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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

All About Data Pipeline and Its Components

Chetan
Chetan Last Updated : 25 Jul, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

With the development of data-driven applications, the complexity of integrating data from multiple simple decision-making sources is often considered a significant challenge. Although data forms the basis for effective and efficient analysis, large-scale data processing requires complete data-driven import and processing techniques in real-time. To help with this, data pipelines help different Service Providers to compile and analyze large data sets by defining a series of functions that convert raw data into valuable data. This article explains how the data pipeline helps process large amounts of data, different architectural options, and best practices for maximizing profits.
data pipeline

 

What is a Data Pipeline?

The data pipeline is a set of functions, tools, and techniques to process raw data. Pipes include a number of related processes to link the series, which allow the transmission of data from its source to the destination for storage and analysis. Once the data has been imported, it is taken for each of these steps, in which the single-step output results next step input.
In modern technology, extensive data applications rely on a microservice-based model, allowing loads of monolithic functions to be divided into modular sections with more minor codes. This promotes data flow across multiple systems, with data generated by one service into one or more service inputs (applications). In addition, a well-designed data pipeline helps to manage the variability, volume, and speed of data in these applications.

Advantages of Data Pipeline

The main benefits of implementing a well-designed data pipeline include:

IT Service Development

When building data processing applications, the data pipeline allows for duplicate patterns – single pipelines can be reused and used in the flow of new data, which increasingly helps to evaluate IT infrastructure. Repeated patterns also incorporate protection from construction from the ground up, allowing for the enforcement of good reusable security operations as the application grows.

Increase Application Visibility

Data pipelines help expand shared understanding of how data flows in the system and visibility of tools and techniques used. Data engineers can also set the telemetry of data flow throughout the pipeline, allowing for continuous monitoring of processing performance.

Improved Production
With a shared understanding of data processing operations, data teams can better organize new data sources and streams, reducing the time and cost of integrating new streams. Giving statistical groups complete visibility of data flow also enables them to extract accurate data, thus helping to improve data quality.

Important Parts of the Data Pipeline

Data pipelines force data development by moving it from one system to another, usually through separate storage usage. These pipelines allow you to analyze data from different sources by converting it into a compact format. This change consists of various processes and components that handle different data functions.
data pipeline process

 

Data Pipeline Processes

Although different operating conditions require different workflows, the following are some common data pipeline procedures:

Data pipeline

Although the complexity of a data pipeline varies based on usage conditions, the amount of data to be extracted, and the frequency of data processing, here are the most common categories of data pipeline:

Export / Import

This category includes the input of data from its source, well known as the source. Data entry points include IoT sensors, data processing applications, online processing applications, social media input forms, social data sets, APIs, etc. Data pipelines enable you to extract information from storage systems, like data pools and storage areas.

Transformation

This section covers the changes made to the data as it moves from one system to another. The data is modified to ensure it matches the format supported by the target system, such as the analytics application.

Processing

This section covers all the functions involved in importing, converting, and uploading data to the output side. Other data processing tasks include merging, sorting, merging, and adding.

Syncing

This process ensures data synchronization across all data sources and pipeline endpoints. The platform actually involves updating the data libraries to keep the data consistent throughout the life cycle of the pipeline.

Data Pipeline Options

The three main design options for building data processing infrastructure for large data pipelines include stream processing, component processing, and lambda processing.

Broadcast Processing

Stream processing involves adding data to a continuous stream and processing data into segments. The purpose of this formulation is a fast-tracking process primarily aimed at real-time data processing in terms of usage conditions such as fraud detection, logging and compilation monitoring, and user behavior analysis.

Batch Processing

For batch processing, data is collected over time and later sent for cluster processing. In contrast to streaming processing, batch processing is a time-consuming process and is designed for large amounts of unwanted data in real-time. Collection processing pipelines are commonly used for applications such as customer orders, payment, and billing.

Lambda Processing

Lambda processing is a hybrid data processing model that combines a real-time streaming pipeline and big data processing. This model divides the pipeline into three layers: clusters, clusters, and feeds.
In this model, data is continuously imported and integrated into both collection and distribution layers. The batch layer incorporates batch views and handles the main database. Stream layer handles unloaded data in big view as big performance is time-consuming. The feed layer creates an indicator of batch view so that from time to time it is asked about low latency.

Key Components of Data Pipeline

  1. Data serialization– Data editing defines common formats that make data easily and accessible and responsible for converting data objects into byte streams.
  2. Event structures – These structures identify actions and processes that lead to change in the system. Events are included for analysis and processing to assist in app-based decisions and user behavior.
  3. Workflow management tools – These tools help to organize activities within the pipeline based on directional dependence. These tools also facilitate the automation, monitoring, and management of piping processes.
  4. Message bus – Message buses are part of an important pipeline, which allows data interchange between systems and ensures the compatibility of different databases.
  5. Data Persistence – A backup system in which data is noted and read. These systems allow the integration of different data sources by enabling a data access protocol in different data formats.
dataflow

 

Best Practices for Using a Pipeline

In order to build efficient pipelines, recommended team processes include the simultaneous performance of tasks, the use of layered tools with built-in connections, investing in appropriate data processing tools, and enforcing catalog data processing and ownership.
  1. Enable the Performance of Similar Tasks
    Multiple big data applications are used to carry out multiple data analysis tasks at a time. A modern data pipeline should be built with elastic, big, and shared patterns that can handle multiple data flows at a time. A well-designed pipeline should load and process data from all data flows, which DataOps teams can analyze to use.
  2. Use Extensible Tools for Internal Connection
    Modern pipelines are built on a number of frameworks and tools that connect and interact. Inbuilt integration tools should be used to reduce time, labor, and cost to build connections between the various systems in the pipeline.
  3. Invest in Proper Data Arguments
    Because inconsistencies often lead to poor data quality, it is recommended that the pipeline use appropriate data dismissal tools to resolve disagreements in different data companies. With clean data, DataOps teams can gather accurate data to make effective decisions.
  4. Enable Data Entry Installation and Identity
    It is important to maintain the log of the data source, the business process that owns the database, and the user or process that accesses those databases. This provides complete visibility of data sets to use, which strengthens data quality reliability and authenticity.

Conclusion

Gartner predicts that the value of automation will continue to rise so much that, “By 2025, more than 90% of businesses will have an automation designer.” In addition, Gartner stated that “by 2024, organizations will reduce operating costs by 30% by combining hyper-automation technology with redesigned operating systems.”

In the end, let’s recap what we have learned, from What is 

  • Data Pipeline to how it is used in industry to get the continuous flow of data, 
  • Data Pipeline Infrastructure which includes Import/Export, Transformation, scanning, and Processing 
  • an advantage like It Service Development, Increase Application visibility & Improved production and key Components, we have seen it all in detail. 

This is all you need in order to get started working and Building a Data Pipeline.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Chetan
Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.

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