Introduction
Azure Databricks has emerged as a powerful tool for big data analytics, offering a unified platform that combines the capabilities of Apache Spark with the scalability of Azure.
As organizations increasingly adopt data-driven strategies, the demand for professionals skilled in Azure Databricks is on the rise.
Whether you’re a data engineer, analyst, or developer, preparing for an Azure Databricks interview requires a deep understanding of its features, functionalities, and real-world applications.
In this post, we’ll explore some of the most commonly asked Azure Databricks interview questions, providing you with insights and tips to excel in your next interview.
What is Azure Databricks?
Azure Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale.
Azure Databricks provides tools that help you connect your sources of data to one platform to process, store, share, analyze, model, and monetize datasets with solutions from BI to generative AI.
The Azure Databricks workspace provides a unified interface and tools for most data tasks, including:
- Data processing scheduling and management, in particular ETL
- Generating dashboards and visualizations
- Managing security, governance, high availability, and disaster recovery
- Data discovery, annotation, and exploration
- Machine learning (ML) modelling, tracking, and model serving
- Generative AI solutions
What are all similar offerings from different Cloud Providers?
There are three cloud offerings for Databricks:
- Azure Databricks
- AWS Databricks
- GCP Databricks
However, Databricks is a standalone data processing platform, which is capable of performing ETL/ELT operation as well as store DB & Tables along with big data processing capabilities.
What is Apache Spark, why it is used to process bigdata?
Apache Spark is a unified analytics engine for large-scale data processing.
It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
- Apache spark can use to perform batch processing.
- Apache spark can also use to perform stream processing. For stream processing, we were using Apache Storm / S4.
- It can be used for interactive processing. Previously we were using Apache Impala or Apache Tez for interactive processing.
- Spark is also useful to perform graph processing. Neo4j / Apache Graph was using for graph processing.
- Spark can process the data in real-time and batch mode.
- So, we can say that Spark is a powerful open-source engine for data processing.
Describe Apache Spark underlying architecture?
The Spark engine consists of Spark Core which lies on top of Mesos, YARN: Cluster manager, Job scheduler. The core engine helps process big data parallelly and exposes four APIs for data management and processing.
- Spark SQL API
- Spark Streaming
- Spark for MLlib
- GraphX
The system currently supports several cluster managers:
- Standalone — a simple cluster manager included with Spark that makes it easy to set up a cluster.
- Apache Mesos — a general cluster manager that can also run Hadoop MapReduce and service applications.
- Hadoop YARN — the resource manager in Hadoop 2.
- Kubernetes — an open-source system for automating deployment, scaling, and management of containerized applications.
What are RDD's?
Apache Spark has a well-defined layer architecture that is designed on two main abstractions RDD’s and DAG’s. let’s see what RDD’s are:
- Resilient Distributed Dataset (RDD): RDD is an immutable (read-only), fundamental collection of elements or items that can be operated on many devices at the same time (spark parallel processing). Each dataset in an RDD can be divided into logical portions, which are then executed on different nodes of a cluster.
What is a Metastore?
Metastore is a relational database that is used by Spark, etc. to manage the metadata of persistent relational entities (e.g. databases, tables, columns, partitions) for fast access.
Additionally, a spark-warehouse is the directory where Spark SQL persists tables.
Meta store is responsible to store all the metadata related to the Spark Tables, table locations, file paths, tables schemas etc. It plays an important role in the data processing in Spark.
What is DBFS?
The term DBFS comes from Databricks File System, which describes the distributed file system used by Databricks to interact with cloud-based storage.
The underlying technology associated with DBFS is still part of the Databricks platform.
The DBFS root is a storage location provisioned during workspace creation in the cloud account containing the Databricks workspace
What are Databricks Tables?
A databricks table resides in a schema and contains rows of data. All tables created in Databricks use Delta Lake by default. Tables backed by Delta Lake are also called Delta tables.
A Delta table stores data as a directory of files in cloud object storage and registers table metadata tothe Metastore within a catalog and schema. All Unity Catalog managed tables and streaming tables are Delta tables. Unity Catalog external tables can be Delta tables but are not required to be.
It is possible to create tables on Databricks that don’t use Delta Lake. These tables do not provide the transactional guarantees or optimized performance of Delta tables. You can choose to create the following tables types using formats other than Delta Lake:
- External tables.
- Foreign tables.
- Tables registered to the legacy Hive Metastore.
What are Managed and Unmanaged Tables?
Managed Tables: Databricks manages the lifecycle and file layout for a managed table. Managed tables are the default way to create tables. Databricks recommends that you use managed tables for all tabular data managed in Databricks.
Unmanaged Tables: External tables(aka unmanaged tables) store data in a directory in cloud object storage in your cloud tenant. You must specify a storage location when you define an external table.
Databricks recommends using external tables only when you require direct access to the data without using compute on Databricks. Unity Catalog privileges are not enforced when users access data files from external systems.
What are Views on Databricks?
