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Managed Databases

MongoDB: NoSQL - Vector Database​

MongoDB is a popular NoSQL database that has evolved to support advanced data types and querying capabilities, including vector search. A vector database is a type of database designed to efficiently store and query large datasets of dense vectors, typically used in machine learning and artificial intelligence applications.

Key Features​

MongoDB's vector database capabilities offer the following key features:

  1. Vector Data Type: MongoDB supports a native vector data type, allowing developers to store and query vectors of varying dimensions.
  2. Indexing: MongoDB provides specialized indexing techniques, such as inverted indexes and quantization-based indexes, to enable efficient similarity search and nearest-neighbor queries.
  3. Query Operators: MongoDB introduces new query operators, like $near and $box, to facilitate vector-based queries, including similarity search, range queries, and geometric queries.
  4. Integration with Machine Learning Libraries: MongoDB integrates seamlessly with popular machine learning libraries, such as TensorFlow and PyTorch, enabling developers to build and deploy AI models using vector data.

Benefits​

By leveraging MongoDB's vector database capabilities, developers can:

  1. Improve Query Performance: Achieve faster query execution times for complex vector-based queries.
  2. Simplify Data Management: Store and manage large vector datasets with ease, using a scalable and flexible NoSQL database.
  3. Enhance Machine Learning Workflows: Streamline the development and deployment of AI models using vector data, with tight integration with popular machine learning libraries.

Overall, MongoDB's vector database capabilities provide a powerful foundation for building innovative applications that rely on vector search and machine learning, while simplifying data management and improving query performance.

Available MongoDB configurations​

ConfigurationSpecifications
MongoDB Node: 2 CPU Core8 GB RAM (Max. 200 GB Disk)
MongoDB Node: 4 CPU Core16 GB RAM (Max. 500 GB Disk)
MongoDB Node: 8 CPU Core32 GB RAM (Max. 1.000GB Disk)
MongoDB Node: 16 CPU Core64 GB RAM (Max. 2.000 GB Disk)
MongoDB Node: 32 CPU Core128 GB RAM (Max. 4.000 GB Disk)
MongoDB Node: 64CPU Core256 GB RAM (Max. 12.000 GB Disk)

Kafka: Event Streaming Database​

Apache Kafka is a popular open-source event streaming platform that has evolved to support advanced data processing and storage capabilities, including event streaming databases. An event streaming database is a type of database designed to efficiently store and process large volumes of event data, typically used in real-time data processing and analytics applications.

Key Features​

Kafka's event streaming database capabilities offer the following key features:

  1. Event Data Model: Kafka supports a native event data model, allowing developers to store and process events with varying structures and schemas.
  2. Streaming Data Processing: Kafka provides specialized streaming data processing capabilities, such as event-time processing and windowed aggregations, to enable real-time data processing and analytics.
  3. Distributed Architecture: Kafka's distributed architecture allows for scalable and fault-tolerant event data processing, with features like replication, partitioning, and leader election.
  4. Integration with Data Processing Frameworks: Kafka integrates seamlessly with popular data processing frameworks, such as Apache Spark, Apache Flink, and Apache Beam, enabling developers to build and deploy real-time data pipelines.

Benefits​

By leveraging Kafka's event streaming database capabilities, developers can:

  1. Improve Real-time Data Processing: Achieve faster and more accurate real-time data processing and analytics, with support for event-time processing and streaming data integration.
  2. Simplify Data Integration: Simplify data integration and processing, using a unified platform for event data ingestion, processing, and storage.
  3. Enhance Scalability and Reliability: Build scalable and reliable real-time data systems, with Kafka's distributed architecture and fault-tolerant design, to support high-volume and high-velocity event data streams.

Overall, Kafka's event streaming database capabilities provide a powerful foundation for building innovative applications that rely on real-time event data processing and analytics, while simplifying data integration and improving scalability and reliability.

Available Kafka configurations​

ConfigurationSpecifications
Kafka Cluster-3 Node: 8 CPU Core8 GB RAM (Max. 1.200 GB Disk)
Kafka Cluster-6 Node: 16 CPU Core64 GB RAM (Max. 3.000 GB Disk)
Kafka Cluster-9 Node: 32 CPU Core128 GB RAM (Max. 4.500 GB Disk)
Kafka Cluster-15 Node: 64 CPU Core256 GB RAM (Max. 7.500 GB Disk)

PostgreSQL: Object Relational Database​

PostgreSQL is a powerful open-source object-relational database management system that has evolved to support advanced data types and querying capabilities, including object-oriented data modeling. An object-relational database is a type of database designed to efficiently store and manage complex data relationships, typically used in enterprise applications and data warehousing.

