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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

Data Lineage: Case Studies of Data-Driven Businesses

Parth Shukla
Parth Shukla Last Updated : 29 Nov, 2022
5 min read

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

Introduction

Data lineage is the process of analyzing the path of the data and how it is involved in different methods with time. Many businesses and companies use it to get an idea of the source, data pathway, and how the data is being used. It can help organizations gain insight from the data to plan for future steps and use the data for better product or service performance.

Data lineage

In this article, we will discuss 3 case studies where data-driven companies like Netflix, Slack, and Postman implemented data lineage and benefitted from that. Here we will also discuss their process of it and its technique they applied while implementing and using it.

Data Lineage Case Studies

Some data-driven businesses like Netflix, Slack, UBS, Postman, and Airbnb are convinced of the benefits of data lineage and are now using it and reaping returns. Let us discuss the data linkage process in these companies and how they get benefitted from it.

Case 1: Improved data infrastructure reliability and efficiency at Netflix

 

netflix | Data lineage

Netflix has been convinced of the benefits of the data lineage and has implemented it. At the project’s inception stage, they defined design goals to help guide the architecture and development work to deliver a complete, accurate, reliable, and scalable lineage system mapping Netflix’s diverse data landscape. A few of these principles are:

  • Ensure data integrity
  • Enable seamless integration
  • Design a flexible data model

Based on a standard data model at the entity level, they have built a generic relationship model that describes the dependencies between any pair of entities. Using this approach, they can make a unified data model and the repository to deliver the proper leverage to enable multiple use cases such as data discovery, SLA service, and Data Efficiency.

Case 2: Easy operational maintenance and better execution of data programs at Slack

 

slack | Data lineage

Slack has been convinced of the benefits of data lineage, and hence they have also invested in the same. Slack states that as datasets become more complex and the number of contributors grows, it becomes more and more challenging to understand the relationships between different data sources.

To make it easier for folks to use their lineage data, they have produced a flattened version of tier tables and stored it in Hive. The flattened table allows folks to query lineage data in our data warehouse and also makes queries easier to write/run for typical use cases.

Also, with the help of data lineage, they have worked on a notifications system. They have built notification tooling on their internal Data Portal to allow their data consumers to use lineage information and notify downstream consumers. There is a notify button, using which the dataset owners can get information.

Case 3: Moving beyond data discovery at Postman

 

Postman has also fixed a missing layer in their data layer. Postman’s data system was pretty simple. They had a set of data tables, and information about those tables lived in the heads of Their early data team members. This worked when the company and its data were small but needed help to keep up as it started to grow exponentially.

Postman currently has hundreds of team members distributed across four continents and more than 17 million users from 500,000 companies using their API platform.

Postman Co-founder and CTO Ankit Sobti wanted to ensure that data was democratized. He said that it is a challenging task for a data engineering team to gain insights from data at any given time in the day. He believed that everyone in the company should be able to access the data and gain insights. This became very tedious in 2020 when Potman became fully online due to the COVID pandemic.

The data team decided to take on Postman’s data system as a project to address this issue. Their main goal was to make Postman’s data easier to access and understand, both for new hires within the data team and for people across the company with the help of data lineage.

They have used data lineage to know where the data comes from and how it is connected to other layers. Data lineage helped them understand the data’s connectivity and daily bugs and errors occurring on the system. It helped them solve issues quicker; Without asking a doubt, the slack team could solve the problem by just looking at data lineage. They are also planning to take further steps in data lineage to make their data management more accessible and quicker.

When Data Lineage is a No-brainer (of No Use) For Some Organizations

Data lineage is proven the best fit solution for most organizations working with data and data management. Still, there are some cases where it is proven to be a no-brainer for organizations.

Some organizations store a large amount of data and work with many data sources and storage. Data lineage can prove a no-brainer for such an organization, as it needs to provide the best reliable information for such data.

Data lineage provides information about the data sources and the entire lifecycle of the data; the data’s design lineage can help one get an idea about the data’s head and consumption. However, it is helpful for architects to understand the implementation of how data flows. However, subject matter experts in the business that wish to audit the data processing can find it complex to navigate.

Business lineage provides simplified views on analyzing business types over the design lineage. A business lineage report may only show the significant systems or may eliminate the systems and job structures only to show the transformation.

So this is how the data lineage is designed to show things quickly and easily, but not to search the items. Let us suppose that the organization works with a large amount of data or discrete data sources that vary frequently. It will not be able to find the desired information from the data as it can show the flowchart or lifecycle of the data. Still, the results from it will only be reliable for a small amount of data or varying data. Hence, it is a proven no-brainer for organizations working with large volumes and ranging data.

Conclusion

In this article, we discussed some case studies of the data-driven companies that implemented and used the data lineage and its application and benefitted from that. We saw data-driven companies like Netflix, Slack, and Postman, which used the concept in their database, which returned positive results. Knowledge about these companies and their data lineage process will help one understand how colossal data companies are using this and also help one answer the questions asked in data engineering interviews very efficiently.

Some Key Takeaways from this article are:

1. Today, most data-driven companies use data lineage for better data governance and handling.

2. Companies with data sources can implement data lineage very efficiently and help them get more idea about the data being used in no time.

3. It is a no-brainer or not so useful for companies with a small amount of generation of data or startups with lighter databases.

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

Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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