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1import{a,f as i}from"./SDtLGXl7.js";import"./Bom5CQ_t.js";import{n as s}from"./BhzG5ji-.js";const o={title:"How to Choose the Right Database: Complete Guide (2025)",description:"Learn how to choose the right database with a clear breakdown of CAP, ACID vs BASE, and SQL vs NoSQL vs NewSQL vs time-series, including factors and use cases.",date:"2025-07-01",categories:["database","comparison"],published:!0,author:"Jonas Fröller",readingTime:"17 min",tags:["database","CAP theorem","ACID","BASE","NoSQL","SQL","architecture","scalability"]},{title:d,description:p,date:h,categories:u,published:g,author:m,readingTime:f,tags:b}=o;var r=i('<h1 id="how-to-choose-the-right-database"><a tabindex="-1" href="#how-to-choose-the-right-database">How to choose the right database</a></h1> <p>Selecting the right database for your application affects performance, scalability, reliability, and user experience. There are many database options, each with its own strengths and trade-offs. This guide covers the CAP theorem, the difference between ACID and BASE models, the major database architectures, and their use cases, risks, and opportunities.</p> <h2 id="understanding-database-fundamentals"><a tabindex="-1" href="#understanding-database-fundamentals">Understanding database fundamentals</a></h2> <p>A database is a structured collection of data for efficient storage, retrieval, and management. Databases are grouped by their data models, which determine how data is organized and accessed. The primary types include:</p> <ul><li><strong>Relational Databases</strong>: Use tables with rows and columns, accessed via SQL.</li> <li><strong>NoSQL Databases</strong>: Include document, key-value, column-family, and graph databases, designed for flexibility and scalability.</li> <li><strong>NewSQL Databases</strong>: Combine relational consistency with NoSQL scalability.</li> <li><strong>Time-Series Databases</strong>: Optimized for time-stamped data.</li></ul> <p>The main concepts are data models (structured, semi-structured, unstructured), schemas (fixed or flexible), and the trade-offs between consistency, availability, and performance.</p> <h2 id="the-cap-theorem"><a tabindex="-1" href="#the-cap-theorem">The CAP theorem</a></h2> <p>The CAP theorem, proposed by Eric Brewer in 2000, states that a distributed data store can only provide two out of three guarantees at any given time:</p> <ul><li><strong>Consistency (C)</strong>: Every read receives the most recent write or an error, so all nodes reflect the same data.</li> <li><strong>Availability (A)</strong>: Every request receives a non-error response, even if the data might not be the most recent.</li> <li><strong>Partition Tolerance (P)</strong>: The system continues to operate despite network failures or message delays between nodes.</li></ul> <p>In practice, distributed systems must be partition-tolerant because network issues are inevitable, and that forces a choice between consistency and availability during partitions. This leads to two main categories:</p> <ul><li><strong>CP Systems</strong>: Prioritize consistency and partition tolerance, potentially sacrificing availability. Examples include MongoDB, Redis, and Apache HBase. During a partition, these systems may reject requests to ensure data accuracy.</li> <li><strong>AP Systems</strong>: Prioritize availability and partition tolerance, potentially sacrificing consistency. Examples include Apache Cassandra, CouchDB, and ScyllaDB. These systems remain available but may return stale data during partitions.</li> <li><strong>CA Systems</strong>: Prioritize consistency and availability but are not partition-tolerant, so they are rare in distributed systems. Single-node databases or tightly coupled systems may fall into this category.</li></ul> <p>Many databases offer configurable consistency levels, so developers can balance these trade-offs based on application needs. For instance, Cassandra’s tunable consistency lets users adjust read and write consistency levels to prioritize either consistency or availability.</p> <p>The PACELC theorem extends CAP by considering trade-offs during normal operations (no partitions), where latency and consistency compete. It matters most for low-latency workloads like real-time analytics.