Learn AI

AI Deep Dive

Tag: graph

  • Implementing Fraud Detection and Prevention Agentic AI on Azure – Detailed

    Implementing Fraud Detection and Prevention Agentic AI on Azure – Detailed Implementing Fraud Detection and Prevention Agentic AI on Azure – Detailed This document provides a comprehensive outline for implementing a Fraud Detection and Prevention Agentic AI system on Microsoft Azure. The objective is to build an intelligent agent capable of autonomously analyzing data, making Read more

  • Implementing Fraud Detection and Prevention Agentic AI on AWS – Detailed

    Implementing Fraud Detection and Prevention Agentic AI on AWS – Detailed This document provides a comprehensive outline for implementing a Fraud Detection and Prevention Agentic AI system on Amazon Web Services (AWS). The goal is to create an intelligent agent capable of autonomously analyzing data, making decisions about potential fraud, and continuously learning and adapting Read more

  • The Saga Pattern in Detail

    The Saga Pattern in Detail The Saga Pattern in Detail The Saga pattern is a design pattern used to manage distributed transactions across a sequence of local transactions. In a microservices architecture, where each service has its own database, traditional ACID (Atomicity, Consistency, Isolation, Durability) transactions spanning multiple services are often difficult or impossible to Read more

  • Fixing CPU Spike Issues in Kafka

    Fixing CPU Spike Issues in Kafka 1. Monitoring CPU Usage: The first step is to effectively monitor the CPU utilization of your Kafka brokers. Key metrics to watch include: System CPU Utilization: The overall CPU usage of the server. User CPU Utilization: The CPU time spent running user-level code (the Kafka broker process itself). I/O Read more

  • Large-scale RDBMS to Neo4j Migration with Apache Spark

    Large-scale RDBMS to Neo4j Migration with Apache Spark Large-scale RDBMS to Neo4j Migration with Apache Spark This document outlines how to perform a large-scale data migration from an RDBMS to Neo4j using Apache Spark. Spark’s distributed computing capabilities enable efficient processing of massive datasets, making it ideal for this task. 1. Understanding the Problem Traditional Read more

  • Sample project: Migrating E-commerce Data to a Graph Database

    Migrating E-commerce Data to a Graph Database Migrating E-commerce Data to a Graph Database This document outlines the process of migrating data from a relational database (RDBMS) to a graph database, using an e-commerce scenario as an example. We’ll cover the key steps involved, from understanding the RDBMS schema to designing the graph model and Read more

  • Advanced RDBMS to Graph Database Loading and Validation

    Advanced RDBMS to Graph Database Loading Advanced Tips for Loading RDBMS Data into Graph Databases This document provides advanced strategies for efficiently transferring data from relational database management systems (RDBMS) to graph databases, such as Neo4j. It covers techniques beyond basic data loading, focusing on performance, data integrity, and schema optimization. 1. Understanding the Challenges Read more

  • Ingesting data from RDBMS to Graph Database

    Advanced RDBMS to Graph Database Loading Advanced Tips for Loading RDBMS Data into Graph Databases This document provides advanced strategies for efficiently transferring data from relational database management systems (RDBMS) to graph databases, such as Neo4j. It covers techniques beyond basic data loading, focusing on performance, data integrity, and schema optimization. 1. Understanding the Challenges Read more

  • Advanced Neo4j Tips

    Advanced Neo4j Tips Advanced Neo4j Tips This document provides advanced tips for optimizing your Neo4j graph database for performance, scalability, and efficient data management. It goes beyond the basics to help you leverage Neo4j’s full potential. Schema Design A well-designed schema is the foundation of a high-performance graph database. It dictates how your data is Read more

  • Implementing Graph-Based Retrieval Augmented Generation

    Implementing Graph-Based Retrieval Augmented Generation Implementing Graph-Based Retrieval Augmented Generation This document outlines the implementation of a system that combines the power of Large Language Models (LLMs) with structured knowledge from a graph database to perform advanced question answering. This approach, known as Graph-Based Retrieval Augmented Generation (RAG), allows us to answer complex queries that Read more