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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
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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
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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
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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
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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
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8 Ways to Improve Accuracy of Machine Learning Models
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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
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Top 5 Interview Questions on Cassandra

Shikha
Shikha Last Updated : 02 Mar, 2023
6 min read

Introduction

Cassandra is an Apache-developed free and open-source distributed NoSQL database management system. It manages huge volumes of data across many commodity servers, ensures fault tolerance with the swift transfer of data, and provides high availability with no single point of failure.

https://blog.knoldus.com/tag/use-cases-of-cassandra/

Java-written Apache Cassandra is highly scalable for Big Data models and comprises flexible schemas. It is a hybrid of column-oriented and key-value store databases, initially designed by Facebook.

In this blog, we discuss Cassandra’s questions deeply, which are beneficial for beginners and experts.

Learning Objectives

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

  • A common understanding of what Cassandra is and its role in the technical era.
  • Knowledge of Cassandra-related terms like Data Replication, Commit Log, Composite Key, Consistency, Cluster, etc.
  • An understanding of the CAP theorem.
  • An understanding of Eventual and Strong tunable consistencies.
  • Insights into the concept of when Cassandra is useful and when we need to avoid its usage.

Overall, by reading this blog, we will gain a comprehensive understanding of managing a large volume of data. We will be equipped with the knowledge and ability to use this technique effectively and handle the coming flow of interview questions.

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

Table of Contents

Q1. Why use Cassandra Despite having many Traditional Databases?

We have many reasons to prove the consideration of Apache Cassandra that are enough to replace traditional databases. Some are:

image.png

Real-time Performance: Apache Cassandra simplifies the job of many Software Engineers, Developers, Administrators, and Data Analysts by providing a near real-time performance that is not available in usual databases.

Peer-to-peer Architecture: It assures no failure just because of its peer-to-peer architecture, whereas in traditional databases, we still use the master-slave architecture. In any data center, Cassandra allows the insertion of multiple nodes into any cluster, which assures phenomenal flexibility. It allows clients to forward its request to any server.

Scalability: When it comes to scalability, Cassandra allows us to scale up and down easily per the user requirements, facilitating extensible scalability.  At the time of scaling, we don’t have to restart this NoSQL application specifically with high throughput for read and write operations.

Data Replication: Cassandra facilitates vital data replication on node capability by allowing users to access the data in another location if one node fails. It offers data storage at multiple locations. Users can choose the number of replicas they want to create as per their requirements.

Massive Dataset: Cassandra is often called the most preferable NoSQL database as it offers outstanding performance when used for massive datasets.

Column-Oriented: Cassandra is a column-oriented database that makes data access and retrieval efficient and fastens the slicing process.

Schema-Free data model:  As Cassandra follows the schema-optional data model, we are not bound to show all the columns of an application; we can avoid unwanted data.

Q2. Explain the Following Terms in Cassandra.

1. Data Replication: Cassandra supports the data replication feature to ensure data redundancy and fault tolerance in the database. Data Replication is basically an operation in which data from one node is copied to other nodes in the cluster. Data replication comprises two components: the replication factor, which decides the count of copies, and the replication strategy, which decides the nodes in which the data is copied.

2. Commit Log: Commit Log is a mechanism that is used at the time of database crashes to recover data. We can recover the data from the commit log easily because every operation that is carried out is saved/defined in the commit log.

3. Composite Key: Cassandra’s composite keys are made up of a row key and column name, used to declare the column family with a concatenation of data of different data types.

4. Consistency: Consistency is a technique used to synchronize and update the replicas and rows of Cassandra data.

5. Memtable: Generally, the cache space carrying the data in key and column format is referred to as Memtable.

6. SSTable: SSTable stands for the Sorted String Table, a data file that accepts the regular Mem Tables.

7. Data Center: As the name suggests, the Data center is a collection of all the data that is available in the Cluster.

8. YAML file in Cassandra: The main configuration file of Cassandra is Cassandra.yaml file; we have to restart the node to see the changes just after updating any properties in this Cassandra.yaml file.