A view is a read-only object composed from one or more tables and views in a Metastore.
A view can be created from tables and other views in multiple schemas and catalogs.
What are some of the methods that can be used to read files to data frame?
Spark SQL API provides various methods to read data from disparate systems/ file stores and transform data using spark.
Below mentioned are some of the methods provided by Spark SQL API’s:
- pyspark.sql.DataFrameReader.csv()
- pyspark.sql.DataFrameReader.json()
- pyspark.sql.DataFrameReader.table()
- pyspark.sql.DataFrameReader.parquet()
- pyspark.sql.DataFrameReader.orc()
Here DataFrameReader is a class and has different attributes for different file types.
What are Clusters, Executors & Drivers?
The Apache Spark framework uses a master-slave architecture that consists of a driver, which runs as a master node, and many executors that run across as worker nodes in the cluster.
The Spark driver:
The driver is the program or process responsible for coordinating the execution of the Spark application. It runs the main function and creates the Spark Context, which connects to the cluster manager.
The Spark executors:
Executors are worker processes responsible for executing tasks in Spark applications. They are launched on worker nodes and communicate with the driver program and cluster manager.
Executors run tasks concurrently and store data in memory or disk for caching and intermediate storage.
The cluster manager:
The cluster manager is responsible for allocating resources and managing the cluster on which the Spark application runs. Spark supports various cluster managers like Apache Mesos, Hadoop YARN, and standalone cluster manager.
What is a Spark Session?
Spark Session is the entry point for any Spark functionality. It represents the connection to a Spark cluster and can be used to create RDDs (Resilient Distributed Datasets), accumulators, and broadcast variables. Spark Context also coordinates the execution of tasks.
What are different Joins in Apache Spark?
Inner Join:
The inner join is the default join in Spark SQL. It selects rows that have matching values in both relations.
Left Join:
A left join returns all values from the left relation and the matched values from the right relation, or appends NULL if there is no match. It is also referred to as a left outer join.
Right Join:
A right join returns all values from the right relation and the matched values from the left relation, or appends NULL if there is no match. It is also referred to as a right outer join.
Full Join:
A full join returns all values from both relations, appending NULL values on the side that does not have a match. It is also referred to as a full outer join.
Cross Join:
A cross join returns the Cartesian product of two relations.
Semi Join:
A semi join returns values from the left side of the relation that has a match with the right. It is also referred to as a left semi join.
Anti Join:
An anti-join returns values from the left relation that has no match with the right. It is also referred to as a left anti join.
What is Broadcast Join, how does it help in faster data processing?
A broadcast join in Apache Spark is a join operation between two datasets where one of the datasets, often referred to as the “small” dataset, is small enough to fit entirely in the memory of each worker node in the cluster.
When performing a broadcast join, Spark broadcasts the entire small dataset to all worker nodes before executing the join operation.
This approach can significantly improve the performance of certain types of joins, especially when dealing with a small dataset joining with a much larger one.
- Because the small dataset is in memory on each worker node, the join operation is fast and efficient.
- There is no data shuffling involved, reducing network I/O and overhead.
- Broadcast joins are particularly beneficial when the small dataset is used for multiple joins or filter conditions within the same Spark job.
What is Data Caching in Spark?
Caching is a powerful optimization technique in Apache Spark that can significantly improve the performance of your data processing tasks.
Caching in Spark:
- Stores DataFrames in memory or on disk
- Improves speed on later transformations and actions
- Reduces resource usage by avoiding recomputation
Caching can be beneficial in several scenarios:
- When a DataFrame is used multiple times in your workflow
- For iterative algorithms that repeatedly access the same data
- When you need to speed up subsequent transformations or actions on a DataFrame
What is Data Shuffling in Spark?
Shuffling is the process of rearranging and redistributing data across nodes or partitions within a computing cluster.
It involves moving data between different servers to organize related data items together, which helps to do efficient analysis and processing tasks such as grouping, aggregation, and sorting.
What is a Delta Lake?
Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. Delta Lake is open-source software that extends Parquet data files with a file- based transaction log for ACID transactions and scalable metadata handling.
Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale.
Delta Lake is the default format for all operations on Databricks. Unless otherwise specified, all tables on Databricks are Delta tables. Databricks originally developed the Delta Lake protocol andcontinues to actively contribute to the open-source project. Many of the optimizations andproducts in the Databricks platform build upon the guarantees provided by Apache Spark and Delta Lake.
What is a Data Lakehouse?
A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.
Conclusion
Azure Databricks is a powerful platform for big data processing and machine learning, making it a crucial skill for data engineers and analysts.
Preparing for interviews on Azure Databricks requires a solid understanding of its architecture, features, and integration capabilities with Azure services. By mastering concepts like notebooks, clusters, Spark operations, and data pipelines, you can confidently tackle challenging questions.
With the right preparation, you can showcase your expertise and secure opportunities in the dynamic world of data engineering and analytics. Good luck!
