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You can store, manage, and analyze the massive amounts of data you need to train machine learning models. You also have a rich set of MongoDB functionality: powerful query and data aggregation language, indexes, and horizontal scaling to efficiently process and extract information for model training.
MongoDB provides fast read and write processing through an optimized storage engine. This allows applications to quickly respond to user requests and process large amounts of data.
MongoDB is also used to implement a queue broker where messages can be stored and processed asynchronously. At the same time, MongoDB collections are suitable for storing messages and indexes are suitable for fast access and retrieval. Read and write operations can be handled efficiently, ensuring reliable delivery of messages.
MongoDB provides high speed as well as flexibility in handling data, which allows it to be used effectively as a cache. Cached data can be updated or cleaned as needed to ensure that the information is up to date.
MongoDB provides efficient data storage and processing, allowing you to quickly retrieve information, use caching, and optimize database queries.
MongoDB is a database management system that works with a document-oriented data model and stores information in the form of documents or collections. Unlike relational DBMSs, it does not require tables, schemas, or a separate query language.
The DBaaS concept involves storing and managing data in a cloud environment, where the user does not need to install and maintain a database - he gets a ready-made and optimized solution in the cloud.
Open source distributed streaming processing platform. Provides high throughput and ensures streamlined data delivery between producers and consumers. Ideal for streaming data processing, event-notification architecture implementation and real-time big data processing.
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High-performance open source columnar database. Provides fast analytics and big data processing. It is optimized to run complex analytical queries on large datasets. ClickHouse offers high speed, efficient resource utilization and scalability, making it a popular choice for real-time analytics and big data workloads.
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A distributed search and analytics system. It is well suited for scenarios where fast and scalable data access is required, such as site search, log analysis and real-time data monitoring.
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High-performance distributed database with great flexibility, scalability and parallel query processing. It is the best choice for analytical tasks and big data processing.
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A powerful relational database that provides high performance and reliability. It is optimally suited for web applications, e-commerce and CMS where handling large amounts of structured data is important.
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A powerful data mining and analysis tool optimized for processing large volumes of data and extracting valuable insights with full-text search, advanced analytics and data security. Well suited for complex analytical systems and real-time data monitoring.
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A powerful and reliable open source relational database. It has a wide range of features, supports ACID transactions and provides flexibility for storing and processing structured data. Supports extensions and provides powerful features for database developers and administrators.
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A fast, scalable storage system best suited for caching, sessions, message queues, and lightweight data analytics. Its flexibility in data structures and high performance make Redis a popular solution for web applications.
DiscoverProvide continuous protection and instant access to rapidly growing datasets. Highly scalable and cost effective storage that integrates with your application scenarios.
Distribute traffic and workloads in the most efficient way: no server downtime, overload or underutilization.
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You can use MongoDB to create applications that do not contain a large number of links, but need to store heterogeneous data.
For example, MongoDB would be suitable for storing:
Managed Service for MongoDB is a service that provides a ready-to-use MongoDB database management system, whose cluster uses cloud architecture for hosting.
You can focus on working with the database, enjoy all its benefits, and we will take care of the technical issues of organizing the database cluster and its operation.
Our area of responsibility includes:
By default, automatic cluster backup is performed every 24 hours between 1:00 AM and 5:00 AM GMT+4. During the backup, the access to the clusters is preserved. Backups are stored for 7 days after creation.
You can restore data to any saved backup.
The actual structure of cost formation and service usage rates are given in the "Rates" section of the documentation or in the configurator when ordering a service.
In general, the cost of using the MongoDB managed database service is calculated based on the actual use of resources, based on the following parameters:
In general, MongoDB Community Edition and Enterprise Edition have the same basic features such as queries, aggregation, replication, segmentation, etc. But MongoDB Enterprise Edition has the following additional functionality:
If the DBMS version you are using is no longer supported by the developer, you will still have access to your data. However, in this case, the creation of new cluster nodes will not be available and other features may be restricted. You will be notified by e-mail and in your personal cabinet about automatic upgrade to the nearest supported version, about the dates of work and database availability. Such upgrade will be performed even if the client has disabled automatic upgrade.
The client is notified about minor version update at least 7 days in advance, about major version update at least 1 month in advance. Upon receipt of such notification, the customer should check the software interfacing with MongoDB for compatibility with the new version. If necessary, the customer should take steps to ensure compatibility with the new version.
Currently, each MongoDB cluster can have no more than 5 hosts, and the maximum cluster storage size should not exceed 600 GB.
In MongoDB, the maximum document size is limited to 16 MB, but when using GridFS, the document size is limited only by the size of the available disk space in the cluster.
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MongoDB databases can run on several servers at once: segmentation allows you to distribute the load, and replication allows you to create copies. This allows you to increase the speed and fault tolerance of the database.
If there are several replicas in a database cluster, if one of them is lost, the cluster will not take the cluster down.
If you work in IT, the term “cloud” isn’t just a buzzword — it’s a powerful tool for storing and processing data.
PaaS (Platform as a Service) was initially designed as a solution for developers to build applications without dealing with server setup and infrastructure management.
TCP/IP is the backbone of everything. This protocol stack is like the circulatory system of the Internet and cloud computing.