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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 Delta Lake Interview Questions

j
jayachandra Last Updated : 25 Jul, 2022
6 min read

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

Introduction

Delta lakes lakehouses have gained tremendous popularity due to the support of ACID transactions and out-of-the-box features. Databricks open-sourced deltalake.io recently. Organizations are integrating delta lake into their data stack to gain all the benefits that delta lake delivers.

With an elevated demand for data engineering roles, staying up to date with the market is crucial, if not mandatory.
Let’s get right into it and strengthen our understanding of delta lake.

deltalake interview questions

Frequently Asked Interview Questions

Q1. Why choose lakehouse over data lake?

Delta lake is a hybrid of a data lake and a data warehouse, and adapting delta lake’s lakehouse will deliver the best of both worlds. Harnessing the benefits of a delta lake for storing unstructured data with ACID transactions, serving BI operations, and training ML models on the data is efficient.
Delta lake presents multiple propitiatory features for handling streaming data, machine learning models, data quality, governance, and scalability.

Q2. What are the different layers of a lakehouse?

Deltalake supports multiple layers by different names – “Delta”, “multi-hop”, “medallion”, and “bronze/silver/gold” layers.
 

Bronze / Raw Layer: This is the docking layer for upstream data sources to ingest raw data.
The bronze layer is for storing raw files or data as it arrives. This layer is a source layer for downstream silver layers.

Silver / Aggregate Layer: This is the central layer where ETL and aggregations take place.
The silver layer is where we perform data quality checks, governance, joins, and aggregations.

Gold / Business Layer: This is the final layer where business-level use-case-specific aggregations take place.
The gold layer is the source for dashboards, analytics, and BI tasks.

Q3. What is the delta format?

Delta format is an exclusive open-source product derived from parquet data format by data bricks.

delta_format = spark.read.format("delta").load("path_to_file")

Delta format uses parquet’s schema-on-read and supports schema-on-write by providing a transactional storage layer. Delta format maintains versioning to support lineage and state. Delta format stores metadata in the _delta_log folder to support ACID transactions.

Q4. what is a transaction in delta lake?

Delta lake transaction is an entry in the _delta_log table to mark a query/operation completed successfully. Delta lake supports transactions at table level. for example,

CREATE OR REPLACE TABLE test_table (name STRING, reputation INT, count INT);
INSERT INTO test_table VALUES ('Analytics_vidya', 2, 10);

The above SQL query creates two transactions in the _delta_log folder for the CRAS statement and INSERT. If we insert multiple entries via individual insert queries, a transaction will get appended for each insert.
Multiple entries in a single insert will add one transaction log.

INSERT INTO test_table VALUES ('Analytics_vidya', 2, 10),
                              ('Databricks', 5, 20);

All the transactions are logged as JSON files in the _delta_log folder incrementally.

delta lake

These files are tables holding information about commit info added and removed fields.

delta lake

Q5. Does delta lake support upserts?

Yes, Delta lake offers upsert operations via the MERGE function.
In SQL, upserts are a means to accomplish updates and inserts simultaneously. Delta lake’s MERGE operates similarly by providing the mechanism to execute insert, update and delete operations in one go.

MERGE INTO table_1 a
USING table_2 b
ON a.id=b.id
WHEN MATCHED AND b.type = condition_1
  THEN UPDATE SET *
WHEN MATCHED AND b.type = condition_2
  THEN DELETE
WHEN NOT MATCHED AND b.type = "condition_3"
  THEN INSERT *;

Although executing multiple operations, MERGE contributes towards a single transaction in the _delta_log.

Q6. How does delta lake provide fault tolerance?

Delta lake supports checkpointing with write-ahead logs / Journaling to overcome data loss with robust recovery mechanisms and guarantees.
To achieve durability in an operation, databases and file systems offer write-ahead logs. Delta lake makes use of this feature through checkpointing.

spark.readStream.table("raw_table")
        .writeStream
        .foreachBatch("custom_logic")
        .outputMode("append")
        .option("checkpointLocation", test_checkpoint)
        .trigger(once=True)
        .start()

In Delta lake’s context, a checkpoint is a directory where all the received data are logged as files and then processed. When a failure has been encountered, a stream or job can pick up from where the operation terminated.

Q7. What is a constraint in delta lake?

Constraints are a way to enforce checks on our data to verify the quality and integrity of the data.

