# Best Enterprise Document Databases *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Products classified in the overall Document Databases category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Document Databases to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Document Databases category. In addition to qualifying for inclusion in the Document Databases category, to qualify for inclusion in the Enterprise Business Document Databases category, a product must have at least 10 reviews left by a reviewer from an enterprise business. ## Category Overview **Total Products under this Category:** 68 ## Trust & Credibility Stats **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 3,300+ Authentic Reviews - 68+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Top-Rated Products (Ranked by G2 Score) ### 1. [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews) Build next generation search experiences for your customers and employees that support your organization’s technology objectives. Elasticsearch gives developers a flexible toolkit to build AI-powered search applications with an extensible platform that also provides out of the box capabilities Save development cycles and get upgraded search to market faster. Elasticsearch is the world’s most popular search engine, backed by a robust developer community. Elastic’s platform lets you ingest any data source, build modern search experiences that integrate with large language models and generative AI, and visualize analytics for data-driven decision-making and insights. Our consistent investments in machine learning help developers stay ahead of the curve with the fast, highly relevant search, at scale. -- Flexible platform and toolkit to deliver powerful search functionality regardless of development resources and technology objectives. Our open platform delivers consistent functionality for cloud, hybrid, or on-prem deployments with exceptional performance, reliability, and scalability. -- Built-in search analytics and visualization tools give teams access to search data and real-time dashboards for optimizing search results and operations. Non-tech teams can tune search experiences too–no development team needed. -- Next level search relevance using textual search, vector search, hybrid, and semantic search and machine learning model flexibility. Powerful capabilities like a vector database provide the foundation for creating, storing, and searching embeddings to capture the context of your unstructured data. Use machine-learning enabled inference at data ingestion, and bring your own model - open or proprietary - to deliver the best, industry-specific results. **Average Rating:** 4.5/5.0 **Total Reviews:** 286 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.6/10) - **Query Optimization:** 8.8/10 (Category avg: 8.0/10) - **Data Model:** 9.5/10 (Category avg: 8.5/10) - **Operating Systems:** 8.9/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Elastic](https://www.g2.com/sellers/elastic) - **Company Website:** https://www.elastic.co - **Year Founded:** 2012 - **HQ Location:** San Francisco, CA - **Twitter:** @elastic (64,541 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/814025/ (4,986 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 38% Mid-Market, 33% Enterprise #### Pros & Cons **Pros:** - Ease of Use (52 reviews) - Speed (36 reviews) - Fast Search (35 reviews) - Results (31 reviews) - Features (30 reviews) **Cons:** - Expensive (28 reviews) - Required Expertise (26 reviews) - Learning Difficulty (25 reviews) - Improvement Needed (24 reviews) - Difficult Learning (23 reviews) ### 2. [Amazon DynamoDB](https://www.g2.com/products/amazon-web-services-aws-amazon-dynamodb/reviews) Amazon DynamoDB is a pioneering NoSQL, fully managed, serverless database with limitless scalability and single-digit millisecond latency performance enabling customers to develop modern, microservice-based applications through a simple API. Customers enjoy the benefits of DynamoDB’s fully-managed service including broad compliance standards, security integration with AWS Identity and Access Management and numerous disaster recovery services. With DynamoDB Global Tables, customers have a 99.999% highly available, multi-Region, multi-active database supporting local reads and writes for globally distributed users. DynamoDB provides cost management features such as scale-to-zero, Time to Live (TTL) for aging data out, and multiple pricing models including a free tier. **Average Rating:** 4.4/5.0 **Total Reviews:** 495 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.6/10) - **Query Optimization:** 8.4/10 (Category avg: 8.0/10) - **Data Model:** 8.8/10 (Category avg: 8.5/10) - **Operating Systems:** 8.4/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,223,984 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 35% Enterprise, 33% Mid-Market #### Pros & Cons **Pros:** - Scalability (11 reviews) - Ease of Use (8 reviews) - Cost Efficiency (5 reviews) - Low Latency (5 reviews) - Managed Services (5 reviews) **Cons:** - Expensive (9 reviews) - Query Complexity (7 reviews) - Complexity (5 reviews) - Learning Curve (5 reviews) - Cost Issues (3 reviews) ### 3. [MongoDB](https://www.g2.com/products/mongodb/reviews) Built by developers, for developers, MongoDB's developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today's wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit www.mongodb.com. **Average Rating:** 4.5/5.0 **Total Reviews:** 518 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.6/10) - **Query