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@oyu.live: OYU Talent x Kaspi.kz | Конкурс   Команда OYU и @kaspi.kz рады объявить конкурс для молодых исполнителей! Мы приглашаем начинающих музыкантов любых жанров и самобытных талантов со всей страны проявить себя и поделиться своим творчеством. Как принять участие: 1. Запишите видео музыкального выступления своей авторской песни длительностью не более 1 минуты. 2. Опубликуйте работу на своей странице Instagram (аккаунт должен быть открытым) с хэштегом #OYUTalentxKaspi 3. Укажите в описании имя исполнителя/название коллектива, ваш город и почему хотели бы победить (2-3 предложения). 4. Дата публикации видео должна быть после объявления конкурса. Подача заявок принимается с 28 февраля до 14 марта включительно. Команда OYU совместно с @moldanazar проведут отбор ТОП-5 участников, отличающихся своими музыкальными навыками, харизмой и оригинальностью. Результаты конкурса будут объявлены на нашей странице 17 марта. Победители  примут участие в съемке восьмого сезона OYU Live при поддержке генерального партнера Kaspi.kz    #OYUTalentхKaspi @Алмаз Мержакыпов #oyulive

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Wednesday 05 March 2025 12:47:05 GMT
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suiindikkulimzhan
Suiindik Kulimzhan :
Сырға ма брат анау?
2025-03-08 02:25:51
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alisher.0322
ARYSTAN :
Наконец-то 🔥
2025-03-06 15:55:23
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anuarwrites
Anuar :
жазушыларға бірдеңке жасаңдаршы 😭😭😭
2025-03-06 00:04:46
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Nem ismeri a “halkan” szót😂 #comedy #couplecomedy
Nem ismeri a “halkan” szót😂 #comedy #couplecomedy
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así es 😊😊
así es 😊😊


Types of Databases: A Technological Overview Databases are the foundation of modern data management, storing and organizing information in a structured way. The type of database chosen depends on the specific needs of an application or organization. Here's a breakdown of the most common types: 1. Relational Databases * Structure: Data is organized into tables, with each table containing rows (records) and columns (fields). Relationships between tables are defined using primary and foreign keys. * Examples: MySQL, PostgreSQL, Oracle, SQL Server * Best for: Applications that require complex data relationships and queries, such as financial systems, e-commerce platforms, and customer relationship management (CRM) systems. 2. NoSQL Databases * Structure: Data is stored in a more flexible format, often as key-value pairs, documents, or graphs. NoSQL databases are designed to scale horizontally and handle large volumes of unstructured data. * Examples: MongoDB, Cassandra, Redis, Neo4j * Best for: Applications that deal with large datasets, real-time analytics, and rapidly changing data structures. 3. Object-Oriented Databases * Structure: Data is stored as objects, which can contain both data and methods (functions). This makes it easier to model complex data structures and relationships. * Examples: ObjectStore, Objectivity/DB * Best for: Applications that require complex object-oriented modeling, such as geographic information systems (GIS) and CAD software. 4. Graph Databases * Structure: Data is represented as nodes (entities) and edges (relationships) in a graph structure. This is ideal for modeling networks and relationships between entities. * Examples: Neo4j, ArangoDB * Best for: Applications that involve social networks, recommendation systems, and fraud detection. 5. Time Series Databases * Structure: Designed specifically to store and analyze time-stamped data, such as sensor readings, financial data, and IoT data. * Examples: InfluxDB, TimescaleDB * Best for: Applications that require real-time monitoring, analytics, and forecasting. 6. Document Databases * Structure: Data is stored as documents, which can be hierarchical or nested. This makes them well-suited for semi-structured data. * Examples: MongoDB, Couchbase * Best for: Applications that deal with JSON or XML data, such as content management systems and web applications. 7. In-Memory Databases * Structure: Data is stored entirely in memory, providing extremely fast read and write performance. * Examples: Redis, Memcached * Best for: Applications that require low-latency data access, such as caching, session management, and real-time analytics. The choice of database type depends on factors such as the nature of the data, the required performance, scalability, and the specific needs of the application. In many cases, a combination of different database types may be used to optimize data management. #data #databases
Types of Databases: A Technological Overview Databases are the foundation of modern data management, storing and organizing information in a structured way. The type of database chosen depends on the specific needs of an application or organization. Here's a breakdown of the most common types: 1. Relational Databases * Structure: Data is organized into tables, with each table containing rows (records) and columns (fields). Relationships between tables are defined using primary and foreign keys. * Examples: MySQL, PostgreSQL, Oracle, SQL Server * Best for: Applications that require complex data relationships and queries, such as financial systems, e-commerce platforms, and customer relationship management (CRM) systems. 2. NoSQL Databases * Structure: Data is stored in a more flexible format, often as key-value pairs, documents, or graphs. NoSQL databases are designed to scale horizontally and handle large volumes of unstructured data. * Examples: MongoDB, Cassandra, Redis, Neo4j * Best for: Applications that deal with large datasets, real-time analytics, and rapidly changing data structures. 3. Object-Oriented Databases * Structure: Data is stored as objects, which can contain both data and methods (functions). This makes it easier to model complex data structures and relationships. * Examples: ObjectStore, Objectivity/DB * Best for: Applications that require complex object-oriented modeling, such as geographic information systems (GIS) and CAD software. 4. Graph Databases * Structure: Data is represented as nodes (entities) and edges (relationships) in a graph structure. This is ideal for modeling networks and relationships between entities. * Examples: Neo4j, ArangoDB * Best for: Applications that involve social networks, recommendation systems, and fraud detection. 5. Time Series Databases * Structure: Designed specifically to store and analyze time-stamped data, such as sensor readings, financial data, and IoT data. * Examples: InfluxDB, TimescaleDB * Best for: Applications that require real-time monitoring, analytics, and forecasting. 6. Document Databases * Structure: Data is stored as documents, which can be hierarchical or nested. This makes them well-suited for semi-structured data. * Examples: MongoDB, Couchbase * Best for: Applications that deal with JSON or XML data, such as content management systems and web applications. 7. In-Memory Databases * Structure: Data is stored entirely in memory, providing extremely fast read and write performance. * Examples: Redis, Memcached * Best for: Applications that require low-latency data access, such as caching, session management, and real-time analytics. The choice of database type depends on factors such as the nature of the data, the required performance, scalability, and the specific needs of the application. In many cases, a combination of different database types may be used to optimize data management. #data #databases
Tiger-1☠️🔥#fypシ #tiger #tigerking #fyp #fury
Tiger-1☠️🔥#fypシ #tiger #tigerking #fyp #fury

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