem, Not as flexible as newer databases, Limited community support	Relational	7.0m	0
Oracle Berkeley DB // 1991	High performance, Supports multiple programming languages, Embeddable	Limited scalability, Complex to manage for large datasets	Embedded, Key-Value	15.8m	0
Amazon DocumentDB 2019	Fully managed service, MongoDB compatibility, High availability	Vendor lock-in, Costly at scale	Document, Distributed	762.1m	0
CloudKit 2014	Seamless integration with Apple ecosystems, Strong focus on privacy and security, Automatic synchronization	Limited to Apple platforms, Less flexible for non-Apple environments	Document, Key-Value	420.8m	0
Stardog 2012	Highly scalable, Semantic reasoning capabilities	Complex pricing model, Requires specialized knowledge for setup	RDF Stores, Graph	18.0k	0
Amazon CloudSearch 2011	Managed search-as-a-service, Scale automatically, Easy to integrate with other AWS services	Limited customization compared to open-source alternatives, Costs can increase with large data sets	Search Engine	762.1m	0
Amazon SimpleDB 2007	NoSQL data store, Fully managed, Flexible and scalable	Not suitable for large performance-intensive workloads, Limited querying capabilities	Distributed, Key-Value	762.1m	0
EDB Postgres 2004	Enterprise-grade support and features, Open-source based, High compatibility with Oracle	Can be complex to manage without expertise, More costly than standard open-source PostgreSQL for enterprise features	Relational	639.8k	0
EXASOL 2000	High-speed analytics, Columnar storage, In-memory processing	Expensive licensing, Limited data type support	Relational, Analytical	9.0k	0
Empress 1979	Embedded database capabilities, Support for various platforms, Low footprint	Limited awareness in the market, Older technology base	Embedded	0	0
IMS 1968	High performance for OLTP, Reliable and mature	Legacy system, Steep learning curve	Hierarchical	13.4m	0
SpatiaLite // 2008	Supports spatial data types, Lightweight and fully self-contained	Not suitable for large-scale enterprise applications, Limited concurrency	Relational, Geospatial	2.8k	0
Datomic // 2012	Immutable data, Temporal queries	License cost, Limited in-memory footprint	Distributed, Document	1.6k	0
Firebolt 2019	High performance, Low-latency query execution, Scalability	Relatively new, less community support, Focused primarily on analytical use cases	Analytical, Columnar	38.2k	0
Fauna 2015	Strong consistency, ACID transactions, Global distribution	Proprietary query language, Can be expensive at scale	NewSQL	12.4k	0
GridGain 2013	Scalability, High performance, In-memory processing	Complex learning curve, Requires extensive memory resources	Distributed, In-Memory	3.1k	0
Tibero 2003	Oracle compatibility, High performance	Limited integration with non-Tibero ecosystems, Smaller market presence compared to leading RDBMS	Relational	18.6k	0
jBASE 1991	Multivalue data model, Efficient for complex querying	Outdated technology stack, Limited developer community	Multivalue DBMS	5.5k	0
VoltDB // 2010	High-speed transactions, In-memory processing	Memory constraints, Complex setup for high availability	Distributed, In-Memory, NewSQL	36	0
HEAVY.AI 2013	High performance, Real-time analytics, GPU acceleration	Niche market focus, Limited ecosystem compared to larger players	Analytical, Distributed, In-Memory	27.6k	0
Actian NoSQL Database 1980s	Embedability, High performance, Low overhead	Less known in the modern tech stack, Limited community	Document, Key-Value	82.6k	0
mSQL 1994	Lightweight, Embedded systems	Obsolete compared to current databases, Limited support and features	Relational, Embedded	235	0
ObjectStore 1988	High performance in object-oriented data storage, Supports complex data models	Complex setup, High license cost	Object-Oriented, Distributed	0	0
Db4o // 2000	Lightweight, Object-Oriented database	Limited support for distributed systems, Slower performance with complex queries	Embedded, Object-Oriented	0	0
TimesTen 1998	In-memory, Real-time data processing	Requires more RAM, Not suitable for large datasets	In-Memory, Relational	15.8m	0
IBM Db2 Warehouse 2016	High scalability, Advanced analytics with embedded machine learning	Cost, Complex configuration	Relational, Analytical	13.4m	0
D3 Unknown	N/A	N/A	Distributed, Document	101.4k	0
Amazon Timestream 2020	Optimized for time series data, Serverless and scalable, Built-in time series analytics	Limited to AWS ecosystem, Relatively new with less community support	Time Series	762.1m	0
Mnesia 1993	Integrates with Erlang/OTP, Supports complex data structures, Highly available	Limited to Erlang ecosystem, Not suitable for very large datasets	Distributed, Relational, In-Memory	74.1k	0
GBase 2004	Strong support for Chinese language data, Good for OLAP and OLTP	Limited international adoption, Documentation primarily in Chinese	Relational, Analytical	15.9k	0
Datameer 2009	Supports data integration from various sources, User-friendly interface, Strong data preparation and analytics features	Primarily tailored for Hadoop ecosystems, Limited query flexibility compared to SQL	Analytical	19.7k	0
openGauss // 2020	High Performance, Extensibility, Security Features	Community Still Growing, Limited Third-Party Integrations	Distributed, Relational	38.2k	0
HFSQL 2005	Embedded Database Capabilities, Ease of Use	Limited to PC SOFT Environment, Less Market Presence Compared to Mainstream DBMS	Embedded, Relational	51.9k	0
SQLBase 1984	Low Maintenance, Integrated Features	Aging Technology, Limited Adoption	Relational, Embedded	96	0
DataEase 1981	Rapid Application Development, User-Friendly Interface	Outdated Technologies, Limited Community Support	Relational, Document	1	0
Oracle Rdb 1984	High Stability, Excellent Performance on Digital Equipment	Niche Market, High Cost of Operation	Relational	15.8m	0
Amazon Keyspaces 2020	Fully managed, Highly scalable, Compatible with Apache Cassandra	Vendor lock-in, Higher cost at scale	Wide Column	762.1m	0
PlanetScale // 2018	Serverless, MySQL compatible, Highly scalable	Schema changes can be complex, Relatively new to broader market	NewSQL, Distributed	109.1k	0
Rockset 2018	Real-time analytics, Built-in connectors, SQL-powered	Can be costly, Limited to analytical workloads	Analytical, Distributed, Document	7.6k	0
GT.M 1977	High concurrency, Proven technology, Large user base in healthcare	Limited support for modern APIs, Steep learning curve	Hierarchical	0	0
eXtremeDB 2001	Fast in-memory processing, Suitable for embedded systems, Supports real-time applications	May not be ideal for large disk-based storage requirements	In-Memory, Embedded	2.0k	0
GigaSpaces 2000	In-memory speed, Scalability, Real-time processing	Cost, Requires proper tuning for optimization	In-Memory, Distributed	7.2k	0
NonStop SQL 1987	High availability, Fault tolerance, Scalability	Legacy system complexities, High cost	Relational, Distributed	2.9m	0
TDSQL for MySQL 2020	High availability, Strong consistency, Scalability	Vendor lock-in, Limited third-party support	Relational, Distributed	13.1m	0
Alibaba Cloud PolarDB 2017	Cost-effective, Compatible with MySQL, High performance	Complex pricing model	Relational, Distributed	1.3m	0
Alibaba Cloud AnalyticDB for MySQL 2017	Advanced analytical capabilities, Designed for big data, High concurrency	Cost can increase with scale	Analytical, Relational	1.3m	0
Alibaba Cloud MaxCompute 2016	Massive data processing capabilities, Integrated with Alibaba Cloud ecosystem, Cost-effective	Steep learning curve for newcomers	Analytical, Distributed	1.3m	0
Infobright 2005	High compression rates, Fast query performance, Optimized for read-heavy workloads	Limited write performance, Legacy software with reduced community support	Analytical, Columnar	0	0
IDMS 1973	Proven reliability, Strong transaction management for hierarchical data	Complex to manage and maintain, Legacy system with limited modern features	Hierarchical	2.5m	0
Altibase 1999	Hybrid architecture supporting in-memory and disk storage, Real-time transaction processing	Limited global market penetration, Requires specialized knowledge for optimal deployment	Relational, In-Memory	833	0