Optimization:** 8.6/10 (Category avg: 8.0/10) - **Data Model:** 8.9/10 (Category avg: 8.5/10) - **Operating Systems:** 8.9/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [MongoDB](https://www.g2.com/sellers/mongodb) - **Year Founded:** 2007 - **HQ Location:** New York - **Twitter:** @MongoDB (502,962 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/783611/ (7,665 employees on LinkedIn®) - **Ownership:** NASDAQ: MDB **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 38% Small-Business, 37% Enterprise #### Pros & Cons **Pros:** - Ease of Use (12 reviews) - Flexibility (11 reviews) - Data Storage (7 reviews) - MongoDB Compatibility (7 reviews) - Scalability (7 reviews) **Cons:** - Difficult Learning (6 reviews) - Query Complexity (5 reviews) - Learning Curve (4 reviews) - Difficult Setup (3 reviews) - Expensive (3 reviews) ### 4. [Google Cloud Firestore](https://www.g2.com/products/google-cloud-firestore/reviews) Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale. **Average Rating:** 4.2/5.0 **Total Reviews:** 96 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.6/10) - **Query Optimization:** 7.9/10 (Category avg: 8.0/10) - **Data Model:** 8.7/10 (Category avg: 8.5/10) - **Operating Systems:** 8.9/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,885,216 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 53% Small-Business, 30% Mid-Market #### Pros & Cons **Pros:** - API Integration (1 reviews) - Features (1 reviews) - Flexibility (1 reviews) - Innovation (1 reviews) - Integrations (1 reviews) **Cons:** - Expensive (1 reviews) - Unclear Pricing (1 reviews) ### 5. [Couchbase](https://www.g2.com/products/couchbase/reviews) Couchbase’s operational data platform for AI is a scalable foundation for enterprise operational, analytical, mobile and AI workloads that replaces legacy infrastructure and data services. **Average Rating:** 4.3/5.0 **Total Reviews:** 142 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.6/10) - **Query Optimization:** 8.8/10 (Category avg: 8.0/10) - **Data Model:** 8.8/10 (Category avg: 8.5/10) - **Operating Systems:** 8.6/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Couchbase](https://www.g2.com/sellers/couchbase) - **Company Website:** https://www.couchbase.com/ - **Year Founded:** 2009 - **HQ Location:** San Jose, CA - **Twitter:** @couchbase (136,375 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1866670/ (888 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Senior Software Engineer, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 39% Small-Business, 34% Enterprise #### Pros & Cons **Pros:** - Ease of Use (5 reviews) - Ease of Setup (3 reviews) - Scalability (2 reviews) - Search Efficiency (2 reviews) - Speed (2 reviews) **Cons:** - Complexity (2 reviews) - Difficult Learning (2 reviews) - Complex Configuration (1 reviews) - Data Management (1 reviews) - High Memory Usage (1 reviews) ### 6. [MongoDB Atlas](https://www.g2.com/products/mongodb-atlas/reviews) MongoDB Atlas is a developer data platform that provides a tightly integrated collection of data and application infrastructure building blocks to enable enterprises to quickly deploy bespoke architectures to address any application need. Atlas supports transactional, full-text search, vector search, time series and stream processing application use cases across mobile, distributed, event-driven, and serverless architectures. **Average Rating:** 4.5/5.0 **Total Reviews:** 346 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.6/10) - **Query Optimization:** 8.6/10 (Category avg: 8.0/10) - **Data Model:** 9.2/10 (Category avg: 8.5/10) - **Operating Systems:** 9.2/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [MongoDB](https://www.g2.com/sellers/mongodb) - **Year Founded:** 2007 - **HQ Location:** New York - **Twitter:** @MongoDB (502,962 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/783611/ (7,665 employees on LinkedIn®) - **Ownership:** NASDAQ: MDB **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 56% Small-Business, 25% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (8 reviews) - User Interface (7 reviews) - Features (6 reviews) - Scalability (5 reviews) - Reliability (4 reviews) **Cons:** - Expensive Pricing (3 reviews) - Unclear Pricing (3 reviews) - Expensive (2 reviews) - High Memory Usage (2 reviews) - Latency Issues (2 reviews) ### 7. [Progress MarkLogic](https://www.g2.com/products/progress-marklogic/reviews) Progress MarkLogic is an enterprise-grade multi-model data management platform that unlocks value from complex data. It works with the full breadth of a company's information and makes it easily discoverable and ready to power high-value applications, decision intelligence and trustworthy AI. Organizations leverage integrated capabilities to integrate, harmonize, search and visualize multi-model data to build a connected data ecosystem as the secure and scalable foundation for the AI era. **Average Rating:** 4.3/5.0 **Total Reviews:** 64 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.6/10) - **Query Optimization:** 8.1/10 (Category avg: 8.0/10) - **Data Model:** 8.3/10 (Category avg: 8.5/10) - **Operating Systems:** 8.3/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Progress Software](https://www.g2.com/sellers/progress-software) - **Company Website:** https://www.progress.com/ - **Year Founded:** 1981 - **HQ Location:** Burlington, MA. - **Twitter:** @ProgressSW (48,853 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/progress-software/ (4,205 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 