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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

Ace Your Interview with Top 10 Interview Questions on Delta Lake

Shikha
Shikha Last Updated : 14 Feb, 2023
6 min read

Introduction

Every data scientist demands an efficient and reliable tool to process this big unstoppable data. Today we discuss one such tool called Delta Lake, which data enthusiasts use to make their data processing pipelines more efficient and reliable.

Basically, Delta Lake is an open-source storage layer that lies on top of our existing data storage infrastructure and enables schema enforcement, versioning, and ACID (atomicity, consistency, isolation, and durability) transactions for our data. Delta Lake offers several benefits, such as managing the huge volume of data, being able to roll back changes easily, and providing data consistency across multiple Spark sessions.

If you’re preparing for the Delta Lake interview, you landed at the right blog. Here we discuss the most frequently asked Delta Lake interview questions.

Learning Objectives

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

  • Understanding of what a Delta Lake is and what role it plays in the technical era.
  • Knowledge of its relationship with Apache Spark.
  • An understanding of the data insertion or loading process in Delta Lake.
  • An understanding of the Delta Lake components and their ACID-compliant properties.
  • Insights into concepts like Upserts, modes of reading data, and Batch and Streaming operations in Delta Lake.

Overall, by reading this guide, we will gain a comprehensive understanding of Delta Lake to store the data. After completing this blog, we have enough knowledge and ability to use this technique effectively and respond to common intermediate-level queries, and you can ace your delta lake interview.

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

Table of Contents

Q1. How does Delta Lake Differ From Other Transactional Storage Layers?

Although Delta Lake also solves the same challenges solved by other transactional layers, that’s not it; it has a broader use case coverage across the data ecosystem, which provides fame to it. Delta Lake provides data security, reliability, and better performance and offers a unified framework for batch and streaming workloads. It improves the efficiency of various downstream activities like BI, ML, data science, and data transformation pipelines.

delta lake

Source: kpipartners

Also, to get more benefits, we can use Delta Lake on Databricks; it provides broader ecosystem support with faster native connectors to the most popular Business Intelligence tools, enables better performance with Delta Engine, and offers better security and governance with fine-grained access controls.

At last, coming to the stats, around 3 petabytes of data is ingested by Delta lakes on a daily basis and has been in production for over 3 years; thousands of users are using Delta Lake on Databricks.

Q2. Explain How Delta Lakes are ACID Compliant.

Delta Lakes are ACID compliant because:

delta lake

A(Atomicity)- Delta Lake offers atomic transactions, which imply all modifications to the data in a Delta table are either all committed or all rolled back.

C(Consistency)- Delta Lake offers data consistency which implies that the data readers will always read the same data at the time the transaction was started.

I(Isolation)- With the help of a time travel feature, Data lakes support isolation and allow users to view data as it exists at any time.

D(Durability)- Data Lake supports durability by showing all the transactional changes despite system failures.

Q3. Explain the Relationship of Delta Lake with Apache Spark.

Delta Lake is a tool built on top of Apache Spark and offers a path to manage storage and enhance performance for Spark applications. Delta Lake enhances the performance when Spark reads and writes data by storing data in Parquet files. It uses a columnar format and to ensure data consistency, it offers a way to manage transactions and keep track of data modifications.

Q4. Why use Delta Lake if we can Store Data in Parquet Format on S3 or HDFS?

Delta Lake is a good choice over Parquet when we have to perform large-scale data processing because it offers high scalability and better performance. Also, despite power outages or hardware failures, the data will remain safe from corruption due to the ACID-compliant design of Delta Lakes.

Q5. Explain the Process of Importing Data into Delta Lake.

We can import data into Delta Lake just by using the Databricks Auto Loader tool or the COPY INTO command with SQL; it intakes new data files into Delta Lake automatically because they come in our data lake (i.e., on S3 or ADLS). Moreover, we can use Apache SparkTM to batch-read our data by performing the necessary changes and storing the outcome in Delta Lake.

Q6. Explain the Main Components of a Delta Lake.

Delta Lake comprises three important components the Delta table, the Delta log, and the Delta cache.

Delta Table: It is the central storage part that carries the entire data for a Delta Lake.

Delta Log: A transaction log is used to track or monitor all the modifications made to the Delta table.

Delta Cache: It is a columnar cache, and just like the normal cache, it stores the current version of the data in the Delta table.

Q7. How do we Perform Upserts in Delta Lake?

Upsert is a combination of two words/operations, i.e., Update and Insert. We can perform upserts in delta lake using MERGE and INSERT INTO commands:

Merge: With the help of the MERGE command, we can update or insert any data into a Delta table depending on a given condition. Using the WHERE clause, we put a condition on any command, and if the condition results in true, the UPDATE action is performed; if the condition results in false, the INSERT action is performed.

Insert:With the help of the INSERT INTO command, we can insert data into a Delta table, but this command will insert only new rows into the table, with no updation operation to the existing rows.

Q8. Explain the Different Modes Available to Read Data from a Delta Lake Table.

To read the data from a Delta Lake table, we have two available modes:

1. Full Scan Mode: This mode is used to read the entire contents of the Delta Lake table.

2. Incremental Scan Mode: This mode is used to read only data inserted or modified since the last time the Delta table was read.

Q9. Explain the Significance of Batch and Streaming Operations in Delta Lake.

We can run batch and streaming operations with Delta Lake on a single simplified architecture, avoiding complex, redundant systems and operational challenges. In Delta Lake, a table is both a batch table and a streaming source.

databricks

Source: hevodata.com

In terms of significance, Interactive queries, Streaming data ingest, and the batch historic backfill work out of the box and directly integrate with Spark Structured Streaming.

Q10. How can we Load Data into a Table From Another File System in Delta Lake?

To perform the load operation, Delta Lake supports a process called “upserts.” It loads data into a Delta table from another existing file system. In this process, first, we check whether a row with the same primary key already exists in the table or not. If the row exists, it gets updated with the new data; otherwise, it gets inserted into the table.

Conclusion

This blog covers some of the frequently asked Delta Lake interview questions that could be asked in data science and big data developer interviews. Using these delta lake interview questions as a reference, you can better understand the concepts and formulate effective answers for upcoming interviews. The key takeaways from this Delta Lake blog are:-

  1. Delta Lake is an ACID-compliant open-source storage layer that lies on top of our existing data storage infrastructure.
  2.  Delta Lake facilitates us with the management of huge data and maintaining data consistency across multiple Spark sessions.
  3. Delta Lake is better than various transactional storage layers in terms of
  4. We discussed the upserts, a way to load data in the Data Lake tables.
  5. In this blog, we also discussed the components of Delta Lake, including table, log, and Delta cache.

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

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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 100 Data Science Interview Questions & Answers 2024
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Top Delta Lake Interview Questions
Top Delta Lake Interview Questions

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