</p> <h2 id="acid-vs-base"><a tabindex="-1" href="#acid-vs-base">ACID vs. BASE</a></h2> <p>Databases also differ in their transactional models, primarily categorized as ACID or BASE.</p> <h3 id="acid-properties"><a tabindex="-1" href="#acid-properties">ACID properties</a></h3> <p>ACID (Atomicity, Consistency, Isolation, Durability) ensures reliable transactions:</p> <ul><li><strong>Atomicity</strong>: Transactions are all-or-nothing.</li> <li><strong>Consistency</strong>: Transactions move the database from one valid state to another.</li> <li><strong>Isolation</strong>: Concurrent transactions do not interfere.</li> <li><strong>Durability</strong>: Committed transactions are permanently saved.</li></ul> <p>ACID is typical in relational databases like MySQL and PostgreSQL, which suits applications that need strong data integrity, such as financial systems or inventory management.</p> <h3 id="base-model"><a tabindex="-1" href="#base-model">BASE model</a></h3> <p>BASE (Basically Available, Soft state, Eventual consistency) prioritizes availability and scalability:</p> <ul><li><strong>Basically Available</strong>: The system is available most of the time.</li> <li><strong>Soft State</strong>: Data may change over time without immediate consistency.</li> <li><strong>Eventual Consistency</strong>: The system will become consistent over time.</li></ul> <p>BASE is common in NoSQL databases like Cassandra and DynamoDB, suitable for applications where occasional data staleness is acceptable, such as social media feeds or real-time analytics.</p> <h3 id="choosing-between-acid-and-base"><a tabindex="-1" href="#choosing-between-acid-and-base">Choosing between ACID and BASE</a></h3> <p>Choose ACID for applications where data accuracy matters most, such as banking or e-commerce. Opt for BASE when scalability and availability matter more and eventual consistency is sufficient, like in content delivery or IoT applications.</p> <h2 id="major-database-architectures"><a tabindex="-1" href="#major-database-architectures">Major database architectures</a></h2> <p>The major database architectures differ in their characteristics, use cases, risks, and opportunities.</p> <h3 id="1-relational-databases"><a tabindex="-1" href="#1-relational-databases">1. Relational Databases</a></h3> <p><strong>Characteristics</strong>:</p> <ul><li>Store data in tables with rows and columns and a fixed schema.</li> <li>Support SQL for complex queries, joins, and transactions.</li> <li>Provide ACID guarantees for strong consistency.</li> <li>Often CP in distributed setups, where consistency matters more than availability.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Financial systems (e.g., banking transactions).</li> <li>E-commerce platforms (e.g., order processing).</li> <li>Enterprise resource planning (ERP) systems.</li></ul> <p><strong>Examples</strong>:</p> <ul><li>MySQL (<a href="https://www.mysql.com/" rel="nofollow">MySQL</a>)</li> <li>PostgreSQL (<a href="https://www.postgresql.org/" rel="nofollow">PostgreSQL</a>)</li> <li>Oracle Database (<a href="https://www.oracle.com/database/" rel="nofollow">Oracle Database</a>)</li> <li>

1Microsoft SQL Server (<a href="https://www.microsoft.com/en-us/sql-server" rel="nofollow">SQL Server</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Mature technology with extensive tooling and community support.</li> <li>Strong data integrity and consistency for critical applications.</li> <li>Strong support for complex queries and transactions.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Scalability can be challenging, especially for write-heavy workloads.</li> <li>Fixed schemas may limit flexibility for rapidly changing data structures.</li> <li>May struggle with large volumes of unstructured data.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Performance bottlenecks in high-traffic scenarios.</li> <li>High operational costs for scaling vertically or managing replication.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Well-suited for applications requiring structured data and complex analytics.</li> <li>Integration with existing enterprise systems and SQL-based tools.</li></ul> <h3 id="2-nosql-databases"><a tabindex="-1" href="#2-nosql-databases">2. NoSQL Databases</a></h3> <p>NoSQL databases are designed for flexibility and scalability, and they handle unstructured or semi-structured data. They include several subtypes.