Posts

DQ Check for DataFrame - Complete Guide to Data Quality Validation

Image
Introduction to DQ Check- DQ Check (Data Quality Check) is the process of validating data to ensure it is accurate, complete, consistent, and reliable before analysis or machine learning tasks. type of data quality checks with example In data engineering and data science projects, DataFrames (Pandas or Spark) are widely used. Performing DQ checks on DataFrames helps: Detect missing or invalid values Ensure correct data types Identify duplicates Improve ML model accuracy Prevent pipeline failures Why DQ Check is Important? Poor data quality leads to: Wrong business insights Poor ML model performance Data pipeline failures Incorrect reporting A proper DQ check ensures clean, trustworthy, and usable data for analytics and AI models. Common Data Quality Checks for DataFrame 1.Null / Missing Value Check Pandas Example df .isnull () .sum () Spark Example from pyspark .sql .functions import col df .select ([col(c) .isNull () .sum () .alias (c) for c in df .columns ]) .show () P...

InvalidObjectException: ReflectionOperationException During Deserialization – How Upgrading Scala Fixes the Error

Image
  InvalidObjectException: ReflectionOperationException During Deserialization – How Upgrading Scala Fixes the Error When working with Scala applications, especially in distributed systems or serialization-heavy environments, you may encounter the following runtime error: java.io.InvalidObjectException: ReflectionOperationException during deserialization This typically appears when Scala attempts to deserialize an object that was serialized using an incompatible or outdated version of Scala or its reflection APIs. In this article, we explore the root cause of this error and how upgrading the Scala version resolves it. What Causes InvalidObjectException: ReflectionOperationException ? This error occurs during object deserialization when: Reflection APIs change between Scala versions. Serialized data is created under an older Scala version. Scala libraries or dependencies use mismatched binary versions. Internal reflection logic fails due to outdated metadata. Sc...

How to Check if a Header Is Available in a Linux File – A Complete Guide

Image
   How to Check if a Header Is Available in a Linux File – A Complete Guide When working in Linux environments, developers and system administrators often need to verify whether a specific header , field name, or column exists inside a file. This is especially common when dealing with CSV files , log files , configuration files , or any structured data. This guide explains multiple methods to check whether a header is present using simple Linux command-line tools. Why Check for a Header in Linux? Checking for a header is useful when you want to: Validate data files Ensure correct file formats Prevent script failures Perform conditional processing Linux provides multiple commands to check headers efficiently. 1. Using grep (Simple & Fast) grep -q "HeaderName" filename && echo "Header exists" || echo "Header not found" 2. Check Only the First Line head -n 1 filename | grep -q "HeaderName" 3. Using awk for Ex...

Handling Pipelines in Data Science with Jenkins

Image
Handling Pipelines in Data Science with Jenkins  Using Jenkins for Data Science Pipelines Jenkins is a popular open-source automation server that supports  Continuous Integration and Continuous Deployment (CI/CD) . It is highly customizable and can automate various stages of a data science pipeline, including data extraction, transformation, model training, and deployment. Create a Git repository ✔ Store: Dataset Python scripts ML models Jenkinsfile Common Pipeline Stages: Data Extraction Data Cleaning & Transformation Feature Engineering Model Training Model Evaluation Model Deployment Sample Jenkins Pipeline Flow Code Commit → Jenkins Trigger → Data Processing → Model Training → Evaluation → Deployment → Monitoring In this guide, we will explore the  step-by-step pipeline of data science using Jenkins , understand how each stage works, and see how Jenkins simplifies the end-to-end machine learning workflow. Steps to Set Up a Data Sc...

How to Use Structs in C : complete guide

Image
  How to Use Structs in C: Get and Print Structure Variable Values Structs are a powerful feature in C programming that allow you to group variables of different data types under a single name. This makes handling related data more efficient and organized. In this tutorial, we will learn how to create a structure, input its values, and display them with a practical example. This tutorial is beginner-friendly and uses easy-to-understand concepts. What is a Struct in C? A struct (short for structure) is a user-defined data type that groups related variables under one name. These variables, known as members, can have different data types. Structs are widely used in C for organizing complex data and simplifying operations. Key Features of Structs: Enables grouping of variables with different types. Uses a single memory block for all members. Allows easy access to members via the struct's name or pointers. Example: Program to Input and Print Struct Values In this example, we define a st...