NuoDB 2010	Supports distributed SQL databases, Elastic scale-out with ACID compliance	Not suitable for write-heavy workloads, Complex configuration for optimal performance	Distributed, NewSQL, Relational	1	0
HPE Ezmeral Data Fabric 2009	Scalability, High Performance, Integrated Data Store	Complexity, Cost	Distributed, Key-Value, Document, Time Series	2.9m	0
Sedna // 2019	Efficient XML Data Processing, Open Source	Limited Adoption, Niche Use Case	Embedded, Machine Learning	0	0
Yellowbrick 2014	High performance, Scalable architecture, Supports complex queries	Limited managed cloud options, Proprietary solution	Analytical, Relational, Distributed	6.0k	0
Alibaba Cloud AnalyticDB for PostgreSQL 2018	High-performance data analysis, PostgreSQL compatibility, Seamless integration with Alibaba Cloud services	Vendor lock-in, Limited to Alibaba Cloud environment	Analytical, Relational, Distributed	1.3m	0
Actian Vector 2009	High-performance analytics, Columnar storage, In-memory processing capabilities	Complex licensing, Steep learning curve	Columnar, Analytical	82.6k	0
SciDB 2011	Array-based data storage, Suitable for scientific data, Strong data integrity features	Niche market focus, Limited adoption	Analytical, Distributed	514	0
Datacom/DB 1970s	Proven reliability, Strong ACID compliance	Legacy system, Limited modern features	Relational, Hierarchical	2.5m	0
DBISAM 1998	Embedded database, Small footprint, Easy integration	Limited scalability, Not open-source	Relational, Embedded	494	0
Objectivity/DB 1980s	High performance, Scalable, Handles complex interrelationships	Steep learning curve, Limited community support	Object-Oriented, Graph	382	0
SQream DB 2010	Handles large-scale data, Accelerates query performance	Resource-intensive, Complex tuning required	Analytical, Columnar, Relational	9.8k	0
solidDB 1992	High-speed in-memory processing, ACID compliance, Embedded database options	Proprietary technology, Limited community support	In-Memory, Relational	13.4m	0
1010data 2000	High-volume data analysis, Cloud-native platform, Integrated analytics	Complex pricing models, Steep learning curve	Analytical, Columnar	3.1k	0
FrontBase 2000	Cross-platform support, High reliability, Full SQL implementation	Lower popularity, Limited recent updates	Relational	24	0
ObjectDB 2003	High-performance for Java applications, Object-oriented, Easy to use API	Limited query language support, Not suitable for non-Java environments	Object-Oriented	3.7k	0
Northgate Reality 1980	High reliability, Strong support for business applications	Older technology stack, May not integrate easily with modern systems	Hierarchical, Relational	631	0
R:BASE 1981	Established user base, Stable for legacy systems	Outdated technology, Limited community support	Relational	0	0
HarperDB // 2017	Schema flexibility, High performance for mixed workloads, Easy deployment	Relatively new in the market, Limited enterprise adoption	Distributed, Document	2.9k	0
NexusDB 2003	High-performance, Embedded database, SQL support	Lack of widespread adoption, Limited cloud support	Embedded, Relational	3.9k	0
Splice Machine 2014	HTAP capabilities, Machine Learning	Complex setup, Limited community support	Analytical, Distributed, Relational	381	0
WebSphere eXtreme Scale 2006	In-memory data grid, High scalability, Transactional support	Complex setup, Vendor lock-in	Distributed, In-Memory, Key-Value	13.4m	0
GemStone/S 1986	Object-oriented database, Transaction consistency, Scalable architecture	Complex learning curve, Limited community	Object-Oriented, In-Memory	84	0
Kingbase 2007	High compatibility with Oracle, Robust security features, Strong transaction processing	Limited global awareness, Smaller community support	Relational	87.4k	0
Kyligence Enterprise 2016	Fast OLAP queries, Easy integration with big data ecosystems	Complex setup, Dependency on Hadoop ecosystem	Analytical, In-Memory	8.6k	0
VistaDB 2004	Embedded database solution, Easy integration with .NET applications	Limited scalability, Windows platform dependency	Relational, Embedded	0	0