54% Enterprise, 25% Small-Business ### 8. [Azure Cosmos DB](https://www.g2.com/products/azure-cosmos-db/reviews) Azure Cosmos DB is a fully managed, globally distributed NoSQL and vector database service designed to support mission-critical applications with ultra-low latency and elastic scalability. It enables developers to build AI-powered applications and agents by providing seamless integration with AI services, allowing for efficient storage and querying of both NoSQL data and vectors. With its schema-agnostic JSON document model, Azure Cosmos DB simplifies the development process by automatically indexing all data, eliminating the need for manual schema or index management. The service offers comprehensive Service Level Agreements (SLAs), ensuring less than 10-millisecond read and write latencies and 99.999% availability, making it a reliable choice for applications requiring high performance and global reach. Key Features and Functionality: - Global Distribution: Azure Cosmos DB allows for turnkey global distribution, enabling data to be replicated across multiple regions worldwide, providing high availability and low latency access to data. - Elastic Scalability: The service offers elastic scaling of throughput and storage, allowing developers to scale resources up or down based on demand without downtime. - Multi-Model Support: It natively supports multiple data models, including document, key-value, graph, and column-family, catering to diverse application needs. - AI Integration: Built-in vector search capabilities simplify the development of AI applications by efficiently storing and querying vectors alongside NoSQL data. - Automatic Indexing: All data is automatically indexed, facilitating fast and efficient queries without the need for manual index management. - Comprehensive SLAs: Azure Cosmos DB provides industry-leading SLAs covering throughput, latency, availability, and consistency, ensuring predictable performance. Primary Value and Solutions Provided: Azure Cosmos DB addresses the challenges of building and managing globally distributed applications by offering a fully managed database service that ensures high availability, low latency, and elastic scalability. Its integration with AI services and support for multiple data models empower developers to create intelligent, responsive applications without the complexity of managing infrastructure. By automatically handling data distribution, scaling, and indexing, Azure Cosmos DB allows organizations to focus on innovation and delivering value to their users, making it an ideal solution for applications requiring real-time data access and global reach. **Average Rating:** 4.2/5.0 **Total Reviews:** 59 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.6/10) - **Query Optimization:** 6.7/10 (Category avg: 8.0/10) - **Data Model:** 8.3/10 (Category avg: 8.5/10) - **Operating Systems:** 7.8/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,105,844 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 44% Enterprise, 28% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (4 reviews) - Features (3 reviews) - Integrations (3 reviews) - Scalability (3 reviews) - Customization (2 reviews) **Cons:** - Expensive (3 reviews) - Cost Issues (2 reviews) - Complexity Issues (1 reviews) - Complex Usage (1 reviews) - Cost Increase (1 reviews) ### 9. [Arango](https://www.g2.com/products/arango/reviews) Arango provides a trusted data foundation for Contextual AI — transforming enterprise data into a System of Context that truly represents the business, so LLMs can deliver better outcomes with unlimited scale and cost efficiency. The Arango AI Data Platform gives developers a single, integrated environment to build and scale AI-powered applications without the complexity of stitching together multiple databases and tools. At its core is a massively scalable multi-model database that unifies graph, vector, document, and key-value data with full-text, geospatial, and vector search — creating the System of Context, the bridge between enterprise data and LLMs. The Arango AI Suite includes automated data pipelines, multimodal data ingestion, AIOps and MLOps, LLM integrations, Graph Analytics, agentic frameworks for context-aware Hybrid/GraphRAG, GraphML, natural-language support, and GPU acceleration — enabling repeatable ROI and faster innovation. Trusted by NVIDIA, HPE, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, Synopsys and Articul8, Arango powers enterprise AI with context, confidence, and scale. We are a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Learn more at arango.ai, LinkedIn, YouTube, and G2. **Average Rating:** 4.6/5.0 **Total Reviews:** 115 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.6/10) - **Query Optimization:** 8.4/10 (Category avg: 8.0/10) - **Data Model:** 9.2/10 (Category avg: 8.5/10) - **Operating Systems:** 8.4/10 (Category avg: 8.4/10) **Seller Details:** - **Seller:** [Arango](https://www.g2.com/sellers/arango) - **Company Website:** https://arango.ai/ - **Year Founded:** 2015 - **HQ Location:** San Francisco, CA - **LinkedIn® Page:** https://www.linkedin.com/company/5289249/ (106 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Senior Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Small-Business, 23% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (14 reviews) - Features (10 reviews) - Querying (7 reviews) - Intuitive (6 reviews) - Customization (5 reviews) **Cons:** - Poor Usability (5 reviews) - Difficult Learning (4 reviews) - Improvement Needed (4 reviews) - Learning