</p> <h4 id="a-document-stores"><a tabindex="-1" href="#a-document-stores">a. Document Stores</a></h4> <p><strong>Characteristics</strong>:</p> <ul><li>Store data in JSON-like documents with flexible schemas.</li> <li>Often CP, with consistency prioritized (e.g., MongoDB).</li> <li>Support rich queries on nested data structures.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Content management systems (e.g., blogs, CMS).</li> <li>Real-time analytics (e.g., user behavior tracking).</li> <li>IoT applications (e.g., device data storage).</li></ul> <p><strong>Examples</strong>:</p> <ul><li>MongoDB (<a href="https://www.mongodb.com/" rel="nofollow">MongoDB</a>)</li> <li>CouchDB (<a href="https://couchdb.apache.org/" rel="nofollow">CouchDB</a>)</li> <li>RavenDB (<a href="https://ravendb.net/" rel="nofollow">RavenDB</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Flexible schema accommodates evolving data structures.</li> <li>Horizontal scalability for large datasets.</li> <li>Good for semi-structured data.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>May not provide strong consistency by default.</li> <li>Limited support for complex joins compared to relational databases.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Potential data inconsistencies in distributed setups.</li> <li>Learning curve for developers unfamiliar with document models.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Good for rapid development and prototyping.</li> <li>Supports diverse data types and dynamic schemas.</li></ul> <h4 id="b-key-value-stores"><a tabindex="-1" href="#b-key-value-stores">b. Key-Value Stores</a></h4> <p><strong>Characteristics</strong>:</p> <ul><li>Simple data model with key-value pairs.</li> <li>Extremely fast for basic read/write operations.</li> <li>Often CP (e.g., Redis) or AP (e.g., DynamoDB) depending on configuration.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Caching (e.g., web application performance).</li> <li>Session management (e.g., user sessions).</li> <li>Real-time bidding or ad serving.</li></ul> <p><strong>Examples</strong>:</p> <ul><li>Redis (<a href="https://redis.io/" rel="nofollow">Redis</a>)</li> <li>Amazon DynamoDB (<a href="https://aws.amazon.com/dynamodb/" rel="nofollow">DynamoDB</a>)</li> <li>Riak (<a href="https://riak.com/" rel="nofollow">Riak</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>High performance for simple operations.</li> <li>Scalable for high-throughput workloads.</li> <li>Low latency for read/write operations.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Limited query capabilities beyond key-based access.</li> <li>Not suitable for complex data relationships.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Data loss in in-memory setups (e.g., Redis without persistence).</li> <li>Limited functionality for analytical queries.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Performance gains in caching and real-time applications.</li> <li>Easy to integrate with other systems as a lightweight store.</li></ul> <h4 id="c-column-family-stores"><a tabindex="-1" href="#c-column-family-stores">c. Column-Family Stores</a></h4> <p><strong>Characteristics</strong>:</p> <ul><li>Store data in columns rather than rows, optimized for large-scale data.</li> <li>Typically AP, with tunable consistency (e.g., Cassandra).</li> <li>Designed for high write and read throughput.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Time-series data (e.g., sensor data).</li> <li>Recommendation systems.</li> <li>Big data analytics.</li></ul> <p><strong>Examples</strong>:</p> <ul><li>Apache Cassandra (<a href="https://cassandra.apache.org/" rel="nofollow">Cassandra</a>)</li> <li>Apache HBase (<a href="https://hbase.apache.org/" rel="nofollow">HBase</a>)</li> <li>ScyllaDB (<a href="https://www.scylladb.com/" rel="nofollow">ScyllaDB</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Highly scalable for big data workloads.</li> <li>Efficient for write-heavy applications.</li> <li>Flexible schema for evolving datasets.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Steeper learning curve due to complex architecture.</li> <li>Eventual consistency may not suit all use cases.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Potential data staleness in AP configurations.</li> <li>Higher operational complexity for cluster management.