Key Features​

PostgreSQL's object-relational database capabilities offer the following key features:

  1. Object-Oriented Data Types: PostgreSQL supports a range of object-oriented data types, including arrays, composite types, and user-defined types, allowing developers to model complex data relationships.
  2. Inheritance and Polymorphism: PostgreSQL's object-relational model supports inheritance and polymorphism, enabling developers to define hierarchical relationships between tables and objects.
  3. Querying and Indexing: PostgreSQL provides advanced querying and indexing capabilities, including support for SQL and procedural languages, to enable efficient data retrieval and manipulation.
  4. Extensions and Integration: PostgreSQL integrates seamlessly with a range of programming languages and frameworks, including Java, Python, and Ruby, and supports extensions for data analytics, machine learning, and more.

Benefits​

By leveraging PostgreSQL's object-relational database capabilities, developers can:

  1. Improve Data Modeling: Achieve more accurate and flexible data modeling, using object-oriented concepts and complex data types to represent real-world entities and relationships.
  2. Simplify Data Management: Simplify data management and administration, using a unified platform for data storage, querying, and analysis, with support for advanced data types and relationships.
  3. Enhance Scalability and Performance: Build scalable and high-performance data systems, with PostgreSQL's robust and reliable architecture, to support large and complex datasets.

Overall, PostgreSQL's object-relational database capabilities provide a powerful foundation for building innovative applications that rely on complex data relationships and object-oriented data modeling, while simplifying data management and improving scalability and performance.

Available PostgreSQL configurations​

ConfigurationSpecifications
PostgreSQL Node: 2 CPU Core8 GB RAM (Max. 200 GB Disk)
PostgreSQL Node: 4 CPU Core16 GB RAM (Max. 500 GB Disk)
PostgreSQL Node: 8 CPU Core32 GB RAM (Max. 1.000GB Disk)
PostgreSQL Node: 16 CPU Core64 GB RAM (Max. 2.000 GB Disk)
PostgreSQL Node: 32 CPU Core128 GB RAM (Max. 4.000 GB Disk)
PostgreSQL Node: 64CPU Core256 GB RAM (Max. 12.000 GB Disk)

Qdrant: Ultra-Fast Vector and Similarity Search Database​

Qdrant is a cutting-edge, open-source database designed for ultra-fast vector and similarity search, enabling developers to build scalable and efficient applications that rely on complex data relationships and vector-based queries. A vector and similarity search database is a type of database optimized for storing and querying large datasets of dense vectors, typically used in machine learning, artificial intelligence, and data science applications.

Key Features​

Qdrant's ultra-fast vector and similarity search database capabilities offer the following key features:

  1. Vector Data Type: Qdrant supports a native vector data type, allowing developers to store and query vectors of varying dimensions, with optimized storage and indexing for fast query performance.
  2. Approximate Nearest Neighbors (ANN) Search: Qdrant provides highly optimized ANN search algorithms, enabling fast and accurate similarity search, with support for various indexing techniques and distance metrics.
  3. Filtering and Indexing: Qdrant offers advanced filtering and indexing capabilities, including support for bitmap indexes and hierarchical indexing, to enable efficient querying and data retrieval.
  4. Scalability and High Performance: Qdrant is designed for scalability and high performance, with support for distributed architecture, parallel querying, and optimized data storage, to handle large and complex datasets.

Benefits​

By leveraging Qdrant's ultra-fast vector and similarity search database capabilities, developers can:

  1. Improve Query Performance: Achieve lightning-fast query performance, with optimized indexing and search algorithms, to enable real-time applications and services.
  2. Simplify Vector Data Management: Simplify vector data management, using a unified platform for data storage, querying, and analysis, with support for advanced vector data types and operations.
  3. Enhance Machine Learning and AI Workflows: Streamline machine learning and AI workflows, with Qdrant's optimized support for vector-based queries and similarity search, to enable faster and more accurate model development and deployment.

Overall, Qdrant's ultra-fast vector and similarity search database capabilities provide a powerful foundation for building innovative applications that rely on complex vector data relationships and similarity search, while simplifying data management and improving query performance.

Available Qdrant configurations​

ConfigurationSpecifications
Qdrant Node: 2 CPU Core8 GB RAM (Max. 200 GB Disk)
Qdrant Node: 4 CPU Core16 GB RAM (Max. 500 GB Disk)
Qdrant Node: 8 CPU Core32 GB RAM (Max. 1.000GB Disk)
Qdrant Node: 16 CPU Core64 GB RAM (Max. 2.000 GB Disk)
Qdrant Node: 32 CPU Core128 GB RAM (Max. 4.000 GB Disk)
Qdrant Node: 64CPU Core256 GB RAM (Max. 12.000 GB Disk)

Valkey: In-Memory Caching Database​

Valkey is a high-performance, open-source in-memory caching database designed to accelerate data access and reduce latency, enabling developers to build scalable and responsive applications. An in-memory caching database is a type of database that stores data in RAM, providing fast data access and retrieval, typically used in real-time web applications, gaming, and financial services.