Curve (4 reviews) - Learning Difficulty (4 reviews) ## Parent Category [NoSQL Databases](https://www.g2.com/categories/nosql-databases) ## Related Categories - [Graph Databases](https://www.g2.com/categories/graph-databases) - [Key Value Databases](https://www.g2.com/categories/key-value-databases) - [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas) --- ## Buyer Guide ### What You Should Know About Document Databases Software ### What are Document Databases Software? Document databases are a class of non-relational databases (NoSQL databases). Document databases store related data in a document format. They are used to design, query, and store the data in a document format (JSON document, XML, YAML, or binary formats such as BSON and PDF). The software is used for storing, retrieving, and managing document-oriented information also known as semi-structured data. Document databases software, also known as document-oriented databases software, is a subclass of key-value stores, which is a NoSQL database concept. In a key-value store or key-value database, data is managed (stored, received) by using associative arrays. This type of data structure is called a “dictionary”. Dictionaries are a collection of objects, and objects are the central data storage repository that store different fields that contain the data. Some of the key examples include MongoDB, Amazon DynamoDB, Google Cloud Firestore, Couchbase Server, Apache CouchDB, among several others. Many of these databases such as MongoDB and Couchbase server are open source in nature. To call the data when required, a key is used, which acts as the unique identifier for the record within the entire database. When talking about document databases, it’s important to identify what exactly is a “document”. A document stores or encodes all the data in a standard format. These formats include JSON, XML, YAML, and others.  Document databases differ greatly from traditional relational SQL databases. The major cause of difference between the two types of databases is that relational databases store data models as a relation—tables, rows, and an object could be a part of numerous tables. However, document databases store all the related information of an object within a single instance of the database, and each object can be stored uniquely. Document databases do not have any restrictions as relational databases do. **CRUD operation** The core operations for document databases are abbreviated as CRUD—create, retrieve, update, and delete. These are the four basic operations that all document databases support. **What is a key?** As stated earlier, a key acts as a unique identifier that is representative of the document. It is used to retrieve the data from the document database. There is usually an index of keys available, which makes it easier for the user to refer to and call back the data represented by that particular key. In case a user needs to add or delete a document within the document database, a key can be used for the same. **Data retrieval ** Although a key-to-document method is enough for data retrieval, the document database offers an API that users can use to query data based on content. The set of query language or query APIs vary significantly between different data model implementations. In this, document databases make use of the metadata of the content to classify the content and differentiate it from one another. **Data organization** There are several ways to arrange documents within document databases software. A document can exist as single or multiple collections. **Hierarchy:** Documents are grouped in a tree-like structure and have a typical path. **Collections:** Group of documents within the software.  **Data tags:** Documents or additional data located outside the content. **Why use document databases?** Since the data is stored in a format that is very close to the application development code used by developers, there is much less translation required for the data to be used by an application. These types of databases give developers the freedom and the flexibility to rework various documents in the format suited for that application. In turn, their application needs to change over time, the document database can also be modeled in the same data format as required by the application. **When can a user opt for document databases?** Document databases software is used to store large volumes of data in a key-value, making it easy for the user to access the data. Considering the significant amount of data to be processed, some of the key uses of the software include content management, user profiles for a company, catalogs, and several other documents. ### What are the Common Features of Document Databases Software? The need for document databases has become imminent with the rise of unstructured data. The following section covers the core features of document databases software that can help users in several ways: **Document databases software are NoSQL:** NoSQL database software was created to meet the needs of the internet era, with the rise of unstructured data. NoSQL document databases have been attributed with increasing the pace of app development and supporting data scaling and new application structures and paradigms. Since document databases are NoSQL in nature, several elements can be indexed and called faster by application developers. The data structure in this software is designed for unstructured data or big data, allowing it to plow through large amounts of data while being able to maintain its efficiency and flexibility.  **Schema support:** Document databases software can support several different schemas of data because there are no restrictions in the structure of the data. The schema is flexible and can be used for different types of document formats to process queries faster. **Richness of indexing:** Several document databases support ad hoc queries, indexing, full-text search, and real-time data collections to ensure that users can access, analyze, and transform data as required.  **Distributed database:** Document databases software are distributed as their central principle, unlike monolithic relational databases. Since the documents are individual and independent, they can be located or distributed on multiple servers across the globe. This is very useful for companies such as e-commerce that have locations across the globe. It also supports replication and self-healing capabilities to ensure that all applications support high availability. The software also supports data sharding (a data partitioning technique) to ensure scalability across numerous independent servers. ### What are the Benefits of Document Databases? The inclusion of document databases software within a firm can help manage thousands of documents that exist within a company. Some of the key benefits of document database software include: **Easy availability:** The data is not spread out or linked over different databases but rather is available in a single database. This is one of the main benefits of document databases. Although interlinking of documents is possible, it is not usually recommended since it would make the database relational in nature and also add to the complexity of managing the database.  **No foreign keys:** Having no foreign keys indicates that there is no relationship formed between the data. Without the existence of this dynamic, documents can be created, managed, and deleted independently making it much faster to process data for several applications querying it. **Open formats:** One of the key benefits of using document databases is that they support several open formats. The process can use XML, JSON, and several other formats for the data. **Supports scalability:** As the amount of data generated increases every minute, the database software being used by customers also needs to ensure flexibility and scalability. Document databases allow users to easily add datasets to scale up, which means more future-proof features. **New integration support:** Since document databases are much more flexible and scalable compared to traditional relational databases, integrating new data into the database software is easy. There is no need for consistency in data formats, and large amounts of unstructured data or big data can be stored. **Fast query nature:** One of the key features of document databases software is its nature to improve the speed of queries. Using document databases can enable several app developers to store and query requested data in the same document-model format that is being used in the code being developed. For example, in the healthcare field where time is of the essence, a user can immediately get access to health records instead of facing any delays or issues.  ### Who Uses Document Databases Software? Some of the main users of document databases software have been listed below: **Database administrator (DBA):** Key persona handling the software. The schema is determined by the DBA. They are also responsible for setting up different user IDs and rights for those who can access the database. This persona also monitors the database, ensures security is maintained, ensures backup and recovery plans are active, tracks errors or failures, provides database support, and several other requirements. **Software developers:** Programmers and software developers would need access to data when developing a software application or making changes to one. This persona will have access to the document database to ensure that the software application development process goes smoothly. In addition, document databases have a long list of supported programming languages which includes Perl, Java, C, C++, Python, and Javascript. **Managers:** Managers can use the database temporarily or whenever they require new information. This persona doesn't use it daily as the other personas, only when the requirement arises.  **Other users:** This includes users such as analysts and scientists who do not write a code, but use the document databases software to query some information as and when required. They have interactions with the database as per their data requirements. #### Software Related to Document Databases Software Related solutions that can be used together with document databases software include other key NoSQL document databases as follows: [XML databases software](https://www.g2.com/categories/xml-databases) **:** XML database software are a subclass of document databases, wherein the database primarily works with XML documents. [Graph databases](https://www.g2.com/categories/graph-databases) **:** Graph databases use graphs and graph structures for database queries. The graph is used to connect the data stores to nodes and edges, where edges form the relationship between nodes. [Columnar databases software](https://www.g2.com/categories/columnar-databases) **:** Under this type of database software, a column store is used to store data. Data can be read quickly when it's in a columnar format. Since the data in the column is of a uniform type, it provides for storage opportunities and storage optimizations within the database. ### Challenges with Document Databases Software Document databases solutions can come with their own set of challenges.  **Consistency issues:** A major challenge that comes with document databases is data consistency and limitations to the checking process. Since the data is not related to other data points as in relational database service there are chances of duplicated data, redundant data, unrelated data being collected together, among several other possibilities. This could hamper the performance of the database. **Security challenges:** Since document databases are primarily focused on the numerous unstructured data stores available from several sources which include web applications, it leads to several points to be exposed where data hackers can get through and breach system security. This could lead to data leaks and unintended personnel getting their hands on critical data. **Issue with atomicity:** In [database management systems (DBMS) software](https://www.g2.com/categories/database-management-systems-dbms), atomicity is one of the ACID transactions. Atomicity is the guarantee that each transaction of data is treated as a single unit that either completely succeeds or fails; there is no in-between. A single command is given to make changes to the data, and all subsequent queries will also reflect these changes. However, in document databases, a change that affects two data collections will need to be run twice which does not follow the principle of atomicity. **Data loss issues:** A key challenge with document databases is data loss. Data loss issues could arise due to wrong configurations since a single node is not being used. ### How to Buy Document Databases Software #### Requirements Gathering (RFI/RFP) for Document Databases Software When choosing a document databases software, some important criteria need to be considered. Factors such as flexibility, usability, functionality, security are key criteria that cannot be compromised. Having features such as dashboards and visualizations is a great benefit to ensure ease of analyzing the data storage and keeping track of several queries. Other important features to look out for are support and development—the hours customer support is available, if they are open to solving queries, and continuous information on updates on the latest new additions and developments in the document databases software, among several other features. As a business grows, scalability is an important criterion to keep in mind. With tons of unstructured data or big data being generated, the document databases software should be able to manage millions of columns of data. Another key feature to ensure that the document databases software has is integration support. Application developers with several different software and this software should be able to easily call data from the document database as required. How these integrations are managed and how the company ensures all these software connect with the document databases software is critical for the smooth flow of data. Checking on what programming languages are supported by the document database is a good factor to look into. #### Compare Document Databases Software Products **Create a long list** In this step, buyers should keep their options open to consider the full range of products. Buyers have the freedom to explore numerous offerings that this software market has. The long list can be made much more concise and smaller by addressing the goals. **Create a short list** Buyers can make much more granular comparisons on this step. In addition to this, buyers can use the G2 reviews to further narrow this list down. **Conduct demos** Once the list has been reduced to a couple of vendors, buyers may begin to request a demo. During the demo, buyers should seek out information that is related to their non-negotiable terms. This is a good stage where the buyer can delve more deeply into understanding how secure their document database will be, high-performance support availability, what the features are—latency in loading document databases, after-service support, staff training, and other additional features that can be provided when opting for their document databases solution.  #### Selection of Document Databases Software **Choose a selection team** Choosing the right team to work together to decide the right document databases software is a critical part of the process since several personas would need to access the database applications as per requirements. The team should include a mix of different personas who have the required skills, the interest, and the time. Some roles include database admins, application developers, key management leaders, IT heads, and others. **Negotiation** A buyer can choose to negotiate to trim costs. The buyer needs to note that if in the future there is a requirement for scaling, there would be additional costs or an increase to the subscription pricing. It is a good practice to check with the document database vendor if they offer any cloud support, training, and other factors. Keeping such factors in mind will help the buyer to put forward better negotiation tactics for the specific functions that matter. **Final decision** Once all the steps are complete, the final decision is made weighing all factors and scenarios. Having a trial run of the software is a good place to start by using smaller document databases. A small group of database admins can pass on their views to the team making the final decision.