ALTER TABLE test_table ADD CONSTRAINT dateWithinRange CHECK (birthDate > '1995-01-01');

Delta lake offers NULL and CHECK constraints to handle nulls and filter data during load/modification.

Q8. What is delta time travel in the lakehouse?

Time travel is a way/technique to roll back or reference a prior data version for audit or to roll back data in case of bad writes or accidental deletes.
Delta lake creates snapshots of the data and maintains versioning. All the changes to the data get logged as metadata in _delta_log.
Delta lake provides two variants to accomplish time travel. One uses version, and the other uses timestamps.
Version format-

SELECT count(*) FROM test_table VERSION AS OF 5
SELECT count(*) FROM test_table@v

Timestamp format-

SELECT count(*) FROM test_table TIMESTAMP AS OF "2022-07-01"

Q9. What is Autoloader?

The autoloader is Databricks’ recommended way to handle streaming data efficiently and with ease. Autoloader offers a way to read data from cloud object storage as it arrives, resulting in limited REST calls to the cloud storage with added security.

spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "json")
        .schema(schema)
        .load("path_to_file")

The autoloader provides many exceptional features, such as schema inference and evolution.

Q10. How do you tackle the small file problem and skewness in delta lake?

Delta lake is packed with tons of optimizations to handle many know issues that we encounter with big data.
Most of the time to make sure that our cluster resources utilization and our aggregations are getting executed optimally, it is necessary to make sure we partition our data.
But, Databricks recommends avoiding partitioning the data until necessary because delta lake takes care of the partitions based on their cardinality using proprietary algorithms(auto-optimize).
Databricks performs multiple scans on partitions to check if the data can be compacted further based on small file size to collate small files and avoid skewness in data.

delta.autoOptimize.autoCompact = true
 delta.autoOptimize.optimizeWrite = true

We can manually perform optimization and use Zorder to index our files to merge small files into a manageable partition.

OPTIMIZE test_table
ZORDER BY id

ZORDER is a mechanism used by delta lake data-skipping algorithms for scanning a set of files to Colocate column information and compact files based on an index.

Bonus Questions

1. What is VACCUM?

Continuous data upserts get logged in the _delta_logs folder with a versioned copy.

SET spark.databricks.delta.retentionDurationCheck.enabled = false;
VACUUM test_table RETAIN 0 HOURS;
SET spark.databricks.delta.vacuum.logging.enabled = true;

VACCUM is useful for truncating files from the directory that are no longer valid after a period.
Default Vaccum period to 7 days. Overwrite this carefully in production to avoid data loss.

2. What is ZORDER?

ZORDER is a mechanism used by delta lake data-skipping algorithms for scanning a set of files to Colocate column information and compact files based on an index.

3. How to roll back or restore data?

Delta lake offers a way similar to time travel to restore the files which are deleted or modified accidentally.

RESTORE TABLE test_table TO VERSION AS OF 8;

4. What is a Dry run?

The dry run is a way to perform checks before executing the operation. A dry run returns a list of files set to delete without deleting them.

VACUUM test_table RETAIN 0 HOURS DRY RUN;

Conclusion

Delta lake can be a forefront for creating genius offerings to eliminate the barriers between Data engineering and Date science. Understanding the features and learning your way through is how you can tackle any problem and face the interviews. The features highlighted above are the most commonly asked queries. Advanced concepts are proving to be very useful in organizations, providing exclusive functions to accomplish a complex integration or triggering task with ease.

Takeaways:

  1. Lakehouse is being adapted everywhere as it is proving to be a viable replacement to data lake from both cost and architectural standpoint. 
  2. Delta format is derived from parquet to act as an optimal and feature-rich alternative.
  3. Transactions are an interesting concept supporting ACID guarantees and allowing recovery from the previous state.
  4. OPTIMIZE and ZORDER are very useful in handling partitioned data and small compact files for better query and performance
  5. The AUTOLOADER is a proprietary feature in delta lake to support streaming jobs without any overhead.
  6. VACCUM and DRY RUN can be a great way to reduce the logs and visualize the query results without logging them in transactions.

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

j
jayachandra

Cloud Data Engineer Love building Functional Backends/Distributed Systems/Streaming Apps / Microservices.

Holds Expertise in Scala, Python, Apache Kafka, Apache Spark, AKKA, ZIO, SQL, NoSQL, AWS, GCP, and K8s.

Transitioning to become an expert in cloud Dataops and MLOps.

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