9. Clusters: Clusters are basically the containers for the Keyspaces. They are the outermost structure in Cassandra and are often known as rings because the data to the cluster node is arranged in a circular ring.

Q3. Define CAP Theorem in Cassandra.

CAP stands for Consistency, Availability, and Partition Tolerance, this theorem plays a significant role in managing the scaling strategy by the time it’s required to scale systems when additional resources are needed.

https://cassandra.apache.org/_/cassandra-basics.html
Source: cassandra.apache.org

CAP theorem is an efficient method to handle scaling in distributed systems like Cassandra. According to the CAP theorem, users can take advantage of only two out of these three characteristics by sacrificing one. We have two possibilities for the characteristics: AP (Availability and Partition Tolerance) and CP( Consistency and Partition Tolerance).

The characteristics are defined as follows:

Consistency: It ensures the user’s return of the most recent write.

Availability: It ensures a rational response within a minimum time.

Partition Tolerance: It ensures that the system will continue its operations whenever the network partition occurs. The two options available are AP and CP.

Q4. Explain Tunable Consistency in Cassandra.

Tunable consistency is nothing but Cassandra’s phenomenal characteristic, making it a famous choice in competition with other traditional databases. The synchronized and up-to-date data rows among the replicas are what consistency refers to here. In order to choose the best-suited consistency level for our use cases, we rely on it’s Tunable Consistency. Tunable Consistency supports two types of consistencies:-

  1. Eventual Consistency: When we don’t have any new update or change over the given data items, we refer to the eventual consistency where all accesses eventually return the last updated value.
  2. Strong Consistency: For strong consistency in Cassandra, the following conditions must be held:

R + W > N

Here,

N: Number of replicas

W: Number of nodes that have to agree for a successful write

R: Number of nodes that have to agree for a successful read

So, we can say that strong consistency is when the number of replicas is smaller than the summation of the number of nodes with successful reads and the number of nodes with successful writes.

Q5. When to use Cassandra, and When not to use?

When it comes to the real-time usage of Cassandra, it plays a significant role in solving the need for heavy write systems by offering an efficient, responsive reporting system. Cassandra is a member of the NoSQL family, and it helps in cases when we’re looking for quick responses. You can see the usage in web analytics where the log data of requests is stored, and we desire to create an analytical platform around that for counting the hits by the IP, by browser, by the hour, etc. in a real-time format.

https://data-flair.training/blogs/cassandra-applications/
 Source: data-flair.training

Although Cassandra is the most famous and reliable technique, when it comes to the use cases like Financial data, it won’t suit due to the unavailability of ACID property and relational databases. It is based on the NoSQL database, and when our demand is to manage the data concerning ACID properties, the scenario becomes hard. We can perform that process here, but we have to write lots of application codes to manage the ACID property and will lose time to market badly. Also, handling these codes in Cassandra would be hard and tedious for us.

Conclusion

This blog covers some of the frequently asked Apache Cassandra 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 Cassandra and start formulating effective answers for upcoming interviews. The key takeaways from this blog are:

  1. Apache Cassandra is a Java-written, NoSQL database management system that can manage large volumes of data and ensures the fault-tolerance.
  2. Although we have many traditional databases, the availability of features like real-time performance, peer-to-peer architecture, scalability, data replication, schema-free model, etc., makes it a unique and irreplaceable technique.
  3. We learn some of the commonly asked definitions of rapid-fire interview rounds in this blog, including SSTable, Memtable, Commit Log, Data Replication, Consistency, etc.
  4. It offers Eventual and Strong tunable consistencies which keep the data up-to-date.

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

ApacheblogathonCassandradatabasedatabasesinterview questionsNoSQL
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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Top 6 Cassandra Interview Questions
Top 6 Cassandra Interview Questions
Apache Cassandra: High-Performance Distributed NO-SQL Database
Apache Cassandra: High-Performance Distributed NO-SQL Database

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