Choosing the Right Database: Vector vs. NoSQL

Vector databases are designed for handling vector data and are optimized for data parallelism, making them ideal for large datasets that require high-performance computing. They are commonly used in fields such as data analytics, machine learning, and scientific computing. On the other hand, NoSQL databases are non-relational databases that can handle large volumes of unstructured data and are known for their scalability and flexible schemas. They are best suited for situations where data doesn't fit neatly into a table or when the data structure may change over time. NoSQL databases are often used in big data and real-time web applications.
VECTOR DB FEATURES
VECTOR DB FEATURES
        
WHAT ARE VECTORS
WHAT ARE VECTORS
        


Database Type Description When to Use
Vector Databases Vector databases are designed to handle vector data, which are mathematical constructs that have both magnitude and direction. They are used in fields such as physics, engineering, and computer graphics. Vector databases are optimized for data parallelism, allowing them to process large amounts of data simultaneously. Vector databases are best used when dealing with large datasets that require high-performance computing. They are particularly useful in fields such as data analytics, machine learning, and scientific computing where the ability to perform complex calculations on large datasets is crucial.
NoSQL Databases NoSQL databases are non-relational databases designed to handle large volumes of data that may not be structured tabularly. They are known for their ability to scale out, and they use flexible schemas which can be document-oriented, column-oriented, graph-based or key-value pairs. NoSQL databases are best used when dealing with large amounts of data that doesn't fit neatly into a table, or when the data structure may change over time. They are often used in big data and real-time web applications, where speed and scalability are more important than complex transactions and consistency.



Challenges-frequent-update    Criteria-to-select-vector-db    Crud Operations For Vector DB    Uses-of-vector-db    Vector-db-applications    Vector-db-crud    Vector-db-dimensions    Vector-db-features    Vector-db-impact-invarious-fi    Vector-db-rag   

Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

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    LLM and AI based Website generation
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