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Works well in distributed, high-traffic environments.</li> <li>Supports large-scale analytics and real-time processing.</li></ul> <h4 id="d-graph-databases"><a tabindex="-1" href="#d-graph-databases">d. Graph Databases</a></h4> <p><strong>Characteristics</strong>:</p> <ul><li>Store data as nodes and relationships, optimized for connected data.</li> <li>Typically CP, which ensures consistency for graph traversals (e.g., Neo4j).</li> <li>Support complex relationship queries.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Social networks (e.g., friend recommendations).</li> <li>Fraud detection (e.g., identifying suspicious patterns).</li> <li>Recommendation engines.</li></ul> <p><strong>Examples</strong>:</p> <ul><li>Neo4j (<a href="https://neo4j.com/" rel="nofollow">Neo4j</a>)</li> <li>ArangoDB (<a href="https://www.arangodb.com/" rel="nofollow">ArangoDB</a>)</li> <li>Amazon Neptune (<a href="https://aws.amazon.com/neptune/" rel="nofollow">Neptune</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Efficient for querying complex relationships.</li> <li>Intuitive modeling of interconnected data.</li> <li>Strong consistency for critical operations.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Resource-intensive for large graphs.</li> <li>Limited horizontal scalability compared to other NoSQL databases.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Performance degradation with very large graphs.</li> <li>Higher computational requirements for traversals.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>

1Good for applications that need deep relationship analysis.</li> <li>Enables advanced analytics like network analysis and pathfinding.</li></ul> <h3 id="3-newsql-databases"><a tabindex="-1" href="#3-newsql-databases">3. NewSQL Databases</a></h3> <p><strong>Characteristics</strong>:</p> <ul><li>Combine relational data models with distributed architecture.</li> <li>Provide ACID transactions with horizontal scalability.</li> <li>Typically CP, with consistency prioritized (e.g., CockroachDB, Google Spanner).</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Global e-commerce platforms (e.g., distributed transactions).</li> <li>Gaming applications requiring scalability and consistency.</li> <li>Financial services with distributed data needs.</li></ul> <p><strong>Examples</strong>:</p> <ul><li>CockroachDB (<a href="https://www.cockroachlabs.com/" rel="nofollow">CockroachDB</a>)</li> <li>Google Spanner (<a href="https://cloud.google.com/spanner" rel="nofollow">Spanner</a>)</li> <li>VoltDB (<a href="https://www.voltdb.com/" rel="nofollow">VoltDB</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Scalability of NoSQL with ACID guarantees.</li> <li>Familiar SQL interface for developers.</li> <li>Suitable for globally distributed applications.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Relatively new, with potentially less community support.</li> <li>Higher operational complexity for distributed setups.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Limited ecosystem compared to traditional databases.</li> <li>Potential cost increases for large-scale deployments.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Bridges the gap between relational and NoSQL databases.</li> <li>Supports modern, cloud-native applications.</li></ul> <h3 id="4-time-series-databases"><a tabindex="-1" href="#4-time-series-databases">4. Time-Series Databases</a></h3> <p><strong>Characteristics</strong>:</p> <ul><li>Optimized for time-stamped data with efficient storage and querying.</li> <li>Often AP or with eventual consistency (e.g., InfluxDB).</li> <li>Include features like data retention and downsampling.</li></ul> <p><strong>Use Cases</strong>:</p> <ul><li>Monitoring systems (e.g., server metrics).</li> <li>IoT applications (e.g., sensor data).</li> <li>Financial data analysis (e.g., stock prices).</li></ul> <p><strong>Examples</strong>:</p> <ul><li>InfluxDB (<a href="https://www.influxdata.com/" rel="nofollow">InfluxDB</a>)</li> <li>TimescaleDB (<a href="https://www.timescale.com/" rel="nofollow">TimescaleDB</a>)</li> <li>Prometheus (<a href="https://prometheus.io/" rel="nofollow">Prometheus</a>)</li></ul> <p><strong>Pros</strong>:</p> <ul><li>Efficient storage and querying of time-series data.