Top 7 Multi-Model Databases
Companies handle data on a daily basis and every company needs an effective database system to store data in all possible formats. There is a wide range of database systems and modern-day technology has revolutionized these systems to meet the changing needs of database users.
Several years ago, the relational database model used to be the standard database model, but things have changed over the years. The truth is that the relational database model can no longer meet the changing needs of organizations. Today, many organizations have to deal with voluminous amounts of data in different formats.
As a result, it is difficult to use ordinary database models such as the relational model to store different data types. Database users need a flexible database system that can support multiple database models. This is the reason why multi-model databases are important.
What are the Top Multi-Model Databases: MarkLogic, ArangoDB, OrientDB, Azure Cosmos DB, FoundationDB, Couchbase, Apache Ignite are some of the Top Multi-Model Databases.
What are Multi-Model Databases?
Multi-model databases refer to databases that combine different types of database models into one integrated database engine. They provide a single back end and support multiple data models depending on the applications they support.
Such databases can accommodate various data models including relational, object-oriented, key-value, wide-column, document, and graph models. Multi-model databases don’t store data like ordinary table-based databases and this makes it possible to store all type of data including semi-structured and unstructured data types. This means that multi-model databases eliminate the problem of fragmentation and ensure consistency.
These databases can perform most of the basic operations performed by other databases such as storing data, indexing, and querying. Many database vendors offer multi-model databases with different features. Prospective users can choose the appropriate database system for their organization depending on their preferences and the database needs of the organization. Let’s look at some of the common features of multi-model databases.
- Data Storage, Backup, and Recovery: Like most database systems, the main purpose of multi-model databases is to store data. They allow databases users to store data in the form of documents, graphs, images and other formats depending on their needs. Users can also create data backups and recover data.
- Querying and Indexing Mechanisms: Multi-model databases use a querying language to perform queries. They also utilize indexing mechanisms to ensure efficient querying.
- ACID transactions: Most multi-model databases are ACID (Atomicity, Consistency, Isolation, Durability) complaint. In simple terms, these databases are fault tolerant. They guarantee validity in case of power failure or unexpected errors.
- Integration: The best multi-model database products integrate seamlessly with the most recent database models and capabilities. They allow database developers and users to develop the data models they need on a single back end. Users can also integrate data from multiple sources and in multiple formats.
- Advanced Security Features: Data security is one of the most important features of any database system. Multi-model databases consist of advanced security features such as topnotch encryption and auditing mechanisms to secure your data.
Some of the benefits include:
- Flexibility - They consolidate different data types in a single platform making it possible to maximize resources.
- Minimal operational costs.
- Fault tolerance - ACID transactions ensure data consistency.
- Different components can be scaled independently.
- Improved reliability.
Top Multi-Model Databases
ArangoDB helps to arrange the data in a very creative and flexible way. The data can be stored as key or value pairs, graphs or documents and all of this can be accessed by just one query language. For safer option more than declarative models can be used in the query. The reason why users can combine different models and their features in one query is because ArangoDB uses the same core and same query language for all the data models. If a new product is being developed then every now and then new ideas are generated and the model…
OrientDB features a 2nd generation distributed graph database that is unique, multi model graph database that offers flexibility for documents all in one product. It includes replication and sharding that can be used in most complex use cases and with an open source that is compatible with Apache 2 license. OrientDB works fast and capable of storing 220,000 records per second on most common hardware and supports schema less, full and mixed modes including SQL as one of the query language used. OrientDB provides safety in all confidential data that is present with the use of authentication, password and data-at-rest…
Azure Cosmos DB
• Automatic indexing allows for filtering against multiple different properties in real-time
• Social features: in-game chat messages, player guild memberships, challenges completed, high-score leader-boards, and social graphs
• Five different consistency levels: bounded staleness, strong, session, eventual, and consistent-prefix
• LINQ language integrated queries
• Geo-fencing ensures data governance and compliance restrictions
• Restoration of deleted data from backups
• SSD Storage - $ 0.25 GB / month
• Reserved Rus / sec -$0.008 / hour
FoundationDB is a multi-model NoSQL database with a shared nothing architecture. It was designed around a "core" database, with additional features supplied in "layers”. It organizes data as an ordered key-value store and employs ACID transactions for all operations. FoundationDB is especially well-suited for read / write workloads but also has excellent performance for write-intensive workloads. Users interact with the database using an API language binding. FoundationDB provides amazing performance on commodity hardware, allowing you to support very heavy loads at low cost. NoSQL database design involves a number of fundamental technical alternatives. FoundationDB is designed to perform transaction processing…
• Interactive transactions: Client code can make an iterative series of reads and writes over the network
• A status monitoring tool lets users monitor cluster health and utilization of the cluster’s physical resources
• Ordered key-value data model
• Distributed cashing
• Transactional watches on keys ensure that users are notified if the value changes
• Multi-version concurrency control provides transactionally isolated reads without locking data or blocking writes
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Built with the most powerful NoSQL technology, the Couchbase was architected on top of an open source foundation for the massively interactive enterprise. Geo-distributed Engagement Database provides unmatched developer agility and manageability, as well as unparalleled performance at any scale, from any cloud to the edge. Couchbase is extremely straightforward to deploy and manage. Features such as replication are built in and happen automatically. Topology changes happen transparently without needing changes to the application or other Couchbase nodes. The entire cluster is managed through a single administrator console that offers single-click cluster expansion and rebalance. Even highly sophisticated technology, such…
• Built-in Big Data and SQL integration
• Memory-first Architecture: Intelligently keeps frequently accessed documents, metadata, and indexes in RAM
• Built-in fault tolerance to get the data when and where you need it
• Unified Programming Interface
• Rich, web-based administration console for monitoring data collected from running deployments
• Built-in full-text search makes it simple for developers to add intelligence to apps
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Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale. Apache Ignite's database utilizes RAM as the default storage and processing tier, thus, belonging to the class of in-memory-computing platforms. Regardless of the API used, data in Ignite is stored in the form of key-value pairs. The database component scales horizontally, distributing key-value pairs across the cluster in such a way that every node owns a portion of the overall data set. Data is rebalanced automatically whenever a node is added to or removed from the cluster. Apache…
• Memory-centric storage: Store and process distributed data in memory and on disk
• Collocated processing: Avoid data noise by sending computations to cluster nodes
• Distributed key-value: Read, write, transact with fastest key-value data grid and cache
• Memory-centric SQL database with support for joins
• Native Hadoop MapReduce provides a significant performance boost
• User-Defined Services
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