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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

Top 20 Apache Oozie Interview Questions

Prateek Majumder
Prateek Majumder Last Updated : 20 Sep, 2022
5 min read

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

Introduction

Apache Oozie is a Hadoop workflow scheduler. It is a system that manages the workflow of dependent tasks. Users can design Directed Acyclic Graphs of workflows that can be run in parallel and sequentially in Hadoop.

Apache Oozie

Image: https://oozie.apache.org/

Apache Oozie is an important topic in Data Engineering, so we shall discuss some Apache Oozie interview questions and answers. These questions and answers will help you prepare for Apache Oozie and Data Engineering Interviews.

Read more about Apache Oozie here.

Interview Questions on Apache Oozie

1. What is Oozie?

Oozie is a Hadoop workflow scheduler. Oozie allows users to design Directed Acyclic Graphs of workflows, which can then be run in Hadoop in parallel or sequentially. It can also execute regular Java classes, Pig operations, and interface with HDFS. It can run jobs both sequentially and concurrently.

2. Why do we need Apache Oozie?

Apache Oozie is an excellent tool for managing many tasks. There are several sorts of jobs that users want to schedule to run later, as well as tasks that must be executed in a specified order. Apache Oozie can make these types of executions much easier. Using Apache Oozie, the administrator or user can execute multiple independent jobs in parallel, run the jobs in a specific sequence, or control them from anywhere, making it extremely helpful.

3. What kind of application is Oozie?

Oozie is a Java Web App that runs in a Java servlet container.

4. What exactly is an application pipeline in Oozie?

It is important to connect workflow jobs that run regularly but at various times. Multiple successive executions of a process become the input to the following workflow. When these procedures are chained together, the outcome is referred to as a data application pipeline.

5. What is a Workflow in Apache Oozie?

Apache Oozie Workflow is a set of actions that include Hadoop MapReduce jobs, Pig jobs, and so on. The activities are organized in a control dependency DAG (Direct Acyclic Graph) that governs how and when they can be executed. hPDL, an XML Process Definition Language, defines Oozie workflows.

6. What are the major elements of the Apache Oozie workflow?

The Apache Oozie workflow has two main components.

  • Control flow nodes: These nodes are used to define the start and finish of the workflow, as well as to govern the workflow’s execution path.
  • Action nodes are used to initiate the processing or calculation task. Oozie supports Hadoop MapReduce, Pig, and File system operations and system-specific activities like HTTP, SSH, and email.

7. What are the functions of the Join and Fork nodes in Oozie?

In Oozie, the fork and join nodes are used in tandem. The fork node divides the execution path into multiple concurrent paths. The join node combines two or more concurrent execution routes into one. The join node’s descendants are the fork nodes that connect concurrently to form join nodes.

Syntax:

< fork name=”[FORK-NODE-NAME]” >

< path start=”[NODE-NAME]” / >

…

< path start=”[NODE-NAME]” / >

< /fork >

…

< join name=”[JOIN-NODE-NAME]” to=”[NODE-NAME]” / >

8. What are the various control nodes in the Oozie workflow?

The various control nodes are:

  • Start
  • End
  • Kill
  • Decision
  • Fork & Join Control nodes

9. How can I set the start, finish, and error nodes for Oozie?

This can be done in the following Syntax:<error

“[A custom message]”

10. What exactly is an application pipeline in Oozie?

It is important to connect workflow jobs that run regularly but at various times. Multiple successive executions of a process become the input to the following workflow. When these procedures are chained together, the outcome is referred to as a data application pipeline.

11. What are Control Flow Nodes?

The mechanisms that specify the beginning and end of the process are known as control flow nodes (start, end, fail). Furthermore, control flow nodes give way for controlling the workflow’s execution path (decision, fork, and join)

12. What are Action Nodes?

The mechanisms initiating the execution of a computation/processing task are called action nodes. Oozie supports a variety of Hadoop actions out of the box, including Hadoop MapReduce, Hadoop file system, Pig, and others. In addition, Oozie supports system-specific jobs such as SSH, HTTP, email, and so forth.

