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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

Most Frequently Asked Apache HBase Interview Questions

Prashant
Prashant Last Updated : 08 Aug, 2022
6 min read

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

Introduction

HBase is a column-oriented non-relational database management system that operates on Hadoop Distributed File System (HDFS). HBase provides a fault-tolerant manner of storing sparse data sets, which are prevalent in several big data use cases. It is ideal for real-time data processing or random read/write access to large data volumes. In contrast to relational databases like SQL, HBase doesn’t provide a structured query language like those provided by that database.

Apache HBase
Source: hbase.apache.org

HBase is a data model that works like Google’s “big table” to make it easy to get to a lot of structured data quickly. It comprises a set of tables that store data in a key-value format. Programmers can use Hbase’s APIs in whatever programming language they want. Data in the Hadoop File System may be read and written in real time using this element of the Hadoop ecosystem.

Either directly or via HBase, data may be stored in HDFS. The data consumer uses HBase to read/access HDFS data at random. Read and write access to the Hadoop File System is provided by HBase.

Features

  • Any number of columns can be added to the horizontal scalability at any moment.

  • A multidimensional sorted map is indexed by row key, column key, and timestamp in a distributed manner.

  • In the case of a system breach, an administrator can use automatic failover to automatically transition data handling to a standby system.

  • Built on top of the Hadoop Distributed File System, each command and Java code implements Map/Reduce internally to complete the operation.

  • Frequently referred to as a key-value store, column family-oriented database, or for storing versioned maps of maps.

  • It is basically a system for storing and retrieving data with random access.

  • It does not impose relationships between data elements.

  • It is intended to run on a cluster of commodity hardware-based computers.

Interview Questions

1. What is Apache HBase’s purpose?

Apache HBase is used when random, real-time read/write access to Big Data is required. The objective of this project is to host tables with billions of rows and millions of columns on clusters of commodity hardware. Apache HBase is a distributed, versioned, non-relational, open-source database inspired by Google’s Bigtable: A Distributed Storage System for Structured Data by Chang et al. Apache HBase delivers Bigtable-like functionality on top of Hadoop and HDFS, much as Bigtable utilizes the distributed data storage provided by the Google File System.

2. What are the major elements of HBase?

Major elements of HBase are:

  • Zookeeper: It performs coordination work between the client and HBase Master.

  • HBase Master: HBase Master keeps an eye on the Region Server.

  • RegionServer: RegionServer is responsible for monitoring the Region.

  • Region: It contains both the in-memory data store (MemStore) and the Hfile.

  • Catalog Tables: Tables in catalogs consist of ROOT and META.

3. Examine the purpose of filters in HBase.

Filters were added to Apache HBase 0.92 to make it easier for users to access HBase through Shell or Thrift. As a result, they handle your server-side filtering requirements. There are also beautifying filters, which allow you to get more control over the data produced by filters. Here are some HBase filter examples:

  • Bloom Filter: A space-efficient means of determining if an HFile contains a given row or cell, it is typically used for real-time queries.

  • Page Filter: The Page Filter can optimize the scan of particular HRegions by accepting the page size as a parameter.


4. How does HBase handle a failed write?

In big distributed systems, failures are common, and HBase is no exception.

If the server hosting a MemStore that has not yet been drained crashes. The data in memory, but not yet persisted, are gone. HBase prevents this by writing to the WAL before the write operation is finished. Every server included in the.

The HBase cluster maintains a WAL to record changes as they occur. The WAL is a file on the file system beneath the WAL. Writing is not successful until the new WAL entry has been successfully written. This promise ensures that HBase is as robust as the support file system. HBase is supported by Hadoop Distributed Filesystem most of the time (HDFS). If HBase fails, the data that have not yet been flushed from MemStore to HFile can be retrieved by replaying the WAL.

 

5. Describe deletion in HBase. What are the three types of tombstone markers supported by HBase?

When a cell is deleted in HBase, the data is not truly removed; instead, a tombstone marker is placed, rendering the deleted cell inaccessible. HBase that has been deleted is removed during compactions.

There are three types of tombstone markers:

  • Version delete marker: It identifies a single version of a column for deletion.

  • Column delete marker: It flags for deletion of every version of a column.

  • Family delete marker: It flags every column in a column family for deletion.

6. How does HBase compare to Cassandra?

Cassandra and HBase are both NoSQL databases, a word that has several definitions. Typically, it indicates that SQL cannot be used to manipulate the database. Nonetheless, Cassandra has implemented CQL (Cassandra Query Language), whose syntax is evidently based on SQL.

Both are intended to manage enormous data collections. According to the HBase documentation, an HBase database should include hundreds of millions or, preferably, billions of records. If not, you should continue with a relational database management system.

Not just in terms of how data is kept but also in terms of how the data may be accessed; both are distributed databases. Clients can connect to any cluster node and have access to any data.

HBase lacks native support for secondary indexes but provides a range of methodologies that enable secondary index functionality. These are outlined in the online reference guide for HBase and the HBase community.

7. What happens when the block size of a column family in a previously populated database is altered?

When you modify the block size of a column family, the new data will occupy the new block size, but the old data will stay in the old block size. In the course of data compression, old data will adopt the new block size. As new files are flushed, their block size will change, although current data will remain accurately read. After the next major data compression, all data must be converted to the new block size.

8. Why would you use HBase?

  • High storage capacity system

  • Distributed layout to accommodate big tables

  • Column-Oriented Stores

  • Horizontally Scalable

  • Superior functionality & Availability

  • HBase aims for at least millions of columns, thousands of versions, and billions of rows.

  • Unlike HDFS (Hadoop Distributed File System), it provides CRUD operations in random real-time.

9. What is the Hbase standalone mode?

This option can be enabled when users do not require Hbase to access the HDFS. It is basically a default mode in Hbase, and users are typically allowed to use it whenever they choose. When the user selects this option, the Hbase uses a file system rather than HDFS.

It is possible to save a significant amount of time by using this mode when doing some key activities. During this mode, you may also impose or remove various time constraints on the data.

10. Contrast HBase and Hive?

Hive can enable SQL-savvy users to perform MapReduce jobs. Since it is JDBC-compliant, it is also compatible with current SQL-based applications. Since Hive queries traverse all of the table’s contents by default, their execution may be time-consuming. Nonetheless, Hive’s partitioning function can restrict the volume of data. Partitioning enables the execution of a filter query across data stored in distinct folders and the reading of just the data that matches the query. It might be used, for instance, to only process files generated between specific dates if the file names contain the date format.

HBase operates by storing data as key/value. It provides four core operations: put for adding or updating rows, scan for retrieving a range of cells, get for returning cells for a particular row, and delete for removing rows, columns, or column variants. Versioning is provided to retrieve past data values (the history can be deleted now and then to clear space via HBase compactions). Although HBase contains tables, a schema is necessary only for tables and column families but not for individual columns, and increment/counter functionality is supported.

MapReduce tasks operate on Hive, a SQL-like engine; HBase, a NoSQL key/value database, runs on Hadoop.

Conclusion

This article provides information about HBase, a column-oriented non-relational database management system, and covers a variety of topics, I hope that this information was useful and that you are now more prepared for the next interviews. Here are some of the article’s most salient points:

  • What is HBase, and what are its features?
  • HBase filters and modes are available.
  • HBase comparisons with Hive and Cassandra, as well as many other topics at the basic, intermediate, and tough levels.

Please share your feedback about the article topic, Apache HBase, in the comments section below. Check out more interview questions articles here.

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Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

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