Key Features​

Valkey's in-memory caching database capabilities offer the following key features:

  1. In-Memory Data Storage: Valkey stores data in RAM, providing fast data access and retrieval, with support for various data structures and caching strategies.
  2. High-Performance Caching: Valkey offers advanced caching capabilities, including support for caching hierarchies, cache expiration, and cache invalidation, to optimize data freshness and reduce latency.
  3. Distributed Architecture: Valkey supports a distributed architecture, enabling developers to scale their caching layer horizontally, with support for clustering, replication, and load balancing.
  4. Integration with Existing Databases: Valkey integrates seamlessly with existing databases, including relational and NoSQL databases, providing a caching layer that accelerates data access and reduces the load on underlying databases.

Benefits​

By leveraging Valkey's in-memory caching database capabilities, developers can:

  1. Improve Application Performance: Achieve significant improvements in application performance, with fast data access and retrieval, to enable responsive and interactive user experiences.
  2. Reduce Latency: Reduce latency and improve data freshness, with optimized caching strategies and advanced cache management, to enable real-time data processing and analytics.
  3. Increase Scalability: Increase scalability and reliability, with Valkey's distributed architecture and support for clustering and replication, to handle large and unpredictable workloads.

Overall, Valkey's in-memory caching database capabilities provide a powerful foundation for building innovative applications that require fast data access and low latency, while improving application performance, reducing latency, and increasing scalability.

Available Valkey configurations​

ConfigurationSpecifications
Valkey Node: 2 CPU Core8 GB RAM (120 GB Disk)
Valkey Node: 4 CPU Core16 GB RAM (240 GB Disk)
Valkey Node: 8 CPU Core32 GB RAM (480 GB Disk)
Valkey Node: 16 CPU Core64 GB RAM (960 GB Disk)

OpenSearch: Search and Analytics​

OpenSearch is an open-source search and analytics engine designed to provide fast, scalable, and flexible search capabilities, enabling developers to build innovative applications that require powerful search and analytics functionality. A search and analytics engine is a type of software that indexes and queries large datasets, providing insights and patterns in the data, typically used in web search, e-commerce, and data science applications.

Key Features​

OpenSearch's search and analytics capabilities offer the following key features:

  1. Distributed Architecture: OpenSearch supports a distributed architecture, enabling developers to scale their search and analytics layer horizontally, with support for clustering, replication, and load balancing.
  2. Inverted Indexing: OpenSearch uses inverted indexing, a technique that allows for fast query performance, with support for various indexing strategies and optimization techniques.
  3. Query DSL: OpenSearch provides a powerful query domain-specific language (DSL), enabling developers to build complex queries and filter data, with support for Boolean queries, faceting, and aggregation.
  4. Integration with Data Sources: OpenSearch integrates seamlessly with various data sources, including databases, logs, and messaging queues, providing a unified search and analytics layer for diverse data sets.

Benefits​

By leveraging OpenSearch's search and analytics capabilities, developers can:

  1. Improve Search Performance: Achieve fast and relevant search results, with optimized indexing and query performance, to enable responsive and interactive user experiences.
  2. Gain Data Insights: Gain insights and patterns in large datasets, with advanced analytics and aggregation capabilities, to enable data-driven decision-making and business intelligence.
  3. Enhance Application Functionality: Enhance application functionality, with flexible and customizable search and analytics capabilities, to enable innovative use cases and applications, such as recommendation systems, sentiment analysis, and predictive analytics.

Overall, OpenSearch's search and analytics capabilities provide a powerful foundation for building innovative applications that require fast, scalable, and flexible search functionality, while improving search performance, gaining data insights, and enhancing application functionality.

Available OpenSearch configurations​

ConfigurationSpecifications
OpenSearch Node: 4 CPU Core16 GB RAM (500 GB Disk)
OpenSearch Cluster-3 Node: 8 CPU Core32 GB RAM (1.200 GB Disk)
OpenSearch Cluster-6 Node: 16 CPU Core64 GB RAM (2.400 GB Disk)
OpenSearch Cluster-9 Node: 32 CPU Core128 GB RAM (3.600 GB Disk)
OpenSearch Cluster-15 Node: 64 CPU Core256 GB RAM (7.200 GB Disk)