13. Are Cycles supported by Apache Oozie Workflow?

Apache Oozie Workflow does not support cycles. Workflow definitions in Apache Oozie must be a strict DAG. If Oozie detects a cycle in the workflow specification during workflow application deployment, the deployment is aborted.

14. What is the use of the Oozie Bundle?

The Oozie bundle enables the user to run the work in batches. Oozie bundle jobs are started, halted, suspended, restarted, re-run, or killed in batches, giving you more operational control.

15. How does a pipeline work in Apache Oozie?

The pipeline in Oozie aids in integrating many jobs in a workflow that runs regularly but at different intervals. The output of numerous workflow executions becomes the input of the next planned task in the workflow, which is conducted back to back in the pipeline. The connected chain of workflows forms the Oozie pipeline of jobs.

16. Explain the role of the Coordinator in Apache Oozie?

To resolve trigger-based workflow execution, the Apache Oozie coordinator is employed. It provides a basic framework for providing triggers or predictions, after which it schedules the workflow depending on those established triggers. It enables administrators to monitor and regulate workflow execution in response to cluster conditions and application-specific constraints.

17. What is the decision node’s function in Apache Oozie?

Switch statements are decision nodes that conduct different jobs dependent on the conclusion of another expression.

18. What are the various control flow nodes offered by Apache Oozie workflows for starting and terminating the workflow?

The following control flow nodes are supported by Apache Oozie workflow and start or stop workflow execution.

  • Start Control Node – The start node is the initial node to which an Oozie workflow job transfers and serves as the workflow job’s entry point. One start node is required for each Apache Oozie workflow definition.
  • End Control Node – The end node is the last node to which an Oozie workflow task transfers, which signifies that the workflow job was completed. When a workflow task reaches the end node, it completes, and the job status switches to SUCCEED. One end node is required for every Apache Oozie workflow definition.
  • The kill control node allows a workflow job to kill itself. When a workflow task reaches the kill node, it terminates in error, and the job status switches to KILLED.

19. What are the various control flow nodes that Apache Oozie workflows offer for controlling the workflow execution path?

The following control flow nodes are supported by Apache Oozie workflow and control the workflow’s execution path.

  • Decision Control Node – A decision control node is similar to a switch-case statement because it allows a process to choose which execution path to take.
  • Fork and Join Control Nodes – The fork and join control nodes work in pairs and function as follows. The fork node divides a single execution path into numerous concurrent execution paths. The join node waits until all concurrent execution paths from the relevant fork node arrive.

20. What is the default database Oozie uses to store job ids and statuses?

Oozie stores job ids and job statuses in the Derby database.

Conclusion

These Apache Oozie Interview Questions can assist you in becoming interview-ready for your upcoming personal interview. In Oozie-related interviews, interviewers usually ask the interviewee these questions.

To sum up:

  • Apache Oozie is a distributed scheduling system to launch and manage Hadoop tasks.
  • Oozie allows you to combine numerous complex jobs that execute in a specific order to complete a larger task.
  • Two or more jobs within a specific set of tasks can be programmed to execute in parallel with Oozie.
The real reason for adopting Oozie is to manage various types of tasks that are being handled in the Hadoop system. The user specifies various dependencies between jobs in the form of a DAG. This information is consumed by Oozie and handled in the order specified in the workflow. This saves the user time when managing the complete workflow. Oozie also determines the frequency at which a job is executed.

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Prateek Majumder
Prateek Majumder

Prateek is a dynamic professional with a strong foundation in Artificial Intelligence and Data Science, currently pursuing his PGP at Jio Institute. He holds a Bachelor's degree in Electrical Engineering and has hands-on experience as a System Engineer at TCS Digital, where he excelled in API management and data integration. Prateek also has a background in product marketing and analytics from his time with start-ups like AppleX and Milkie Way, Inc., where he was involved in growth campaigns and technical blog management. Recognized for his structured thinking and problem-solving abilities, he has received accolades like the Dr. Sudarshan Chakraborty Award for Best Student Performance. Fluent in multiple languages and passionate about technology, Prateek continues to expand his expertise in the rapidly evolving AI and tech landscape.

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