Raima Database Manager 1982	High performance for embedded systems, Real-time data processing	Niche use case focus, Smaller developer community	Relational, Embedded	899	0
atoti 2020	High performance for OLAP analyses, Integrated with Python, Interactive data visualization	Relatively new in the market, Limited community support	Analytical	1.7k	0
Kinetica 2016	GPU-accelerated, Real-time streaming data processing, Geospatial capabilities	Higher cost, Requires specific hardware for optimal performance	In-Memory, Distributed, Geospatial	4.4k	0
Perst 2005	Embedded and lightweight, Java and C# support, Small footprint	Limited scalability, Not suitable for large applications	Object-Oriented, Embedded	2.0k	0
Alibaba Cloud Log Service 2015	Scalable log processing, Real-time analytics, Easy integration with other Alibaba Cloud services	Region-specific services, Vendor lock-in	Analytical, Streaming	1.3m	0
Postgres-XL // 2014	Scalability, PostgreSQL compatibility, High availability	Complex setup, Limited community support compared to PostgreSQL	Distributed, Relational	133	0
Rasdaman // 1998	Geospatial data strength, Massive array data support	Niche application focus, Limited general-purpose database features	Geospatial	49	0
ScaleArc 2009	Database traffic management, Load balancing	Not a database itself but a proxy, Complex deployment	Relational, NewSQL	0	0
OpenInsight 1992	MultiValue flexibility, Backward compatibility	Legacy system, Limited modern support	Multivalue DBMS	187	0
ITTIA 2007	Embedded use, Power efficiency, Targeted at IoT	Limited to embedded systems	Embedded, In-Memory	0	0
	N/A	N/A		0.0	0
Strabon // 2012	Geospatial capabilities, Semantic web support	Can be complex to set up, Niche use cases	RDF Stores, Geospatial	1.1m	0
Starcounter 2010	High performance, In-memory database technology, Integration capabilities	Limited market presence, Niche use cases	In-Memory, Relational	0	0
PieCloudDB 2019	Cloud-native architecture, Scalability	New to market, Limited documentation	NewSQL, Distributed	0	0
LeanXcale 2017	Scalable transactions, Hybrid transactional/analytical processing	Limited adoption, Complex setup	NewSQL, Distributed, Relational	0	0
InfiniteGraph 2010	Scalability, High-performance graph queries	Complex setup, Limited community support	Graph, Distributed	33	0
Cloudflare Workers KV 2018	Global distribution, Low latency	Size limitations, Eventual consistency	Key-Value, Distributed	29.3m	0
SearchBlox 2003	Full-text search, Easy setup	Feature limitations, Scaling challenges	Search Engine, Document	10.1k	0
MyScale 2022	Scalable, High performance for analytical queries	Limited documentation, Complex configuration	Time Series, Distributed	55.6k	0
OpenQM 2004	MultiValue DBMS capabilities, Cost-effective	Niche market, Smaller community	Multivalue DBMS	0	0
Brytlyt 2013	GPU acceleration, Real-time analytics	High hardware cost, Complex integration	Analytical, Relational	234	0
RDFox 2015	Highly performant RDF store, Supports complex reasoning	Complex to implement, Limited to RDF	RDF Stores, Graph	2.3k	0
Alibaba Cloud TSDB 2017	Scalable time series data storage, High performance for big data analysis, Seamless integration with Alibaba Cloud ecosystem	Limited adoption outside of Alibaba Cloud ecosystem, Less community support compared to open-source alternatives	Time Series	1.3m	0
Fujitsu Enterprise Postgres 2015	Enterprise-grade security features, Enhanced performance and scalability, Advanced analytics and data visualization	Higher cost for enterprise features, Limited community-driven developments	Relational	1.8m	0
AnzoGraph DB 2020	Massively parallel processing, High-performance graph analytics	Complexity in setup, Limited community support	Graph, RDF Stores, Analytical	5.4k	0
FeatureBase // 2019	High-performance real-time analytics, Efficient data ingestion	Limited to a specific use case, Steep learning curve for new users	Columnar, Distributed	22.3k	0
PipelineDB 2014	Designed for continuous aggregation, Integrates with PostgreSQL	Limited to streaming workloads, Small community size	Relational, Streaming, Time Series	0	0
