Top Azure Databricks Interview Questions (2024)

1. What is an Azure Databricks?

2. What is an Azure Databricks Lakehouse Platform?

3. What are the features of Azure Databricks?

4. What is DBU Azure Databricks?

5. How does autoscaling work in Databricks?

6. What are DataFrames in Azure Databricks?

7. How to create a DataFrame in PySpark Databricks?

8. How to load data into a DataFrame from files in Databricks?

9. What is PySpark Partition in Databricks?

Q: What is an Azure Databricks?
Ans:

Azure Databricks is a large data analytics service built on Apache Spark that is quick, simple, and collaborative. It is intended for data science and data engineering.

Q: What is an Azure Databricks Lakehouse Platform?
Ans:

The Azure Databricks Lakehouse Platform offers an uniform collection of tools for rapidly developing, deploying, sharing, and supporting enterprise-grade data solutions at scale. Azure Databricks interacts with cloud storage and security, as well as managing and deploying cloud infrastructure.

Q: What are the features of Azure Databricks?
Ans:

1. Data processing on a large scale for batch and streaming workloads.
2. Activate analytics for the most up-to-date and complete data.
3. Facilitate and simplify large-scale data science.
4. Quick, efficient Apache Spark environment.

Q: What is DBU Azure Databricks?
Ans:

A Databricks Unit (DBU) is a standardized unit of processing power used for measurement and pricing on the Databricks Lakehouse Platform. The number of DBUs consumed by a task is determined by processing metrics, such as compute resources used and data processed.

Q: How does autoscaling work in Databricks?
Ans:

Azure Databricks assures that the cluster has the correct number of workers when user provide a fixed size cluster. When users specify a range for the number of workers, Databricks selects the number of workers needed to complete your job. This is known as autoscaling.

When needed, Databricks clusters spin up and scale to process huge volumes of data, and they spin down when not in use. Pools allow clusters to start and scale more quickly by establishing a managed cache of virtual machine instances that can be obtained as required.

Q: What are DataFrames in Azure Databricks?
Ans:

A DataFrame is a data structure that, like a spreadsheet, arranges data into a 2-dimensional table of rows and columns. Since they offer a flexible and simple way to store and working with data, DataFrames are one of the most commonly used data structures in modern data analytics.

Q: How to create a DataFrame in PySpark Databricks?
Ans:

The PySpark DataFrame can be created using the toDF() and createDataFrame() methods from RDD(Resilient Distributed Datasets), list, and DataFrame. They can also be created from a variety of sources, such as Structured Data Files, TXT, CSV, JSON, ORV, Avro, Parquet, Hive Tables, external databases, existing RDDs, and so on.

// Create a DataFrame from RDD 
spark = SparkSession.builder.appName('Azure DataFrame Example').getOrCreate()
rdd = spark.sparkContext.parallelize(data)

//toDF() function
dataframe1 = rdd.toDF()
dataframe1.printSchema()

Q: How to load data into a DataFrame from files in Databricks?
Ans:

We can load the data from supported file formats such as Delta Lake, Delta Sharing, Parquet, ORC, JSON, CSV, Avro, Text,Binary.

df = (spark.read
  .format("csv")
  .option("header", "true")
  .option("inferSchema", "true")
  .load("/databricks-datasets/samples/population-vs-price/data_geo.csv")
)

Q: What is PySpark Partition in Databricks?
Ans:

The PySpark partition can be used to split a large dataset into smaller ones using one or more partition keys.