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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
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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

Must-Read Top 8 Interview Questions on Apache Flume

Shikha
Shikha Last Updated : 04 Feb, 2023
6 min read

Introduction

In this constantly growing technical era, big data is at its peak, with the need for a tool to collect and move this massive data effectively. Apache Flume is one tool that can collect, aggregate, and transfer massive volumes of data from one or more sources to a centralized data source efficiently and reliably.

The data sources are customizable in Apache Flume, that’s why it can ingest any data, including log data, network data, event data, social-media generated data, emails, message queues, etc.

What is interceptor in Apache flume? - Quora

Source: Quora

Moreover, Hadoop and Big Data developers use this tool to get log data from social media websites. Cloudera developed Flume gathers log files from multiple data sources and asynchronously persist in the Hadoop cluster.

In this blog, I discussed eight interview-winning questions that will help you to set a pace for Apache Flume and ace your upcoming interview!

Learning Objectives

Below is what we’ll learn after reading this blog thoroughly:

  • A common understanding of an Apache Flume and its role in the technical era.

  • Knowledge of reliability and failure handling in Apache Flume.

  • An understanding of the data flow process in Flume.

  • An understanding of features and use cases of Apache Flume.

  • Insights into concepts like channel Selectors, Consolidation, and Interceptors in Flume.

Overall, by reading this guide, we will gain a comprehensive understanding of Flume to move the data. We will be equipped with the knowledge and ability to use this technique effectively.

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

Table of Contents

  1. Explain the concept of data flow in Apache Flume.
  2. Explain the use cases of Apache Flume.
  3. Explain the concept of Reliability and Failure Handling in Apache Flume.
  4. What do you mean by Channel Selectors?
  5. Explain the concept of Consolidation in Flume.
  6. Define the usage of Interceptors in Flume.
  7. How can you use the HDFS “put” command for Data Transfer from Flume to HDFS?
  8. Explain the features of Flume.
  9. Conclusion.

Q1. Explain the Concept of Data Flow in Apache Flume.

In flume, we use log servers to generate the events and log data, and the Flume framework transfers that log data into HDFS. The Flume agents were constantly running on the log servers and receiving data from the data generators.

Flume Data Flow - Types & Failure Handling in Apache Flume - DataFlair

Source: data-flair.training

The role of Flume is like an intermediate node that collects the data in these agents, those nodes are what we call Collectors, and n-number of collectors are available in Flume just like agents. Finally, data will be aggregated and pushed to a centralized store like Hadoop, HBase, Hive, etc. just by using these collectors.

Flume 1.11.0 User Guide — Apache Flume

Source: flume.apache.org

Types of Data Flow in Flume:-

  • Multi-hop Flow: In the multi-hop Data flow, multiple agents can be present, and an event may travel through more than one agent before reaching the final destination within Flume.

Flume Data Flow - Types & Failure Handling in Apache Flume - DataFlair

 

Source: data-flair.training

  • Fan-out Flow: Fan-out data flow is the condition when the data transfers or flows from one source to multiple channels within Flume.

Flume Data Flow - Types & Failure Handling in Apache Flume - DataFlair

Source: data-flair.training

  • Fan-in Flow: In contrast with Fan-out flow, Fan-in flow is the transfer of data or data flow in which the data will be transferred from many sources to one channel.

Flume Data Flow - Types & Failure Handling in Apache Flume

Source: data-flair.training

Q2. Explain the Use Cases of Apache Flume.

Although several uses cases of Apache Flume are there in the market, and few of them are:

  1. Apache Flume had a high usage in applications where we want to acquire data from various sources and store it in the Hadoop system.

  2. Apache Flume plays a significant role in applications where we require to handle high-velocity and high-volume data in a Hadoop system.

  3. Apache flume facilitates reliable data delivery to the desired destination or HDFS.

  4. Apache Flume is proven a scalable solution when the velocity and volume of data increase by adding more machines.

  5. Flume can configure the various components of the architecture without incurring any downtime.

Q3. Explain the Concept of Reliability and Failure Handling in Apache Flume.

