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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

An End-to-End Starter Guide on Apache Spark and RDD

Abhishek Jaiswal
Abhishek Jaiswal Last Updated : 08 Jun, 2022
6 min read

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

Apache Spark

Introduction

In this article, we will introduce you to Apache Spark and its role in big data and the way it makes a big data ecosystem we will also explore Resilient Distributed Dataset (RDD) in spark.

As we all have seen the growth of data in today’s world, Growing data helps many companies to get insights into data and this information is used for the growth. Smart assistants like Siri, and Google Home, Alexa use Big Data, Machine learning, and IoT technologies to collect data and answer questions.

Due to the high growth of data-driven technologies, more data is being generated and we need to process petabytes of data. Processing petabytes of data is not easy for a single computer and this task is distributed among various computers and all performed together, this is the idea behind big data.

Processing of Big Data

Processing a high volume of data needs various computers to work parallelly and process it. Since we can’t load a high volume of data on a single computer. This is known as distributed computing. In other words, the processing task is divided and run independently with its computing resources. If any single computer or node gets failed or goes down other nodes take the remaining job.

A cluster is a distributed system ( a collection of multiple nodes) that works together in synchronization on a particular task. The big data ecosystem is made of up many open source tools. for example, Open-Source projects like Hadoop and big data tools like Apache Spark, and Hive complete the big data ecosystem. Since they are free to use and transparent.

Big Data EcoSystem

The big data ecosystem is consist of various open-source to paid tools. all are made for their specific purpose. Big data requires cloud computing, Data processing tools, Database management tools, and business intelligence to programming language.

Categorized tools required

  • Programming Tools
  • Business Intelligence tools
  • Databases (NoSql and SQL )
  • Analytics and visualization
  • Data technologies
  • Cloud Technologies

 

Hadoop Ecosystem

Hadoop Ecosystem is made up of various open-source tools that fall under the Hadoop project. All these components support one another.

  1. Ingest Data( Flume , Sqoop)
  2. Access Data ( Impala, Hue)
  3. Process and Analyze Data (Pig & Hive)
  4. Store Data (HDFS, HBase)

Why Apache Spark?

Big Tech Companies like Tencent, Uber, Netflix, Google, and Alibaba all run Apache spark operations.

Spark is a powerful replacement for Apache Map-Reduce. Spark is more powerful and faster than Hadoop in computation, it is highly accessible and capable of processing a high volume of data in the distributed system.

In-memory computation ( computation in ram instead of the disk) makes Spark run faster than Map-Reduce for distributed data processing.

Top Features of Apache Spark

These are the top features of apache spark that make it widely popular and superior.

1. Fast Processing

Spark processes data 100 times faster in memory and 10 times faster in the Hadoop cluster. Spark has already set the world record for fastest data sorting on disk.

2. Supports various APIs

Spark provides various programming language APIs which means spark applications can be written in any language for example SCALA, JAVA, PYTHON, R.

Spark is written on SCALA natively. Spark core performs large-scale distributed and parallel data processing. Spark Core is fault-tolerant which means if any node goes down processing will not stop.

Apache Spark

SPark also provides many built-in packages for Machine learning tasks.

3. PowerFul libraries

Spark also supports Map-Reduce functions. Spark works on Distributed dataframes and SQL.

ML-lib is a rich machine learning package that comes with spark. Spark Streaming makes real-time data processing and analysis possible.

4. Real-Time Processing

Spark supports also supports conventional MapReduce which is capable of processing data from Hadoop HDFS files. Spark can work on the HDFS cluster without any conflicts.

5. Deployment and Compatibility

Spark is able to run on any cloud notebook without needing many dependencies. Spark can run on Kubernetes, Mesos, Hadoop, and cloud notebooks.

Apache Spark Architecture

Apache Spark Architecture

Any Spark Engine contains 3 high-level components:

  • Spark Driver
  • Cluster Manager
  • Executors

Getting Started with Spark with Python

Spark is natively written in Scala programming language that compiles in java. We can communicate with JVM using py4j. This makes using Python to work on Spark Possible.

Hence we can create Spark applications in Python with ease. In the backend, it will be using py4j.

Objectives

  • Setting Up Pyspark
  • Context and Session
  • Spark RDD
  • Transformation and actions

For Running Spark, you need a python environment it could be any cloud notebook or local python environment, but I highly encourage you to create a data-bricks spark cluster, this is a guide on how to spin spark clusters on data-bricks for free.

PySpark It is a Spark API that lets us create applications in Spark. In this article, we will be using Pyspark to work on Spark.

Installing Required Packages

Setting up the spark in python needs the following libraries.

!pip install pyspark
!pip install findspark

Findspark It adds a startup file to the current IPython profile and creates an environment ready for Spark in Python.

import findspark
findspark.init()

Creating the Spark Context, and Spark Session.

Spark session It is needed for working on Spark dataframes and SQL.

SparkContextIt is the entry point for Spark applications and it also concludes functions to create RDDs(Resilient distributed databases) likeparallelize().

# Initialization spark context class
sc = SparkContext()
# Create spark session
spark = SparkSession 
.builder 
.appName("Python Spark DataFrames basic example") 
.config("spark.some.config.option", "some-value") 
.getOrCreate()

getOrCreate It creates a new session if the session with the specified name doesn’t exist.

Spark Session

After the successful creation of the session, it can be verified by running the spark command. It will return the details for the running session.

spark

Spark RDDs

Spark RDDs are primitive distributed data abstraction in spark and we apply functional programming concepts to work with RDDs.

RDD accepts multiple types of files including :

  • Sequence Files, Avro, Parquet, Text, Hadoop inputs, etc.
  • Cassandra, HBase, Amazon S3, HDFS, etc.

Note: RDDs are immutable in nature in order to maintain data integrity. We can’t modify data in RDD we can only create a new one or replace it.

RDDs are works in a distributed system, which means the data in RDD will be distributed across the multiple nodes.

RDDs are lazily evaluated data systems. which means it won’t apply any transformation until the action is not called.

  • sc.parallelize It inputs a series and turns it into RDD.

Converting a list into an RDD using parallelize method.

data = range(1,20)
# print first element of iterator
print(data[0])
len(data)
xRDD = sc.parallelize(data, 4)

Apache Spark RDD Transformation

As we know that RDDs are immutable in nature, when we perform a transformation it returns a new RDD. RDDs are lazy evaluated which means no calculation is performed until the action is not called explicitly.

Transformations are the set of rules to be applied to the RDDs and the actions are the command that applies the transformation of RDDs.

# Reduces each number by 1
sRDD = xrangeRDD.map(lambda x: x-1)
# selects all number less than 10
filteredRDD = sRDD.filter(lambda x : x<10)

Calling action

When we call an action, RDDs start it applying all the transformation that has already been specified on that particular RDD.

print(filteredRDD.collect())
filteredRDD.count()

Conclusion

In this article, we discussed how the Big data ecosystem is made, and what are the tools that make it.

We looked at the various category of tools that works in the big data ecosystem. We learned about Spark’s Core and its components. We discussed what makes Spark faster than MapReduce and how it replaces MapReduce.

We talked about Spark RDD and Transformations and actions:

  • Spark RDDs are immutable that maintain data integrity.
  • Spark RDDs are lazy evaluated, which makes data processing easier and simple.
  • Spark also Supports Distributed SQL processing.
  • Spark can run on any python environment.
  • Spark Session and context must be created.

Thanks for reading this article!

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Abhishek Jaiswal
Abhishek Jaiswal

A data enthusiast exploring the leading technologies related to the data

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