</li> <li>High ingestion rates for time-stamped data.</li> <li>Built-in features for data retention and aggregation.</li></ul> <p><strong>Cons</strong>:</p> <ul><li>Limited to time-series use cases.</li> <li>May not support complex relational queries.</li></ul> <p><strong>Risks</strong>:</p> <ul><li>Potential data inconsistencies in AP configurations.</li> <li>Specialized use case may limit general-purpose applicability.</li></ul> <p><strong>Opportunities</strong>:</p> <ul><li>Good for real-time monitoring and analytics.</li> <li>Supports IoT and DevOps use cases effectively.</li></ul> <h2 id="factors-to-consider-when-choosing-a-database"><a tabindex="-1" href="#factors-to-consider-when-choosing-a-database">Factors to consider when choosing a database</a></h2> <p>Selecting a database comes down to several factors:</p> <ol><li><strong>Data Model</strong>: Match the database to your data structure (structured, semi-structured, unstructured). Relational databases suit structured data, while NoSQL databases handle flexible schemas.</li> <li><strong>Scalability</strong>: Assess whether you need vertical (more powerful servers) or horizontal (more servers) scalability. NoSQL and NewSQL databases scale horizontally well.</li> <li><strong>Consistency Requirements</strong>: Determine if strong consistency (ACID) or eventual consistency (BASE) is needed. Financial applications require strong consistency, while social media can tolerate eventual consistency.</li> <li><strong>Availability</strong>: Evaluate the need for high availability, especially in distributed systems. AP databases like Cassandra prioritize availability.</li> <li><strong>Performance</strong>: Consider read and write performance requirements. Key-value stores like Redis offer low-latency operations, while relational databases support complex queries.</li> <li><strong>Query Language</strong>: SQL is standard for relational and NewSQL databases, while NoSQL databases use varied query languages, which may impact developer productivity.</li> <li><strong>Ecosystem and Community</strong>: Strong communities and ecosystems (e.g., MySQL, PostgreSQL) mean better support and more resources.</li> <li><strong>Cost</strong>: Factor in licensing, operational, and scaling costs. Open-source databases like PostgreSQL are cost-effective, while cloud-native solutions like DynamoDB may incur higher costs.</li> <li><strong>Security</strong>: Ensure the database supports encryption, access control, and compliance with regulations like GDPR.</li> <li><strong>Specific Use Case Requirements</strong>: Consider specialized needs, such as geospatial support (e.g., PostGIS in PostgreSQL), full-text search (e.g., Elasticsearch), or time-series optimization (e.g., InfluxDB).</li></ol> <h2 id="comparison-of-database-architectures"><a tabindex="-1" href="#comparison-of-database-architectures">Comparison of database architectures</a></h2> <table><thead><tr><th>

1<strong>Database Type</strong></th><th><strong>Data Model</strong></th><th><strong>CAP Classification</strong></th><th><strong>Use Cases</strong></th><th><strong>Pros</strong></th><th><strong>Cons</strong></th></tr></thead><tbody><tr><td>Relational</td><td>Tables (Structured)</td><td>CP (Distributed)</td><td>E-commerce, Banking</td><td>Strong consistency, SQL support</td><td>Limited scalability, rigid schema</td></tr><tr><td>Document Store</td><td>JSON-like Documents</td><td>CP (e.g., MongoDB)</td><td>Content Management, IoT</td><td>Flexible schema, scalable</td><td>Limited joins, potential inconsistency</td></tr><tr><td>Key-Value Store</td><td>Key-Value Pairs</td><td>CP/AP (e.g., Redis/AP)</td><td>Caching, Session Management</td><td>High performance, simple model</td><td>Limited query capabilities</td></tr><tr><td>Column-Family Store</td><td>Columns</td><td>AP (e.g., Cassandra)</td><td>Big Data, Time-Series</td><td>High scalability, write throughput</td><td>Steep learning curve, eventual consistency</td></tr><tr><td>Graph Database</td><td>Nodes & Relationships</td><td>CP (e.g., Neo4j)</td><td>Social Networks, Fraud Detection</td><td>Efficient relationship queries</td><td>Resource-intensive for large graphs</td></tr><tr><td>NewSQL</td><td>Relational (Distributed)</td><td>CP (e.g., CockroachDB)</td><td>Global Apps, Gaming</td><td>Scalable ACID transactions</td><td>Newer, less community support</td></tr><tr><td>Time-Series</td><td>Time-Stamped