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Category: python

  • AI Agent with Short-Term Memory on Azure

    AI Agent with Short-Term Memory on Azure Creating AI agents capable of handling complex tasks and maintaining context requires implementing short-term memory, often referred to as “scratchpad” or working memory. This allows agents to temporarily store and process information relevant to their immediate goals. Microsoft Azure offers a range of services that can be utilized Read more

  • AI Agent with Long-Term Memory on AWS

    AI Agent with Long-Term Memory on AWS Building truly intelligent AI agents requires not only short-term “scratchpad” memory but also robust long-term memory capabilities. Long-term memory allows agents to retain and recall information over extended periods, learn from past experiences, build knowledge, and personalize interactions based on accumulated history. Amazon Web Services (AWS) offers a Read more

  • DynamoDB Index Comparison: GSI vs. LSI

    DynamoDB Index Comparison: GSI vs. LSI DynamoDB Index Comparison: GSI vs. LSI DynamoDB offers two types of secondary indexes to enhance query performance: Global Secondary Indexes (GSIs) and Local Secondary Indexes (LSIs). Here’s a detailed comparison: Key Differences Feature Global Secondary Index (GSI) Local Secondary Index (LSI) Partition and Sort Keys Can have a different Read more

  • Python Examples: CPU-Bound and I/O-Bound Operations

    Examples of CPU-Bound and I/O-Bound Operations Here are some examples of CPU-bound and I/O-bound operations to help you understand the difference: CPU-Bound Operations A CPU-bound operation is one that primarily relies on the processing power of the CPU. The CPU is the bottleneck in these operations, and increasing the CPU’s performance will directly improve the Read more

  • Python Multiprocessing samples in API Backend

    Python Multiprocessing in API Backend Multiprocessing in Python can significantly improve the performance of an API backend, especially for CPU-bound tasks, by leveraging multiple CPU cores. Unlike multithreading, multiprocessing creates separate Python processes, each with its own memory space, effectively bypassing the Global Interpreter Lock (GIL). Understanding Multiprocessing Multiprocessing creates a new process for each Read more

  • Python Multithreading in API Backend

    Python Multithreading in API Backend Python Multithreading in API Backend Multithreading in Python can improve the performance of an API backend by allowing it to handle multiple requests concurrently. This is particularly useful for I/O-bound operations, such as fetching data from external APIs or databases. Understanding the GIL Before diving into the code, it’s crucial Read more

  • Implementing few e-Commerce queries in Spark SQL

    Spark SQL Implementation – E-commerce & Retail (First 5) Implementation # 1. Calculate daily/weekly/monthly sales trends. This query calculates the total sales for each day, week, and month. It assumes you have an orders table with an order_date and a total_amount. — Daily Sales Trend SELECT order_date, SUM(total_amount) AS daily_sales FROM orders GROUP BY order_date 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