100+ Azure Data Engineer Interview Questions and Answers

You can load multiple tables at a time using Azure Data Factory by creating a single pipeline with multiple copy activities.

• Create a pipeline in Azure Data Factory

• Add multiple copy activities to the pipeline, each copy activity for loading data from one table

• Configure each copy activity to load data from a different table

• Run the pipeline to load data from all tables simultaneously

RDD, Dataframe and Dataset are data structures in Spark. RDD is a low-level structure, Dataframe is tabular and Dataset is a combination of both.

• RDD stands for Resilient Distributed Datasets and is a low-level structure in Spark that is immutable and fault-tolerant.

• Dataframe is a tabular structure with named columns and is similar to a table in a relational database.

• Dataset is a combination of RDD and Dataframe and provides type-safety and object-oriented programming features...read more

Incremental load is a process of updating only the new or changed data in a target system. Other types of loads include full load and delta load.

• Incremental load reduces the time and resources required to update the target system.

• Full load loads all the data from the source system to the target system.

• Delta load loads only the changed data from the source system to the target system.

• In ADF pipeline, incremental load can be implemented using watermarking or change data capture...read more

Get metadata activity is used to retrieve metadata of a specified data store or dataset in Azure Data Factory.

• Get metadata activity is used in Azure Data Factory to retrieve metadata of a specified data store or dataset.

• Parameters to pass include dataset, linked service, and optional folder path.

• The output of the activity includes information like schema, size, last modified timestamp, etc.

• Example: Get metadata of a SQL Server table using a linked service to the database.

You can monitor the child pipeline in the master pipeline by using Azure Monitor or Azure Data Factory monitoring tools.

• Use Azure Monitor to track the performance and health of the child pipeline within the master pipeline.

• Leverage Azure Data Factory monitoring tools to view detailed logs and metrics for the child pipeline execution.

• Set up alerts and notifications to be informed of any issues or failures in the child pipeline.

Designing an effective ADF pipeline involves considering various metrics and factors.

• Understand the data sources and destinations

• Identify the dependencies between activities

• Optimize data movement and processing for performance

• Monitor and track pipeline execution for troubleshooting

• Consider security and compliance requirements

• Use parameterization and dynamic content for flexibility

• Implement error handling and retries for robustness

The output after inner join of table 1 and table 2 will be 2,3,5.

• Inner join only includes rows that have matching values in both tables.

• Values 2, 3, and 5 are present in both tables, so they will be included in the output.

• Null values are not considered as matching values in inner join.

Use Azure Data Factory to move data from on-premise Oracle server to Azure cloud.

• Create a linked service to connect to the on-premise Oracle server

• Create a linked service to connect to the Azure cloud storage

• Create a pipeline with a copy activity to move data from on-premise to cloud

• Schedule the pipeline to run daily

• Monitor the pipeline for any errors or issues

Answering questions related to Azure Data Engineer interview

• To read parquet file, use PyArrow or Pandas library

• To call notebook from ADF, use Notebook activity in ADF pipeline

• For Azure DevOps CI/CD process, use Azure Pipelines

• System variables in ADF can be accessed using expressions like @pipeline().RunId

Yes, encryption keys can be created in Databricks. Cluster size can be adjusted based on workload.

• Encryption keys can be created using Azure Key Vault or Databricks secrets

• Cluster size can be adjusted manually or using autoscaling based on workload

• Encryption at rest can also be enabled for data stored in Databricks

Blob is a storage service for unstructured data, while ADLS is a distributed file system for big data analytics.

• Blob is a general-purpose object storage service for unstructured data, while ADLS is optimized for big data analytics workloads.

• Blob storage is suitable for storing large amounts of data, such as images, videos, and logs, while ADLS is designed for processing large datasets in parallel.

• ADLS offers features like hierarchical namespace, POSIX-compliant file system se...read more

Yes, I am familiar with Databricks and have been working on it for the past 2 years.

• I have been using Databricks for data engineering tasks such as data processing, data transformation, and data visualization.

• I have experience in building and optimizing data pipelines using Databricks.

• I have worked on collaborative projects with team members using Databricks notebooks.

• I have utilized Databricks for big data processing and analysis, leveraging its scalability and performance c...read more

To connect data bricks with storage account, you need to create a storage account and configure it in the data bricks workspace.

• Create a storage account in Azure portal

• Get the connection string of the storage account

• In the data bricks workspace, go to 'Storage' and click on 'Create'

• Select the storage account type and provide the connection string

• Test the connection and save the configuration

Designing/implementing database solutions in the cloud involves selecting appropriate cloud service, data modeling, security, and scalability.

• Select the appropriate cloud service (e.g. Azure SQL Database, Cosmos DB, etc.) based on the requirements of the application

• Design the data model to optimize for the cloud environment (e.g. denormalization, partitioning, etc.)

• Implement security measures such as encryption, access control, and auditing

• Ensure scalability by using features...read more

There are three types of triggers available in Azure Data Factory: Schedule, Tumbling Window, and Event.

• Schedule trigger: Runs pipelines on a specified schedule.

• Tumbling Window trigger: Runs pipelines at specified time intervals.

• Event trigger: Runs pipelines in response to events like a file being added to a storage account.

Migrating to the cloud offers numerous benefits such as cost savings, scalability, and improved security.

• Highlight the cost savings that can be achieved by migrating to the cloud, as clients can avoid upfront infrastructure costs and pay only for the resources they use.

• Emphasize the scalability of cloud services, allowing clients to easily scale up or down based on their needs without the need for additional hardware investments.

• Discuss the improved security measures provided...read more

Choose a cluster based on data size, complexity, and processing requirements.

• Consider the size and complexity of the data to be processed.

• Determine the processing requirements, such as batch or real-time processing.

• Choose a cluster with appropriate resources, such as CPU, memory, and storage.

• Examples of Azure clusters include HDInsight, Databricks, and Synapse Analytics.

To create mount points in ADLS, use the Azure Storage Explorer or Azure Portal. To load data source, use Azure Data Factory or Azure Databricks.

• Mount points can be created using Azure Storage Explorer or Azure Portal

• To load data source, use Azure Data Factory or Azure Databricks

• Mount points allow you to access data in ADLS as if it were a local file system

• Data can be loaded into ADLS using various tools such as Azure Data Factory, Azure Databricks, or Azure HDInsight

SQL query to fetch customers who have not transacted in last 30 days but did before

• Use a subquery to filter customers who transacted before 30 days

• Use NOT IN or NOT EXISTS to exclude customers who transacted in last 30 days

SQL inner and left join can be used to combine tables with duplicate values based on specified conditions.

• Use INNER JOIN to return rows from both tables that have matching values

• Use LEFT JOIN to return all rows from the left table and the matched rows from the right table

• Handle duplicate values by using DISTINCT or GROUP BY clauses

Reading files in notebook, configuring data, using ADF trigger, parquet format, window functions vs group by, reading CSV file and storing in parquet, dataset vs dataframe, transformations, delta lake

• To read files in notebook, use libraries like pandas or pyspark

• Configuration needed includes specifying file path, format, and any additional options

• ADF trigger can be used for automated data processing, but may not be necessary for all scenarios

• Parquet format is a columnar stora...read more

Distributed table in Synapse is a table that is distributed across multiple nodes for parallel processing.

• Distributed tables in Synapse are divided into distributions to optimize query performance.

• There are three distribution types: Hash distribution, Round-robin distribution, and Replicate distribution.

• Hash distribution is ideal for joining large tables on a common key, Round-robin distribution evenly distributes data, and Replicate distribution duplicates data on all nodes....read more

ADLS is Azure Data Lake Storage, a scalable and secure data lake solution. ADLS gen2 is an improved version of gen1.

• ADLS is a cloud-based storage solution for big data analytics workloads

• ADLS gen1 is based on Hadoop Distributed File System (HDFS) and has limitations in terms of scalability and performance

• ADLS gen2 is built on Azure Blob Storage and offers improved performance, scalability, and security features

• ADLS gen2 supports hierarchical namespace, which enables efficient...read more

Accumulators are variables used for aggregating data in Spark. GroupByKey and ReduceByKey are operations used for data transformation.

• Accumulators are used to accumulate values across multiple tasks in a distributed environment.

• GroupByKey is used to group data based on a key and create a pair of key-value pairs.

• ReduceByKey is used to aggregate data based on a key and reduce the data to a single value.

• GroupByKey is less efficient than ReduceByKey as it shuffles all the data ac...read more

CTE is used to create temporary result sets, stored procedures are reusable blocks of code, left outer join combines rows from two tables based on a related column

• CTE (Common Table Expression) is used to create temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement.

• Stored Procedures are reusable blocks of code that can be executed with a single call. They can accept input parameters and return output parameters.

• Left Outer Join combin...read more

I have applied optimization techniques like partitioning, caching, and cluster sizing in Databricks projects.

• Utilized partitioning to improve query performance by limiting the amount of data scanned

• Implemented caching to store frequently accessed data in memory for faster retrieval

• Adjusted cluster sizing based on workload requirements to optimize cost and performance

Dynamic Content in ADF allows for dynamic values to be passed between activities in Azure Data Factory.

• Dynamic Content can be used to pass values between activities, such as passing output from one activity as input to another.

• Expressions can be used within Dynamic Content to manipulate data or create dynamic values.

• Dynamic Content can be used in various ADF components like datasets, linked services, and activities.

• For example, in a pipeline, you can use Dynamic Content to pa...read more

We should use the Self-hosted Integration Runtime (IR) to copy data from on-premise db to Azure.

• Self-hosted IR allows data movement between on-premise and Azure

• It is installed on a local machine or virtual machine in the on-premise network

• Self-hosted IR securely connects to the on-premise data source and transfers data to Azure

• It supports various data sources like SQL Server, Oracle, MySQL, etc.

• Self-hosted IR can be managed and monitored through Azure Data Factory

Serialization is the process of converting an object into a stream of bytes for storage or transmission.

• Serialization is used to transfer objects between different applications or systems.

• It allows objects to be stored in a file or database.

• Serialization can be used for caching and improving performance.

• Examples of serialization formats include JSON, XML, and binary formats like Protocol Buffers and Apache Avro.

Spark architecture is a distributed computing framework that processes large datasets in parallel across a cluster of nodes.

• Spark has a master-slave architecture with a driver program that communicates with the cluster manager to allocate resources and tasks to worker nodes.

• Worker nodes execute tasks in parallel and store data in memory or disk.

• Spark supports various data sources and APIs for batch processing, streaming, machine learning, and graph processing.

• Azure Databricks...read more

Data bricks is a unified analytics platform for big data and machine learning, while ADF (Azure Data Factory) is a cloud-based data integration service.

• Data bricks is a unified analytics platform that provides a collaborative environment for big data and machine learning projects.

• ADF is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.

• Data bricks supports multiple programming languages like Python, Scala, and SQL, while ADF...read more

IF Else activity can be implemented using the Switch activity in Azure Data Factory.

• Create a Switch activity in your pipeline

• Define the condition in the expression field

• Add cases for each condition with corresponding activities

• Add a default activity for cases that do not match any condition

Yes, I have worked on real-time data processing projects using technologies like Apache Kafka and Spark Streaming.

• Implemented real-time data pipelines using Apache Kafka for streaming data ingestion

• Utilized Spark Streaming for processing and analyzing real-time data

• Worked on monitoring and optimizing the performance of real-time data processing systems

You can load data from Databricks to Synapse using PolyBase or Azure Data Factory.

• Use PolyBase to load data from Databricks to Synapse by creating an external table in Synapse pointing to the Databricks data location.

• Alternatively, use Azure Data Factory to copy data from Databricks to Synapse by creating a pipeline with Databricks as source and Synapse as destination.

• Ensure proper permissions and connectivity between Databricks and Synapse for data transfer.

Delta load in ADF is achieved by comparing source and target data and only loading the changed data.

• Use a Lookup activity to retrieve the latest watermark or timestamp from the target table

• Use a Source activity to extract data from the source system based on the watermark or timestamp

• Use a Join activity to compare the source and target data and identify the changed records

• Use a Sink activity to load only the changed records into the target table

ADLS gen 2 is an upgrade to gen 1 with improved performance, scalability, and security features.

