Database

This page explains the concept of database, vector databases, and multiple databases.

Databases are fundamental to modern computing, enabling efficient data management and retrieval, which is crucial for everything from business operations to scientific research.

A database is an organized collection of data that you can electronically access, store and manage. Databases allow you to retrieve large amounts of information efficiently. They are essential for various applications, from simple data storage to complex data analysis and processing.

Here are the key concepts associated with databases:

  • Data: The raw information that is stored in the database. This can include various formats, such as text, numbers, and images.

  • Database Management System (DBMS): Software platform that facilitates the interaction between the database, users, and applications to capture and analyze data (for example, MySQL).

  • Tables: Relational databases organize data into tables. Each table consists of rows and columns, where a row is a record, and a column is a field within the record.

  • Queries: Requests made to the database to retrieve or edit data. SQL (Structured Query Language) is commonly used for writing queries in relational databases.

  • Indexes: Structures that speed up data retrieval operations on database tables.

  • Transactions: A sequence of actions executed as a single logical unit of work. Transactions ensure data integrity and consistency.

Types of databases

There are multiple types of databases, each uniquely suited to exact use cases and optimized for handling different kinds of data and queries:

  • Relational databases: Use structured query language (SQL) to define, manage and manipulate data organized into tables.

  • NoSQL databases: Designed for unstructured data and can handle large volumes of diverse data types.

  • Time-Series databases: Optimized to handle time-stamped data, they're ideal for applications like monitoring and IoT.

  • Graph databases: Represent and store data using graph structures, which consist of nodes (entities), edges (relationships), and properties (attributes).

  • Vector databases: Optimized for high-dimensional data and are particularly useful in AI applications such as Machine Learning (ML) and Natural Language Processing (NLP).

Using multiple types of databases allows organizations to leverage the strengths of each type to handle different data and query requirements more effectively. For instance, a company might use a relational database for transactional data, a time-series database for monitoring system performance, and a vector database for AI-driven search and recommendation systems.

Vector databases

A vector database is a database specifically designed to efficiently store, manage, and retrieve vector data. Vectors are ordered sets of numerical values that represent various types of data, such as spatial coordinates, feature attributes, or embeddings used in machine learning.

Key features of vector databases:

  • High-dimensional data: Vector databases handle data represented as vectors (both sparse and dense).

  • Similarity search: Vector databases excel at finding similar items based on vector similarity, which is crucial for applications like recommendation systems, image and video search, and natural language processing.

  • Efficient storage and retrieval: Optimized for low-latency queries, making them suitable for real-time applications.

Multiple Databases

In the context of vector databases such as KDB.AI, having multiple databases typically means that the system can manage and store data across several distinct databases, each optimized for different types of data or use cases. For example, you might have databases like “market_data”, “trades”, and “hr_data”. Each of these databases can contain several tables.

Here are a few key points:

  • Database structure: Each table belongs to a specific database. Think of databases as individual containers that hold multiple tables.

  • Organization: This structure helps in organizing tables logically, so you don’t end up with a clutter of 100 tables in a single database.

  • Table naming: You can have tables with the same name in different databases. For example, both market_data and trades can have a table named transactions without any conflict.

Next steps

Now that you’re familiar with databases: