Azure Data Engineer

100+ Azure Data Engineer Interview Questions and Answers

Updated 17 Jan 2025

Q51. How are you connecting your onPerm from Azure?

Ans.

I connect onPrem to Azure using Azure ExpressRoute or VPN Gateway.

  • Use Azure ExpressRoute for private connection through a dedicated connection.

  • Set up a VPN Gateway for secure connection over the internet.

  • Ensure proper network configurations and security settings.

  • Use Azure Virtual Network Gateway to establish the connection.

  • Consider using Azure Site-to-Site VPN for connecting onPremises network to Azure Virtual Network.

Q52. How did you handle failures in ADF Pipelines

Ans.

I handle failures in ADF Pipelines by setting up monitoring, alerts, retries, and error handling mechanisms.

  • Implement monitoring to track pipeline runs and identify failures

  • Set up alerts to notify when a pipeline fails

  • Configure retries for transient failures

  • Use error handling activities like Try/Catch to manage exceptions

  • Utilize Azure Monitor to analyze pipeline performance and troubleshoot issues

Q53. What is the main advantage of delta lake?

Ans.

Delta Lake provides ACID transactions, schema enforcement, and time travel capabilities for data lakes.

  • ACID transactions ensure data consistency and reliability.

  • Schema enforcement helps maintain data quality and prevent data corruption.

  • Time travel allows users to access and revert to previous versions of data for auditing or analysis purposes.

Q54. what is DAG? what is RDD?

Ans.

DAG stands for Directed Acyclic Graph and is a way to represent dependencies between tasks. RDD stands for Resilient Distributed Datasets and is a fundamental data structure in Apache Spark.

  • DAG is used to represent a series of tasks or operations where each task depends on the output of the previous task.

  • RDD is a distributed collection of data that can be processed in parallel across multiple nodes in a cluster.

  • RDDs are immutable and can be cached in memory for faster process...read more

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Q55. 4. Difference between delta and parquet?

Ans.

Delta is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, while Parquet is a columnar storage format optimized for reading and writing data in large volumes.

  • Delta is designed for use with big data workloads and provides ACID transactions, while Parquet is optimized for reading and writing large volumes of data efficiently.

  • Delta allows for updates and deletes of data, while Parquet is a read-only format.

  • Delta supports schema ev...read more

Q56. what are the difference b/w data lake gen1 and gen2

Ans.

Data Lake Gen1 is based on Hadoop Distributed File System (HDFS) while Gen2 is built on Azure Blob Storage.

  • Data Lake Gen1 uses HDFS for storing data while Gen2 uses Azure Blob Storage.

  • Gen1 has a hierarchical file system while Gen2 has a flat file system.

  • Gen2 provides better performance, scalability, and security compared to Gen1.

  • Gen2 supports Azure Data Lake Storage features like tiering, lifecycle management, and access control lists (ACLs).

  • Gen2 allows direct access to data ...read more

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Q57. What are linked service What is data set Function vs SP

Ans.

Linked services are connections to external data sources in Azure Data Factory. Data sets are representations of data in those sources. Functions and stored procedures are used for data transformation.

  • Linked services are connections to external data sources such as databases, file systems, or APIs.

  • Data sets are representations of data in those sources, specifying the location, format, and schema of the data.

  • Functions are reusable code snippets used for data transformation and...read more

Q58. What are the control flow activites in adf

Ans.

Control flow activities in Azure Data Factory (ADF) are used to define the workflow and execution order of activities.

  • Control flow activities are used to manage the flow of data and control the execution order of activities in ADF.

  • They allow you to define dependencies between activities and specify conditions for their execution.

  • Some commonly used control flow activities in ADF are If Condition, For Each, Until, and Switch.

  • If Condition activity allows you to define conditiona...read more

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Q59. How do you write stored procedures in databricks?

Ans.

Stored procedures in Databricks can be written using SQL or Python.

  • Use %sql magic command to write SQL stored procedures

  • Use %python magic command to write Python stored procedures

  • Stored procedures can be saved and executed in Databricks notebooks

Q60. Find the student with marks greater than 80 in all subjects

Ans.

Filter students with marks greater than 80 in all subjects

  • Iterate through each student's marks in all subjects

  • Check if all marks are greater than 80 for a student

  • Return the student if all marks are greater than 80

Q61. Write the syntax to define the schema of a file for loading.

Ans.

Syntax to define schema of a file for loading

  • Use CREATE EXTERNAL TABLE statement in SQL

  • Specify column names and data types in the schema definition

  • Example: CREATE EXTERNAL TABLE MyTable (col1 INT, col2 STRING) USING CSV

Q62. What is incremental load. What is partition and bucketing. Spark archtecture

Ans.

Incremental load is the process of loading only new or updated data into a data warehouse, rather than reloading all data each time.

  • Incremental load helps in reducing the time and resources required for data processing.

  • It involves identifying new or updated data since the last load and merging it with the existing data.

  • Common techniques for incremental load include using timestamps or change data capture (CDC) mechanisms.

  • Example: Loading only new sales transactions into a dat...read more

Q63. Advanced SQL questions - highest sales from each city

Ans.

Use window functions like ROW_NUMBER() to find highest sales from each city in SQL.

  • Use PARTITION BY clause in ROW_NUMBER() to partition data by city

  • Order the data by sales in descending order

  • Filter the results to only include rows with row number 1

Q64. What are the types of transformation?

Ans.

Types of transformations include filtering, sorting, aggregating, joining, and pivoting.

  • Filtering: Selecting a subset of rows based on certain criteria.

  • Sorting: Arranging rows in a specific order based on one or more columns.

  • Aggregating: Combining multiple rows into a single result, such as summing or averaging values.

  • Joining: Combining data from multiple sources based on a common key.

  • Pivoting: Restructuring data from rows to columns or vice versa.

Q65. how to find max value and min value in PySpark

Ans.

Use the agg() function with max() and min() functions to find the maximum and minimum values in PySpark.

  • Use the agg() function with max() and min() functions on the DataFrame to find the maximum and minimum values.

  • Example: df.agg({'column_name': 'max'}).show() to find the maximum value in a specific column.

  • Example: df.agg({'column_name': 'min'}).show() to find the minimum value in a specific column.

Q66. SQL query and difference between rank,dense rank and row number

Ans.

Rank, dense rank, and row number are SQL functions used to assign a unique sequential number to rows in a result set.

  • Rank function assigns a unique number to each row based on the ordering specified in the query.

  • Dense rank function also assigns a unique number to each row, but it does not leave gaps in the ranking sequence.

  • Row number function simply assigns a sequential number to each row in the result set, without any consideration of the order.

Q67. what is the difference between set and tuple

Ans.

Sets are unordered collections of unique elements, while tuples are ordered collections of elements that can be of different data types.

  • Sets do not allow duplicate elements, while tuples can have duplicate elements.

  • Sets are mutable and can be modified after creation, while tuples are immutable and cannot be changed once created.

  • Sets are defined using curly braces {}, while tuples are defined using parentheses ().

  • Example of a set: {1, 2, 3, 4}

  • Example of a tuple: (1, 'apple', T...read more

Q68. How do you normalize your Json data

Ans.

Json data normalization involves structuring data to eliminate redundancy and improve efficiency.

  • Identify repeating groups of data

  • Create separate tables for each group

  • Establish relationships between tables using foreign keys

  • Eliminate redundant data by referencing shared values

Q69. How you migrated oracle data into azure?

Ans.

I migrated Oracle data into Azure using Azure Data Factory and Azure Database Migration Service.

  • Used Azure Data Factory to create pipelines for data migration

  • Utilized Azure Database Migration Service for schema and data migration

  • Ensured data consistency and integrity during the migration process

Q70. How do you optimize pyspark jobs?

Ans.

Optimizing pyspark jobs involves tuning configurations, partitioning data, caching, and using efficient transformations.

  • Tune configurations such as executor memory, number of executors, and parallelism to optimize performance.

  • Partition data properly to distribute workload evenly and avoid shuffling.

  • Cache intermediate results to avoid recomputation.

  • Use efficient transformations like map, filter, and reduceByKey instead of costly operations like groupByKey.

  • Optimize joins by bro...read more

Q71. Architecuture of Cloud and various tools / Technologies

Ans.

Cloud architecture involves various tools and technologies for data engineering, such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.

  • Azure Data Factory is used for data integration and orchestration.

  • Azure Databricks is a unified analytics platform for big data and AI.

  • Azure Synapse Analytics combines big data and data warehousing for real-time analytics.

Q72. Difference between dataframe and rdd

Ans.

Dataframe is a distributed collection of data organized into named columns while RDD is a distributed collection of data organized into partitions.

  • Dataframe is immutable while RDD is mutable

  • Dataframe has a schema while RDD does not

  • Dataframe is optimized for structured and semi-structured data while RDD is optimized for unstructured data

  • Dataframe has better performance than RDD due to its optimized execution engine

  • Dataframe supports SQL queries while RDD does not

Q73. Difference between olap and oltp

Ans.

OLAP is for analytics and reporting while OLTP is for transaction processing.

  • OLAP stands for Online Analytical Processing

  • OLTP stands for Online Transaction Processing

  • OLAP is used for complex queries and data analysis

  • OLTP is used for real-time transaction processing

  • OLAP databases are read-intensive while OLTP databases are write-intensive

  • Examples of OLAP databases include data warehouses and data marts

  • Examples of OLTP databases include banking systems and e-commerce websites

Q74. What are the types of IR

Ans.

IR stands for Integration Runtime. There are two types of IR: Self-hosted and Azure-SSIS.

  • Self-hosted IR is used to connect to on-premises data sources.

  • Azure-SSIS IR is used to run SSIS packages in Azure Data Factory.

  • Self-hosted IR requires an on-premises machine to be installed and configured.

  • Azure-SSIS IR is a fully managed service provided by Azure.

  • Both types of IR enable data movement and transformation in Azure Data Factory.

Q75. What is Autoloader in Databricks?

Ans.

Autoloader in Databricks is a feature that automatically loads new data files as they arrive in a specified directory.

  • Autoloader monitors a specified directory for new data files and loads them into a Databricks table.

  • It supports various file formats such as CSV, JSON, Parquet, Avro, and ORC.

  • Autoloader simplifies the process of ingesting streaming data into Databricks without the need for manual intervention.

  • It can be configured to handle schema evolution and data partitionin...read more

Q76. SCD Types and how you implement it

Ans.

SCD Types are Slowly Changing Dimensions used to track historical data changes in a data warehouse.

  • SCD Type 1: Overwrite old data with new data, losing historical information.

  • SCD Type 2: Create new records for each change, maintaining historical data.

  • SCD Type 3: Add columns to track changes, keeping both old and new data in the same record.

Q77. How to pass parameters form ADF to ADB

Ans.

Parameters can be passed from Azure Data Factory (ADF) to Azure Data Factory (ADB) using linked services and datasets.

  • Create a linked service in ADF to connect to ADB

  • Define parameters in ADF pipeline and pass them to ADB activities

  • Use dynamic content expressions to pass parameters between ADF and ADB

  • Example: Pass a parameter for a SQL query in ADB from ADF pipeline

Q78. What is SCD and there types?

Ans.

SCD stands for Slowly Changing Dimension. There are three types: Type 1, Type 2, and Type 3.

  • SCD is used in data warehousing to track changes in dimension data over time.

  • Type 1 SCD overwrites old data with new data, losing historical information.

  • Type 2 SCD creates new records for each change, preserving historical data.

  • Type 3 SCD keeps both old and new data in the same record, with separate columns for each version.

Q79. What is catalyst optimiser in Spark

Ans.

Catalyst optimizer is a query optimizer in Apache Spark that leverages advanced techniques to optimize and improve the performance of Spark SQL queries.

  • Catalyst optimizer uses a rule-based and cost-based optimization approach to generate an optimized query plan.

  • It performs various optimizations such as constant folding, predicate pushdown, and projection pruning to improve query performance.

  • Catalyst optimizer also leverages advanced techniques like query plan caching and code...read more

Q80. how to do performance tuning in adf

Ans.

Performance tuning in Azure Data Factory involves optimizing data flows and activities to improve efficiency and reduce processing time.

  • Identify bottlenecks in data flows and activities

  • Optimize data partitioning and distribution

  • Use appropriate data integration patterns

  • Leverage caching and parallel processing

  • Monitor and analyze performance metrics

Q81. What is Azure synapse architecture?

Ans.

Azure Synapse is a cloud-based analytics service that brings together big data and data warehousing.

  • Azure Synapse integrates big data and data warehousing capabilities in a single service

  • It allows for data ingestion, preparation, management, and serving for BI and machine learning

  • Supports both serverless and provisioned resources for data processing

  • Offers integration with Azure Machine Learning, Power BI, and Azure Data Factory

Q82. What is Driver node and Executors?

Ans.

Driver node is the node in Spark that manages the execution of a Spark application, while Executors are the nodes that actually perform the computation.

  • Driver node coordinates tasks and schedules work across Executors

  • Executors are responsible for executing tasks assigned by the Driver node

  • Driver node maintains information about the Spark application and distributes tasks to Executors

  • Executors run computations and store data for tasks

Q83. How do you perform Partitioning

Ans.

Partitioning in Azure Data Engineer involves dividing data into smaller chunks for better performance and manageability.

  • Partitioning can be done based on a specific column or key in the dataset

  • It helps in distributing data across multiple nodes for parallel processing

  • Partitioning can improve query performance by reducing the amount of data that needs to be scanned

  • In Azure Synapse Analytics, you can use ROUND_ROBIN or HASH distribution for partitioning

Q84. How to mask data in azure

Ans.

Data masking in Azure helps protect sensitive information by replacing original data with fictitious data.

  • Use Dynamic Data Masking in Azure SQL Database to obfuscate sensitive data in real-time

  • Leverage Azure Purview to discover, classify, and mask sensitive data across various data sources

  • Implement Azure Data Factory to transform and mask data during ETL processes

  • Utilize Azure Information Protection to apply encryption and access controls to sensitive data

Q85. Project Architecture, spark transformations used?

Ans.

The project architecture includes Spark transformations for processing large volumes of data.

  • Spark transformations are used to manipulate data in distributed computing environments.

  • Examples of Spark transformations include map, filter, reduceByKey, join, etc.

Q86. what is partion key?

Ans.

Partition key is a field used to distribute data across multiple partitions in a database for scalability and performance.

  • Partition key determines the partition in which a row will be stored in a database.

  • It helps in distributing data evenly across multiple partitions to improve query performance.

  • Choosing the right partition key is crucial for efficient data storage and retrieval.

  • For example, in Azure Cosmos DB, partition key can be a property like 'customerId' or 'date'.

Q87. What is the data flow of databricks

Ans.

Data flow in Databricks involves reading data from various sources, processing it using Spark, and storing the results in different formats.

  • Data is read from sources like Azure Data Lake Storage, Azure Blob Storage, or databases

  • Data is processed using Apache Spark clusters in Databricks

  • Results can be stored in various formats like Parquet, Delta Lake, or SQL tables

Q88. Why is spark a lazy execution

Ans.

Spark is lazy execution to optimize performance by delaying computation until necessary.

  • Spark delays execution until an action is called to optimize performance.

  • This allows Spark to optimize the execution plan and minimize unnecessary computations.

  • Lazy evaluation helps in reducing unnecessary data shuffling and processing.

  • Example: Transformations like map, filter, and reduce are not executed until an action like collect or saveAsTextFile is called.

Q89. What is linked services in adf

Ans.

Linked services in ADF are connections to external data sources or destinations that allow data movement and transformation.

  • Linked services are used to connect to various data sources such as databases, file systems, and cloud services.

  • They provide the necessary information and credentials to establish a connection.

  • Linked services enable data movement activities like copying data from one source to another or transforming data during the movement process.

  • Examples of linked se...read more

Q90. What is Azure data lake gen2?

Ans.

Azure Data Lake Gen2 is a scalable and secure cloud-based storage solution for big data analytics.

  • Combines the scalability of Azure Blob Storage with the hierarchical file system of Azure Data Lake Storage Gen1

  • Supports both structured and unstructured data

  • Provides high throughput and low latency access to data

  • Offers advanced security features like encryption and access control

  • Integrates with various Azure services like Azure Databricks and Azure HDInsight

Q91. What is azure data factory

Ans.

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.

  • Azure Data Factory is used to move and transform data from various sources to destinations.

  • It supports data integration and orchestration of workflows.

  • You can monitor and manage data pipelines using Azure Data Factory.

  • It provides a visual interface for designing and monitoring data pipelines.

  • Azure Data Factory can be used for data migration, data warehou...read more

Q92. What is Azure data lake

Ans.

Azure Data Lake is a scalable data storage and analytics service provided by Microsoft Azure.

  • Azure Data Lake Store is a secure data repository that allows you to store and analyze petabytes of data.

  • Azure Data Lake Analytics is a distributed analytics service that can process big data using Apache Hadoop and Apache Spark.

  • It is designed for big data processing and analytics tasks, providing high performance and scalability.

Q93. What is index in table

Ans.

An index in a table is a data structure that improves the speed of data retrieval operations on a database table.

  • Indexes are used to quickly locate data without having to search every row in a table.

  • They can be created on one or more columns in a table.

  • Examples of indexes include primary keys, unique constraints, and non-unique indexes.

Q94. 4. Do you know pyspark?

Ans.

Yes, pyspark is a Python API for Apache Spark, used for big data processing and analytics.

  • pyspark is a Python API for Apache Spark, allowing users to write Spark applications using Python.

  • It provides high-level APIs in Python for Spark's functionality, making it easier to work with big data.

  • pyspark is commonly used for data processing, machine learning, and analytics tasks.

  • Example: Using pyspark to read data from a CSV file, perform transformations, and store the results in a...read more

Q95. Databricks - how to mount?

Ans.

Databricks can be mounted using the Databricks CLI or the Databricks REST API.

  • Use the Databricks CLI command 'databricks fs mount' to mount a storage account to a Databricks workspace.

  • Alternatively, you can use the Databricks REST API to programmatically mount storage.

Q96. What is tumbling window trigger

Ans.

Tumbling window trigger is a type of trigger in Azure Data Factory that defines a fixed-size window of time for data processing.

  • Tumbling window trigger divides data into fixed-size time intervals for processing

  • It is useful for scenarios where data needs to be processed in regular intervals

  • Example: Triggering a pipeline every hour to process data for the past hour

Q97. Difference between azure Iaas and Paas

Ans.

IaaS provides virtualized infrastructure resources, while PaaS offers a platform for developing, testing, and managing applications.

  • IaaS allows users to rent virtualized hardware resources like virtual machines, storage, and networking, while PaaS provides a platform for developers to build, deploy, and manage applications without worrying about the underlying infrastructure.

  • In IaaS, users have more control over the operating system, applications, and data, while in PaaS, the...read more

Q98. Types of cluster in data bricks??

Ans.

Types of clusters in Databricks include Standard, High Concurrency, and Single Node clusters.

  • Standard cluster: Suitable for running single jobs or workflows.

  • High Concurrency cluster: Designed for multiple users running concurrent jobs.

  • Single Node cluster: Used for development and testing purposes.

Q99. what is IR in adf pipe line

Ans.

IR in ADF pipeline stands for Integration Runtime, which is a compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.

  • IR in ADF pipeline is responsible for executing activities within the pipeline.

  • It can be configured to run in different modes such as Azure, Self-hosted, and SSIS.

  • Integration Runtime allows data movement between on-premises and cloud data stores.

  • It provides secure connectivity and data en...read more

Q100. What is a catalyst optimizer?

Ans.

The catalyst optimizer is a query optimization engine in Apache Spark that improves performance by generating optimized query plans.

  • It is a query optimization engine in Apache Spark.

  • It improves performance by generating optimized query plans.

  • It uses rule-based and cost-based optimization techniques.

  • It leverages advanced techniques like code generation and adaptive query execution.

  • Example: Catalyst optimizer in Spark SQL analyzes the query and generates an optimized query plan...read more

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