Improve your data analytics with Managed Service for Elasticsearch in the cloud. Auto-scaling, high availability, and full integration with cloud services to efficiently process, search, and analyze your information.
ElasticSearch provides efficient storage and quick access to logs, allowing you to analyze and extract valuable information from them. ElasticSearch also offers powerful tools for searching and aggregating data, enabling various types of analytics, including monitoring, error tracking, and user behavior analysis.
ElasticSearch is a powerful search platform that provides full-text search, filtering and data aggregation functionality. It allows you to efficiently index and search large volumes of textual information. ElasticSearch supports various types of search queries, including keyword, phrase, prefix, and complex logical queries and filters.
ElasticSearch can be used to collect and analyze monitoring data from various sources. It allows you to monitor system health in real time, check performance and detect problems to optimize system performance.
Managed Service for Elasticsearch frees developers from having to deal with backups, logging, monitoring, scaling, hardware configuration, and software patching. Developers can fully focus on building high-quality applications.
When storing and transmitting data, the service uses encryption, which guarantees its protection from unauthorized access. In addition, the service allows you to use your own encryption key, providing an additional level of control over the security of stored data.
Managed Service for Elasticsearch has a serverless architecture that gives you the ability to scale disk space and RAM according to your application requirements. This allows you to flexibly adjust resources to the workload, ensuring high performance and efficient resource utilization.
The service is fully compatible with the Elasticsearch API, and supports a wide range of data formats and clients. This means that applications that already use Elasticsearch can easily migrate to Managed Service for Elasticsearch as a replacement without the need for significant code changes or integration.
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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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 flexible NoSQL database optimized for storing unstructured data such as documents, images and social media. With easy scalability and high performance, MongoDB is the ideal choice for Big Data projects and applications requiring real-time 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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Elasticsearch is a highly scalable distributed search engine for full-text search and data analysis with a web interface that can help you quickly find information in a large data set, for example, from a document in a corporate system to a product on a marketplace.
Elasticsearch is able to store a huge array of data and search through it.
Elasticsearch can be suitable for the following application scenarios:
Elasticsearch is suitable for organizing full-text search, both by separately specified criteria and fuzzy search. This solution is especially relevant for organizing searches among a large number of products and items, for example, for e-commerce: online stores, online pharmacies, marketplaces. An example of using Elasticsearch is the online store and catalog of Leroy Merlin products.
Since Elasticsearch is a non-relational storage of unstructured documents, it is perfect for organizing messaging systems. For example, Netflix and Tinder messaging systems are organized on Elasticsearch.
Elasticsearch allows you to store and process any data, logs, logs, system data, database analytics. Based on this data, you can build reports, dashboards, track business metrics, and customize alerts. An example is Airbus, which organized its document storage system on Elasticsearch.
Managed Service for Elasticsearch - provides a ready-to-use Elasticsearch search engine with a cluster hosted in the cloud.
You can focus on working with the system, 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, the cluster is automatically backed up every hour with all indexes saved. All backups are available for restoring within 7 days after creation.
Yes, you can upgrade the cluster version, but downgrading the version cannot be done. There are limitations for version upgrades, so you cannot upgrade a major version of the cluster, for example, from V7.X to V8.X.
However, you can always create a new cluster with the version you need and migrate data from the original cluster to it, and after successful data migration you can abandon the original cluster.
You do not need to install updates containing only bug and vulnerability fixes (maintenance release) yourself; we will do it, notifying you in advance about the timing and availability of the databases.
The time it takes to upgrade is determined by the structure and volume of data, as well as the configuration of your cluster. On average, a version upgrade takes about 1 hour.
When you upgrade an Elasticsearch cluster, you can still read data from or write data to the cluster, but you cannot make other changes.
We recommend that you perform version upgrades after hours or during times of minimal load on the cluster.
From Elasticsearch's point of view, a document is a set of fields, where each field is a pair of "key": "value". An index stores the data of these fields in the document in an optimized form to enable fast search on the fields in the document.
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.