Whatever technique we are using, if it is unreliable or can’t handle the failures, it becomes a garbage box for our system. Flume uses channel-based transactions to ensure reliable message delivery. Basically, when a message transfers from one agent to another, two transactions are started, one on the side of the agent that delivers the event and the other on the side of the agent that receives the event.

To commit the transaction at the end of the sending agent, it is mandatory to receive the success indication from the receiving agent.

This process ensures the guaranteed delivery of semantics because the receiving agent only returns a success indication if its transaction commits successfully.

Q4. What do you mean by Channel Selectors?

In the case of multiple channels, we usually need clarification while choosing the right track to transfer the data, and then we use the channel selector technique.

Architecture Tutorial

Source: www.cloudduggu.com

Basically, we have two different types of channel selectors:

  • Default channel selectors:− Default channel selector is the replicating selector which is capable of replicating all the events in each channel.

  • Multiplexing channel selectors:− Multiplexing channel selectors are the selectors that choose the channel to send an event based on the address in the event’s header.

Q5. Explain the Concept of Consolidation in Apache Flume.

Consolidation, often known as the beauty of Flume, collects data from multiple sources and flume agents. A flume source can collect the entire flow of data from multiple sources and flows through channels and sink. At last, transfer this data to the Hadoop or the target destinations.

Hadoop DP Notes

Source: hadoopdpnotes

Q6. Define the Usage of Interceptors in Apache Flume.

Generally, the basic usage of interceptors is to either modify/change or drop events in flight. Flume can use interceptors to decide the type of data that should be allowed to pass through to the channel. Flume is also capable of binding interceptors, and the developer can choose the criteria based on which an interceptor can either modify or drop events.

The list of interceptors that you may be easily available includes Timestamp Interceptor, Host Interceptor, Static interceptor, and Regex filtering interceptor.

Q7. How can you use HDFS “put” Command for Data Transfer from Flume to HDFS?

The major challenge data enthusiast face in handling the log data is transferring the logs produced by multiple servers to the Hadoop environment. Hadoop Distributed File System(HDFS) offers several commands to read data from Hadoop and insert data into it. With the help of the HDFS put command, we can transfer the data from Apache Flume.

Syntax:-

$ Hadoop fs –put / /

In the above syntax, the source path means the path of the required file, and the destination path is the path in HDFS where to save the file.

Q8. Explain the Features of Flume.

Flume can be considered a very strong data tool due to its special features, which are-1. Apache Flume is a horizontally scalable tool that supports huge sources, channels, and sinks.

2. Apache Flume enables us to gather data from multiple web servers in real-time and batch mode. It also facilitates us with contextual routing.

3. Flume can handle the read-write rates; for example, if the read rate exceeds the write rate, Flume offers a steady flow of data between read and write operations.

4. The gathering of data carried by Flume data between sources and sinks. It can either be scheduled or event-driven. Now, Flume offers its query processing engine, which facilitates the easy transformation of each new batch of data before moving it to the intended sink.

Conclusion

This blog covers some of the frequently asked Apache Flume interview questions that could be asked in data science and big data developer interviews. Using these interview questions as a reference, you can better understand the concept of Apache Flume and start formulating effective answers for upcoming interviews. The key takeaways from this Flume blog are:-

  1. Apache Flume is one of the strongest tools that can be used to collect and transfer massive volumes of data from multiple sources to a centralized data source.

  2. It is highly useful in applications where we want to transfer data from various sources, and we’re more concerned with the scalability and reliability of data.

  3. We have discussed the concept of consolidation, interceptors, and channel selectors.

  4. At last, we end this blog by discussing some of the important features of Apache Flume.

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

Shikha
Shikha

I am a tech enthusiast, a student, and a learner. I am a critical reader and a lover of words who finds writing blogs interesting. I possess the capability to research and learn new technologies quickly.

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