Mimer SQL 1970	High concurrency, Embedded support	Limited community, Less popular compared to other relational databases	Relational	1.2k	0
Actian FastObjects 1980	Optimized for object-oriented applications, Flexible schema design	Niche use case, Less adoption outside specific industries	Embedded, Object-Oriented	82.6k	0
Alibaba Cloud Table Store 2017	Scalable, High availability, Flexible data model	Limited language support, Complex setup for beginners	Key-Value, Wide Column, Time Series	1.3m	0
Axibase 2014	Time Series optimized, Powerful analytics tools	Niche use cases, Steep learning curve	Time Series, Geospatial	88	0
RedStore Unknown	Lightweight RDF store	Limited capabilities, Sparse documentation	RDF Stores, Graph	32.6k	0
Faircom DB 1979	Hybrid data model, Proven reliability	Costly licensing, Complex deployment	Document, Relational, Embedded	4.8k	0
Tibco ComputeDB 2019	High-speed data processing, Seamless integration with Apache Spark, In-memory processing	Requires technical expertise to manage	Distributed, In-Memory, Relational	155.6k	0
IBM Db2 Event Store 2018	Real-time event storage and analytics, Integration with IBM Cloud services	Limited third-party integrations, IBM Cloud dependency	Event Stores, In-Memory, Relational	13.4m	0
AgensGraph 2017	Multi-model database supporting SQL and graphs, Combines relational and graph processing	Solid understanding of SQL and graph databases required, Smaller community support	Graph, Relational	0	0
TransLattice 2010	High availability, Geographically distributed architecture	Limited market penetration, Complex setup	Distributed, Relational	0	0
Actian PSQL 1981	Strong data security, High performance	Proprietary system, Cost	Relational, Embedded	82.6k	0
Speedb 2021	High-speed operations, NoSQL capabilities	Relatively new, Limited ecosystem	Embedded, Key-Value	58	0
Valentina Server 1998	Cross-platform, Integration with Valentina Studio	Niche market, Limited public documentation	Relational, Document	9.4k	0
Riak TS 2015	Scalable, Designed for time series data, High availability	Complex setup, Limited query language support	Time Series, Key-Value	2.2k	0
EsgynDB 2015	SQL support on Hadoop, Scalable, Robust querying	Complex to manage, Requires Hadoop expertise	Relational, Distributed	88	0
XtremeData 2007	MPP (Massively Parallel Processing) capabilities, High-performance analytics	Proprietary technology, Niche use cases	Analytical, Distributed, Relational	293	0
ElevateDB 2008	Small footprint, Embedded database capabilities	Limited scalability, Less popular than major DBMS options	Embedded, Relational	494	0
Jade 1978	Integrated development environment, Object-oriented database	Older technology, Limited to Jade platform	Object-Oriented, Document	806	0
BigObject 2014	Real-time analytics, In-memory processing	Proprietary technology, Limited third-party integrations	Analytical, Columnar	0	0
Quasardb 2009	High-speed data ingestion, Time series analysis	Complex setup, Cost	Distributed, In-Memory, Time Series	0	0
RaptorDB 2012	Simplicity, Key-value store	Limited feature set, Not suitable for large-scale applications	Document, Key-Value	0	0
Ultipa 2018	Real-time graph processing, Advanced graph algorithms	Specialized use case, Complexity	Graph	426	0
GreptimeDB // 2020	High performance, Scalable time-series storage	Relatively new ecosystem	Distributed, Time Series	1.9k	0
Tokyo Tyrant 2008	Fast key-value storage, Simple API	Limited feature set, No managed cloud offering	Key-Value	1.1k	0
Sparksee 2006	High performance for graph data, Good data compression	Limited community support	Graph	0	0
FalkorDB 2021	Flexible architecture, Supports federation	Limited maturity, Limited documentation	Document, Distributed	1.7k	0
GraphBase 2015	Optimized for complex queries, Highly scalable	Complex setup	Graph	0	0
CubicWeb // 2008	Semantic web functionalities, Flexible data modeling, Strong community support	Complex learning curve, Limited commercial support	RDF Stores	0	0
Mulgara 2005	High-performance RDF store, Scalable triple store	Limited active development, Smaller community	RDF Stores	0	0
