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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

What is big data and how is big data architecture designed?

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
5 min read

Consider following fact:

[stextbox id=”section”]Facebook currently has more than 1 Billion active users every month[/stextbox]

Let us spend a few seconds to think what information Facebook typically stores about its users. Some of this is:

  • Basic demographics (e.g. date of birth, gender, current location, past location, College)
  • Pact activities & updates of users (your photos, comments, likes, applications you have used, games you played, messages, chats etc.)
  • Your social network (Your friends, their circles, how you are related etc.)
  • Interests of users (books read, movies watched, places etc.)

big-data

Using this and a lot of other information (e.g. what did a user click, read and how much time did he spend on it), Facebook then performs following in real time:

  • Recommend people you might know & mutual connections with them
  • Use your current and all past activities to understand what you are interested in
  • Target you with updates, activities and advertisements you might be most interested in

Over and above these, there are near time activities (refreshed in batches and not in real time) like number of people talking about a page, people reached in a week.

Now, imagine the kind of data infrastructure required to run Facebook, the size of its data center, the processing power required to suffice its user requirements.The magnitude could be exciting or scary, depending on how you look at it.

Following infographic from IBM brings out the magnitude of data requirement / processing for some similar Organizations:

big-data_ibm

This kind of size and scale was unheard by any analyst till a few years back and the data infrastructure some of these Organizations had invested in was not prepared to handle this scale. This is typically referred as Big data problem.

[stextbox id=”section”]So, what is big data?[/stextbox]

Big Data is data that is too large, complex and dynamic for any conventional data tools to capture, store, manage and analyze. Traditional tools were designed with a scale in mind. For example, when an Organization would want to invest in a Business Intelligence solution, the implementation partner would come in, study the business requirements and then would create a solution to cater to these requirements.

If the requirement of this Organization increases over time or if it wants to run more granular analysis, it had to re-invest in data infrastructure. The cost of resources involved in scaling up the resources typically used to increase exponentially. Further, there would be a limitation on the size you could scale up to (e.g. size of machine, CPU, RAM etc.). These traditional systems would not be able to support the scale required by some of the internet companies.

[stextbox id=”section”]How is big data different from traditional data? [/stextbox]

Fortunately or unfortunately, there is no size / parametric cut off to decide whether the data is “big data” or not. Big data is typically characterized basis what is popularly known as 3 Vs:

  • Volume – Today, there are organizations producing terabytes of data in a day. With increasing data, you will need to leave some data without analyzing, if you want to use traditional tools. As data size increases further, you will leave more and more data without analysis. This means leaving value on table. You have all the information about what customer is doing and saying, but you are unable to comprehend! – a sure sign that you are dealing with bigger data than what your system supports.
  • Variety – While volume is just the start, variety is what makes it really difficult for traditional tools. Traditional tools work best with structured data. They require data is a particular structure and format to make sense out of it. However, the flood of data coming from emails, customer comments, social media forums, customer journey on website and call-centers are unstructured in nature or semi-structured at best.
  • Velocity – The pace at which data gets generated is as critical as the other two factors. The speed with which a company can analyze data would eventually become competitive advantage for them. It is their speed of analysis, which allows Google to predict the location of flu patients almost real time. So, if you are unable to analyze data at a speed faster than its inflow, you might need a big data solution.

Individually, each of these Vs can still be worked around with help of traditional solutions. For example, if most of your data is structured, you can still get 80% – 90% of business value through traditional tools. However, if you face a challenge with all three Vs – you know you are dealing with “big data”.

three V

[stextbox id=”section”]When do you need a big data solution?[/stextbox]

While the 3 Vs will tell you whether you are dealing with “big data” or not, you may or may not need a big data solution depending on your need. Following are scenarios where big data solutions are inherently more suitable:

  • When you are dealing with huge semi-structured or unstructured data from multiple sources
  • You need to analyze all of your data and can not work with sampling them.
  • The process is iterative in nature (e.g. Searches on Google, Facebook graph search)

[stextbox id=”section”]How do big data solution work?[/stextbox]

While the limitations of traditional solutions are clear, how do big data solutions solve them? Big data solutions work on a fundamentally different architecture which is built on following characteristics (illustrative below):

  • Data Distribution and parallel processing: Big data solutions work on distributed storage and parallel processing. Simply put, files are broken into multiple small blocks and stored in different units (called racks). Then, the processing happens in parallel on these blocks and results are merged back together. The first part of the operation is typically called a Distributed File System (DFS) while the second part is called Mapreduce.
  • Tolerance to failure: By nature of their design, big data solution have built in redundancy. For example, Hadoop creates 3 copies of each data block across at least 2 racks. So, even if an entire rack fails or is unavailable, the solution continues to work. Why is this built in? This feature enables big data solutions to scale up even on cheap commodity hardware rather than expensive SAN disks.
  • Scalability & Flexibility: This is the genesis of entire big data solution paradigm. You can easily add or remove racks from the cluster without worrying about the size for which this solution was designed for.
  • Cost effectiveness: Because of use of commodity hardware, the cost of creating this infrastructure is far lower than buying expensive servers with failure resistant disks (e.g. SAN)

HDFS illustrative_v2

[stextbox id=”section”]Finally, what if all of this was on cloud?[/stextbox]

While developing big data architecture is cost effective, finding right resources is difficult which increases the cost of implementation.

Imagine a situation where all of your IT / infrastructure worries are also taken care by a cloud service provider. You focus on performing analysis and delivering results to business rather than arranging racks and worrying about extent of their usage.

All you have to do is pay as per your usage. Today, there are such end to end solutions available in market, where you can not only store your data on cloud, but also query and analyze it over the cloud. You can query terabytes of data in matter of seconds and leave all the worry about these infrastructure for some one else!

While I have provided an overview of big data solutions, this by no means covers the entire spectrum. The purpose is to start the journey and be ready for the revolution which is on it’s way.

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Big databig data solutionBusiness AnalyticsHadoopMapreduceweb analytics
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Ruchi Mahajan
Ruchi Mahajan

Superb artical, has made my job a lot more easier.

123

Madhusudan
Madhusudan

Nice article, Would like to know your opinion on the capability of SAP HANA to analyse unstructured data. Probably should be a better solution considering SAP's expertise in data warehousing and ERP.

123

Kunal Jain
Kunal Jain

Madhusudan, The whole gamut of big data is evolving and changing very fast. The entire landscape changes every 6 - 9 months. Further, the right way to compare any solution should be based on need of the business. Needs of a startup would be very different from that of an evolved business. Hence, there is no one size fits all comparison which any one can perform. Independently, SAP HANA is definitely an exciting solution from SAP and has inherent capabilities to deal with unstructured data. I am not sure whether data warehousing knowledge & expertise of any Organization would aid them or become a baggage in new era of big data tools. Hence, would not rely on these factors to reach a conclusion.

123
vikas sharma
vikas sharma

Great article..Thanks for this..

123

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