• ADLS gen 2 is built on top of Azure Blob Storage, while gen 1 is a standalone service.

• ADLS gen 2 supports hierarchical namespace, which allows for better organization and management of data.

• ADLS gen 2 has better performance for large-scale analytics workloads, with faster read and write speeds.

• ADLS gen 2 has improved security features, including encryption at rest and in transit.

• AD...read more

Blob is a storage service for unstructured data, while ADLS is optimized for big data analytics workloads.

• Blob is a general-purpose object storage service for unstructured data, while ADLS is optimized for big data analytics workloads.

• ADLS offers features like file system semantics, file-level security, and scalability for big data analytics, while Blob storage is simpler and more cost-effective for general storage needs.

• ADLS is designed for big data processing frameworks lik...read more

As an Azure Data Engineer, my current responsibilities include designing and implementing data solutions on Azure, optimizing data storage and processing, and ensuring data security and compliance.

• Designing and implementing data solutions on Azure

• Optimizing data storage and processing for performance and cost efficiency

• Ensuring data security and compliance with regulations

• Collaborating with data scientists and analysts to support their data needs

Semantic layer is a virtual layer that provides a simplified view of complex data.

• It acts as a bridge between the physical data and the end-user.

• It provides a common business language for users to access data.

• It simplifies data access by hiding the complexity of the underlying data sources.

• Examples include OLAP cubes, data marts, and virtual tables.

Scheduled trigger is time-based while tumbling window trigger is data-based.

• Scheduled trigger is based on a specific time or interval, such as every hour or every day.

• Tumbling window trigger is based on the arrival of new data or a specific event.

• Scheduled trigger is useful for regular data processing tasks, like ETL jobs.

• Tumbling window trigger is useful for aggregating data over fixed time intervals.

• Scheduled trigger can be set to run at a specific time, while tumbling wind...read more

Yes, I have worked on developing a Data Validation Framework to ensure data accuracy and consistency.

• Developed automated data validation scripts to check for data accuracy and consistency

• Implemented data quality checks to identify and resolve data issues

• Utilized tools like SQL queries, Python scripts, and Azure Data Factory for data validation

• Worked closely with data stakeholders to define validation rules and requirements

A data warehouse is a structured repository for storing and analyzing structured data, while a data lake is a centralized repository for storing and analyzing structured, semi-structured, and unstructured data.

• Data Warehouse: Stores structured data, follows a schema, optimized for querying and analysis.

• Data Lake: Stores structured, semi-structured, and unstructured data, schema-on-read, supports exploratory analysis.

• Data Warehouse: Data is typically transformed and loaded fro...read more

Window functions are used to perform calculations across a set of rows in a table.

• Window functions are used to calculate values based on a subset of rows within a table

• They are used to perform calculations such as running totals, ranking, and moving averages

• Examples of window functions include ROW_NUMBER(), RANK(), and SUM() OVER()

Set up ETL flow for data in Lake House using Databricks

• Connect Databricks to Lake House storage (e.g. Azure Data Lake Storage)

• Define ETL process using Databricks notebooks or jobs

• Extract data from Lake House, transform as needed, and load into target destination

• Monitor and schedule ETL jobs for automated data processing

SQL query to fetch Top 3 revenue generating Products from Sales table

• Use the SELECT statement to retrieve data from the Sales table

• Use the GROUP BY clause to group the data by Product

• Use the ORDER BY clause to sort the revenue in descending order

• Use the LIMIT clause to fetch only the top 3 revenue generating Products

Encountered a data corruption issue in Azure Data Lake Storage and resolved it by restoring from a backup.

• Identified the corrupted files by analyzing error logs and data inconsistencies

• Restored the affected data from the latest backup available

• Implemented preventive measures such as regular data integrity checks and backups

• Collaborated with the Azure support team to investigate the root cause

Use Azure Data Factory to copy multiple tables from on-premises to Azure Blob Storage

• Create a linked service to connect to the on-premises data source

• Create datasets for each table to be copied

• Create a pipeline with copy activities for each table

• Use Azure Blob Storage as the sink for the copied tables