AntDB 2010	High concurrency, Scalability	Limited international adoption, Complexity in setup	Distributed, Relational	0	0
Bangdb 2013	High performance, Supports AI and machine learning	Limited community support, Less known compared to mainstream databases	Key-Value, Document	4.1k	0
Exorbyte 2000	Robust search capabilities, Fault-tolerant	High initial cost, Complex setup	Search Engine, Content Stores	33	0
ScaleOut StateServer 2005	Distributed in-memory data grid, Real-time analytics	Limited integrations, Licensing costs	In-Memory, Distributed	1.9k	0
SiteWhere // 2015	Open-source IoT platform, Flexible and scalable	Complex setup for new users, Requires integration expertise	Distributed	20	0
chDB 2023	High performance, Scalability, Efficiency in analytical queries	Limited user community, Relatively new in the market	Columnar, Analytical	0.0	0
OushuDB 2021	Highly scalable, Optimized for OLAP workloads	Limited ecosystem, Niche focus	Analytical, Columnar	0	0
Transbase 1987	Proven reliability, ACID compliant	Proprietary, Lacks modern features	Relational	115	0
WakandaDB 2012	Unified platform, JavaScript support	Limited community support, Niche use cases	Document, In-Memory	0.0	0
JethroData 2012	High-performance analytics, Good for large data sets	Complex setup, Steep learning curve	Analytical, Columnar, Distributed	270	0
STSdb 2010	In-memory performance, Lightweight	Limited compared to full-featured DBMS, No cloud offering	In-Memory, Key-Value	97.6k	0
JaguarDB 2014	Performance, Supports ACID transactions	Limited adoption, Niche market	In-Memory, Relational, Distributed	0	0
Transwarp KunDB 2013	High performance, Scalability, Integration with big data ecosystems	Less known in Western markets, Limited community resources	Analytical, Distributed, Relational	0	0
Transwarp ArgoDB 2016	Real-time data processing, Compatibility with multiple data formats	Complex setup, Smaller user community	Distributed, Relational	0	0
Faircom EDGE 2018	Efficiency in edge computing, Data synchronization	Newer product with less maturity, Limited ecosystem	Embedded, Relational, Document	4.8k	0
SmallSQL 2004	Lightweight, Java integration	Limited scalability, Fewer features compared to major SQL databases	Relational	0	0
SparkleDB Unknown	N/A	N/A	Graph, RDF Stores	0	0
gStore Unknown	N/A	N/A	Graph, RDF Stores	251	0
Resin Cache Unknown	N/A	N/A	In-Memory, Key-Value	2.5k	0
Acebase Unknown	N/A	N/A	Document, NoSQL	0.0	0
SWC-DB Unknown	N/A	N/A	Wide Column, Distributed	0	0
Dydra 2010	RDF data storage, SPARQL query execution, Managed cloud service	Specialized use, Limited broader use outside RDF	Graph, RDF Stores	154	0
Eloquera 2011	Object-oriented structure, Fast prototyping, Flexible data storage	Less common compared to relational DBs, Specialized niche	Object-Oriented, Embedded	0	0
Indica N/A	N/A	N/A	Document, Search Engine	156	0
Siaqodb // 2009	Embedded, Cross-platform, Lightweight	Limited query capabilities, Smaller community support	Embedded, Object-Oriented	0	0
Sadas Engine 2007	High performance, Compression, Scalability	Proprietary, License cost	Analytical, Relational	0	0
SwayDB // 2018	Highly scalable, Simplified design, Immutable structure	Limited ecosystem, Niche user base	Key-Value, Embedded	0	0
ActorDB 2015	Distributed, Scalability, Fault tolerance	Limited community support, Complex setup	Distributed, Relational	0	0
BergDB Unknown	N/A	N/A	In-Memory, Distributed	0	0
Cachelot.io // 2016	High performance, In-memory key-value storage	Limited feature set, Primarily for caching	In-Memory, Key-Value	144	0
CortexDB 2020	Graph-based, Schema-less	Emerging technology, Limited documentation	Document, Distributed	0	0
DaggerDB 2020	Optimized for hybrid workloads, High concurrency, Scalable	Limited adoption and community support, May require significant tuning for specific use cases	Graph, Distributed	0	0
Edge Intelligence 2021	Optimized for edge computing, Low latency processing, Real-time analytics	Limited support for complex query languages, May require specialized hardware	Distributed, Machine Learning	89	0
