Ace Your Interview with Top 10 Interview Questions on Delta Lake

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.

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.

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:

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?

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

Q6. Explain the Main Components of a Delta Lake.

Q7. How do we Perform Upserts in Delta Lake?

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

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

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

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.

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