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Tag: monitoring

  • Top 20 Databricks Interview Questions

    Preparing for a Databricks interview? This article compiles 20 key questions covering various aspects of the platform, designed to help you showcase your knowledge and skills. 1. What is Databricks? Answer: Databricks is a unified analytics platform built on top of Apache Spark. It provides a collaborative environment for data engineering, data science, and machine Read more

  • Autonomous Content Creation for Social Media Marketing using Agentic AI

    Here we implement agentic AI use case focusing on a creative and dynamic domain: Autonomous Content Creation for Social Media Marketing. Use Case: A marketing agency wants to automate the process of creating engaging content for various social media platforms for their clients. Instead of relying solely on human content creators, an agentic AI can Read more

  • Agentic AI Tools

    Agentic AI refers to a type of artificial intelligence system that can operate autonomously to achieve specific goals. Unlike traditional AI, which typically follows pre-programmed instructions, agentic AI can perceive its environment, reason about complex situations, make decisions, and take actions with limited or no direct human intervention. These systems often leverage large language models Read more

  • Comparing various Time Series Databases

    A Time Series Database (TSDB) is a type of database specifically designed to handle sequences of data points indexed by time. This is in contrast to traditional relational databases that are optimized for transactional data and may not efficiently handle the unique characteristics of time-stamped data. Here’s a comparison of key aspects of Time Series Read more

  • The Monolith to Microservices Journey: Empowered by AI

    The transition from a monolithic application architecture to a microservices architecture, offers significant advantages. However, it can also be a complex and resource-intensive undertaking. The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers powerful tools and techniques to streamline, automate, and optimize various stages of this journey, making it more efficient, less risky, Read more

  • The Monolith to Microservices Journey: A Phased Approach to Architectural Evolution

    The transition from a monolithic application architecture to a microservices architecture is a significant undertaking, often driven by the desire for increased agility, scalability, resilience, and maintainability. A monolith, with its tightly coupled components, can become a bottleneck to innovation and growth. Microservices, on the other hand, offer a decentralized approach where independent services communicate Read more

  • Kafka Disk I/O Tuning Guide

    Disk I/O is a critical bottleneck for Kafka performance. Kafka relies heavily on the file system for storing and retrieving messages, and inefficient disk I/O can lead to increased latency, reduced throughput, and overall system degradation. Here’s a guide to help you tune Kafka for optimal disk I/O performance: 1. Understanding Kafka’s Disk I/O Patterns Read more

  • Apache Spark

    Let’s illustrate Apache Spark with a classic “word count” example using PySpark (the Python API for Spark). This example demonstrates the fundamental concepts of distributed data processing with Spark. Scenario: You have a large text file (or multiple files) and you want to count the occurrences of each unique word in the file(s). Steps: from Read more

  • MLOps pipeline

    While a full-fledged MLOps pipeline involves integrating various tools and platforms, here are some illustrative code snippets demonstrating key MLOps concepts using popular Python libraries and tools. These examples focus on individual stages and can be combined to build a more comprehensive pipeline. 1. Data Versioning with DVC (Data Version Control): This isn’t Python code, Read more

  • Workflow of MLOps

    The workflow of MLOps is an iterative and cyclical process that encompasses the entire lifecycle of a machine learning model, from initial ideation to ongoing monitoring and maintenance in production. While specific implementations can vary, here’s a common and comprehensive workflow: Phase 1: Business Understanding & Problem Definition Phase 2: Data Engineering & Preparation Phase Read more