Galaxybase 2020	Supports large-scale graph data, High performance, Flexible schema	Limited community support, Less mature compared to established graph databases	Graph, Analytical	0	0
H2GIS 2015	Integration with Spatial features, Open-source	Limited support for non-spatial queries, Small community	Geospatial, Relational	416	0
Helium 2019	Highly efficient, Immutable storage	Limited query options, Niche use cases	In-Memory, Document, Distributed	88	0
HGraphDB 2017	Flexible graph model, Compatibility with Hadoop	Complex setup, Limited documentation	Graph, Distributed	0.0	0
HyperLevelDB 2011	High write throughput, Efficient storage management	Not suitable for complex queries, Limited built-in analytics	Key-Value, Embedded	0.0	0
iBoxDB 2013	Embedded design, Ease of integration	Limited scalability, Small community support	Document, Embedded	163	0
InfinityDB 2000	High performance, Scalable architecture	Proprietary system, Limited documentation	Embedded, Hierarchical	0	0
JasDB // 2012	Flexible data model, JSON support	Limited commercial support, Basic querying capabilities	Document, Embedded	0	0
K-DB Unknown	High-speed columnar processing, Strong for financial applications	Limited general-purpose usage, Specialized use case	Time Series, In-Memory	124.8k	0
Linter 1995	Strong SQL compatibility, ACID compliance	Niche market focus, Legacy system	Relational	1.6k	0
Newts unknown	Time Series Management, Scalability, Efficiency	Limited Documentation, Lack of Major Community Support	Time Series, Distributed	0.0	0
NSDb // unknown	Distributed Architecture, Real-Time Processing	Emerging Ecosystem, Integration Challenges	Time Series, Distributed	28	0
OpenTenBase // unknown	Flexibility, Customizability	Lack of Enterprise Support, Niche Market	Time Series, In-Memory	8	0
Rizhiyi 2020	Scalability, High Performance	Limited Community Support	Time Series, Distributed	10.5k	0
searchxml 2018	Efficient XML Processing	Niche Use Case	Native XML DBMS	0	0
SiriDB 2016	Optimized for Time Series Data, High Write Performance	Limited Ecosystem Integration	Time Series, Distributed	0	0
SpaceTime 2019	Geospatial Data Handling, Real-Time Processing	Complex Setup	Time Series, Geospatial	899	0
SvectorDB 2021	Handling Vector Data, Scalable Architecture	Emerging Technology	Vector DBMS, Machine Learning	3	0
TerarkDB 2016	High-performance, Low-latency, Efficient storage optimization	Complexity in configuration, Limited community support	Key-Value, Columnar	0.0	0
Transwarp Hippo 2013	High concurrency, Real-time processing, Robust storage	Proprietary system, Higher cost	Distributed, In-Memory, SQL	0	0
Transwarp StellarDB 2013	High availability, Strong consistency, Scalable architecture	Proprietary technology, Limited community support	Relational, Distributed	0	0
Upscaledb 2009	High performance key-value store, ACID transactions, Designed for embedded use	Limited community support, Lacks variety in query languages	Embedded, Key-Value	0	0
VelocityDB 2011	Highly optimized for .NET applications, Object-oriented data storage	Limited to .NET environments, Niche use cases	Object-Oriented, In-Memory, Distributed	130	0
Microsoft Azure Synapse Analytics 2010	Integrates with all Azure services, High scalability, Robust analytics	High complexity, Cost, Requires Azure ecosystem	Analytical, Distributed, Relational	723.2m	0
Model 204 1965	High performance, Scalable, Reliable	Legacy system, Limited modern integration	Hierarchical, Multivalue DBMS	101.4k	0
AllegroGraph 2004	Advanced graph analytics, Proven scalability and reliability, Supports multiple languages like SPARQL and Prolog	Complex setup and maintenance, Can be expensive for large-scale deployments	Graph, RDF Stores	20.6k	0
Machbase Neo 2020	High-performance for time series data, In-memory processing	Limited to time series use cases, Less known in the market	Time Series, In-Memory	694	0
Blueflood 2012	Scalable, Optimized for time series metrics	Limited documentation, Niche use case specific	Time Series, Distributed	0	0
SenseiDB 2010	Real-time analytics, Faceted search support	Complex integration, Niche market	Distributed, Search Engine	0.0	0

