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