Data</td><td>AP/Eventual (e.g., InfluxDB)</td><td>Monitoring, IoT</td><td>Optimized for time-series, high ingestion</td><td>Limited to specific use cases</td></tr></tbody></table> <h2 id="conclusion"><a tabindex="-1" href="#conclusion">Conclusion</a></h2> <p>Picking a database means matching its capabilities to your application’s requirements. The CAP theorem lays out the trade-offs between consistency, availability, and partition tolerance, while ACID and BASE models describe the transactional side. The strengths and limitations of relational, NoSQL, NewSQL, and time-series databases differ, and factors like data model, scalability, and cost should drive the choice. Evaluate your specific use case and test potential databases to confirm they meet your needs.</p> <h2 id="sources"><a tabindex="-1" href="#sources">Sources</a></h2> <p><a href="https://en.wikipedia.org/wiki/CAP_theorem" rel="nofollow">CAP Theorem - Wikipedia</a><br/> <a href="https://www.scylladb.com/glossary/cap-theorem" rel="nofollow">What is CAP Theorem? Definition & FAQs | ScyllaDB</a><br/> <a href="https://www.bmc.com/blogs/cap-theorem" rel="nofollow">CAP Theorem Explained: Consistency, Availability & Partition Tolerance - BMC Software</a><br/> <a href="https://www.geeksforgeeks.org/dbms/the-cap-theorem-in-dbms" rel="nofollow">The CAP Theorem in DBMS - GeeksforGeeks</a><br/> <a href="https://www.ibm.com/think/topics/cap-theorem" rel="nofollow">What Is the CAP Theorem? | IBM</a><br/> <a href="https://avssridhar.medium.com/choosing-the-right-database-using-cap-theorem-43ced137cba5" rel="nofollow">Choosing the Right Database using CAP Theorem | Medium</a><br/> <a href="https://www.instaclustr.com/blog/cassandra-vs-mongodb" rel="nofollow">Cassandra vs MongoDB: Using CAP Theorem | Instaclustr</a><br/> <a href="https://www.quora.com/How-is-Neo4j-to-be-classified-in-relation-to-the-CAP-theorem" rel="nofollow">How is Neo4j to be classified in relation to the CAP theorem? | Quora</a><br/> <a href="https://www.influxdata.com/blog/influxdb-clustering-design-neither-strictly-cp-or-ap" rel="nofollow">InfluxDB Clustering Design - neither strictly CP or AP | InfluxData</a><br/> <a href="https://www.quora.com/What-is-Redis-in-the-context-of-the-CAP-Theorem" rel="nofollow">What is Redis in the context of the CAP Theorem? | Quora</a><br/> <a href="https://www.mysql.com" rel="nofollow">MySQL Official Website</a><br/> <a href="https://www.postgresql.org" rel="nofollow">PostgreSQL Official Website</a><br/> <a href="https://www.oracle.com/database" rel="nofollow">Oracle Database Official Website</a><br/>

1 <a href="https://www.microsoft.com/en-us/sql-server" rel="nofollow">Microsoft SQL Server Official Website</a><br/> <a href="https://www.mongodb.com" rel="nofollow">MongoDB Official Website</a><br/> <a href="https://couchdb.apache.org" rel="nofollow">CouchDB Official Website</a><br/> <a href="https://ravendb.net" rel="nofollow">RavenDB Official Website</a><br/> <a href="https://redis.io" rel="nofollow">Redis Official Website</a><br/> <a href="https://aws.amazon.com/dynamodb" rel="nofollow">Amazon DynamoDB Official Website</a><br/> <a href="https://riak.com" rel="nofollow">Riak Official Website</a><br/> <a href="https://cassandra.apache.org" rel="nofollow">Apache Cassandra Official Website</a><br/> <a href="https://hbase.apache.org" rel="nofollow">Apache HBase Official Website</a><br/> <a href="https://www.scylladb.com" rel="nofollow">ScyllaDB Official Website</a><br/> <a href="https://neo4j.com" rel="nofollow">Neo4j Official Website</a><br/> <a href="https://www.arangodb.com" rel="nofollow">ArangoDB Official Website</a><br/> <a href="https://aws.amazon.com/neptune" rel="nofollow">Amazon Neptune Official Website</a><br/> <a href="https://www.cockroachlabs.com" rel="nofollow">CockroachDB Official Website</a><br/> <a href="https://cloud.google.com/spanner" rel="nofollow">Google Spanner Official Website</a><br/> <a href="https://www.voltdb.com" rel="nofollow">VoltDB Official Website</a><br/> <a href="https://www.influxdata.com" rel="nofollow">InfluxDB Official Website</a><br/> <a href="https://www.timescale.com" rel="nofollow">TimescaleDB Official Website</a><br/> <a href="https://prometheus.io" rel="nofollow">Prometheus Official Website</a></p>',1);function y(e){var t=r();s(284),a(e,t)}export{y as default,o as metadata};