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What is a Database?

A database is an organized collection of data that is stored, managed, and accessed electronically. Databases are essential for storing information in a structured way, making retrieval, updates, and data analysis efficient. Whether it’s your personal contact list or a large-scale enterprise system managing billions of records, databases are foundational to modern computing.

At its core, a database ensures data integrity, scalability, and accessibility. Databases come in many forms, tailored to specific use cases, from relational systems powering financial transactions to cutting-edge vector databases for machine learning models.

Understanding Database Types

Databases are designed with specific functionalities and data models in mind. Here’s a comprehensive list of database types and their primary purposes:

Analytical: Optimized for querying and reporting large datasets, commonly used in business intelligence.
Blockchain: Immutable, decentralized ledgers for secure and transparent transactions.
Columnar: Stores data in columns rather than rows, ideal for analytical workloads.
Content Stores: Designed for managing unstructured data like documents, images, and videos.
Distributed: Spans multiple servers, ensuring reliability and scalability across regions.
Document: Stores semi-structured data like JSON or XML, commonly used in web apps.
Embedded: Lightweight databases embedded into software applications (e.g., mobile apps).
Event Stores: Focused on capturing and storing events, often used in event-driven architectures.
Geospatial: Handles spatial data for maps, geographic applications, and geolocation services.
Graph: Specializes in relationships, storing data as nodes and edges (e.g., social networks).
Hierarchical: Stores data in a tree-like structure, often used in legacy systems.
In-Memory: Stores data in memory for ultra-fast processing.
Key-Value: Simple storage model using key-value pairs, ideal for caching.
Machine Learning: Tailored for storing and retrieving AI/ML model features.
Multivalue DBMS: Allows fields to contain multiple values, simplifying complex data relationships.
Native XML DBMS: Specialized for managing and querying XML data.
NewSQL: Provides scalability of NoSQL with relational data consistency.
Object-Oriented: Stores data as objects, commonly used in object-oriented programming.
RDF Stores: Designed for semantic web and linked data applications.
Relational: The most common type, based on tables and structured query language (SQL).
Search Engine: Optimized for text-based data retrieval and indexing.
Streaming: Processes real-time data streams for low-latency applications.
Time Series: Optimized for time-stamped data (e.g., IoT, financial applications).
Vector DBMS: Stores vector embeddings, ideal for AI/ML-driven search and recommendations.
Wide Column: Schema-less, column-family storage (e.g., Apache Cassandra).

Key Features to Look for in a Database

When evaluating a database, these key features can help you make the right choice:

Scalability: Ability to grow with your data, horizontally (adding more servers) or vertically (adding resources to a server).
Performance: Fast query execution and minimal latency.
Security: Encryption, access controls, and compliance with regulations like GDPR.
Data Model: Whether relational, NoSQL, graph, or hybrid, choose a model suited to your needs.
Backup and Recovery: Reliable mechanisms for preventing data loss.
Ease of Use: Developer-friendly APIs and tools for integration.
Community and Support: A robust user base and active development are crucial for long-term use.

Choosing the Right Database for Your Project

Selecting a database depends on your specific project requirements. Here's a decision-making guide:

Define Your Use Case: Is it transactional (e.g., e-commerce) or analytical (e.g., reporting)?
Data Type: Choose a database that fits your data structure (e.g., relational for tabular data, graph for relationships).
Scale Needs: Small-scale apps can use lightweight solutions like SQLite, while enterprise-grade systems may need distributed databases like Cassandra.
Budget: Consider open-source options like PostgreSQL or MySQL if cost is a concern.
Flexibility: Choose NoSQL for dynamic schema requirements or frequent changes.
Real-Time Needs: If you need live data, opt for streaming databases like Apache Kafka.

Common Mistakes When Choosing a Database

Avoid these pitfalls to ensure a smooth database implementation:

Ignoring Scalability Needs: Starting with a database that can't grow with your business.
Over-Engineering: Using a complex database for simple applications.
Neglecting Backups: Lack of proper backup strategies leads to data loss.
Underestimating Costs: Forgetting to account for hosting, maintenance, and licensing fees.
Poor Performance Testing: Not benchmarking the database with realistic workloads.

Future Trends in Database Technologies

As technology evolves, databases are becoming more innovative and specialized. Here are some trends shaping the future:

AI and Machine Learning Integration: Databases like Pinecone are optimized for vector embeddings, powering AI-driven applications.
Serverless Databases: Elastic, cost-effective solutions that scale automatically without server management.
Blockchain Databases: Decentralized databases ensuring trust and transparency.
Multi-Model Databases: Combining multiple database types to meet diverse needs.
Graph Databases: Growing in popularity for analyzing relationships in social networks, fraud detection, and more.
Edge Databases: Databases optimized for edge computing, reducing latency in IoT and mobile applications.
Quantum Databases: A future-forward concept leveraging quantum computing for unparalleled data processing.

After exploring the comprehensive database comparison table and diving into the key concepts, types, and trends discussed in this guide, you’re now well-equipped to choose the right database for your specific needs. This combination of data and insights helps you avoid common pitfalls, make informed decisions, and stay ahead of emerging trends. Databases are the backbone of modern technology—mastering them is an invaluable skill in today’s data-driven world.

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