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

Databricks enhances data processing with advanced analytics, collaboration, and scalability beyond ADF's capabilities.

• Databricks provides a collaborative environment for data scientists and engineers to work together using notebooks.

• It supports advanced analytics and machine learning workflows, which ADF lacks natively.

• Databricks can handle large-scale data processing with Apache Spark, making it more efficient for big data tasks.

• With Databricks, you can leverage Delta Lake f...read more

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

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

Query to find customer names with the maximum orders from Customers and Orders tables.

• Use JOIN to combine Customers and Orders tables on CustomerID.

• Group by CustomerID and count orders to find the maximum.

• Use a subquery to filter customers with the maximum order count.

• Example SQL: SELECT c.customerName FROM Customers c JOIN Orders o ON c.customerID = o.CustomerID GROUP BY c.customerID HAVING COUNT(o.OrderId) = (SELECT MAX(orderCount) FROM (SELECT COUNT(OrderId) AS orderCount ...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.

Optimize data processing by partitioning, indexing, and using efficient storage formats.

• Partitioning: Divide large datasets into smaller, manageable chunks. For example, partitioning a sales dataset by year.

• Indexing: Create indexes on frequently queried columns to speed up data retrieval. For instance, indexing customer IDs in a transaction table.

• Data Compression: Use compressed formats like Parquet or ORC to reduce storage size and improve I/O performance.

• Caching: Implement ...read more

ADF key components include pipelines, activities, datasets, triggers, and linked services.

• Pipelines - logical grouping of activities

• Activities - individual tasks within a pipeline

• Datasets - data sources and destinations

• Triggers - event-based or time-based execution of pipelines

• Linked Services - connections to external data sources

• Examples: Copy Data activity, Lookup activity, Blob Storage dataset

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.

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

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

Copy activity in ADF is used to move data from source to destination.

• Copy activity supports various sources and destinations such as Azure Blob Storage, Azure SQL Database, etc.

• It can be used for both one-time and scheduled data movement.

• It supports mapping data between source and destination using mapping data flows.

• Slowly changing dimensions can be handled using copy activity in ADF.

• Copy activity is commonly used in data warehousing scenarios.

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

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

Use Slowly Changing Dimensions (SCD) to preserve historical data while reconstructing a table.

• Implement SCD Type 1 for overwriting old data without keeping history.

• Use SCD Type 2 to create new records for changes, preserving history.

• Example of SCD Type 2: If a customer's address changes, add a new record with the new address and mark the old record as inactive.

• SCD Type 3 allows for limited history by adding new columns for previous values.

• Choose the appropriate SCD type based...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

Create a data pipeline using Azure Data Factory to move data from ADLS to SQL Database.

• 1. Set up Azure Data Factory (ADF) instance in your Azure portal.

• 2. Create linked services for both Azure Data Lake Storage (ADLS) and SQL Database.

• 3. Define datasets for the source (ADLS) and destination (SQL Database).

• 4. Create a pipeline in ADF and add a copy activity to move data.

• 5. Configure the copy activity to specify source and destination datasets.

• 6. Set up triggers for scheduling ...read more

Use Azure Data Factory (ADF) to filter, join, and aggregate data through data flows and pipelines.

• Utilize Data Flows in ADF for data transformation tasks.

• Use the Filter transformation to remove unwanted rows based on conditions.

• Employ the Join transformation to combine datasets on common keys, e.g., joining patient records with treatment data.

• Implement Aggregate transformation to summarize data, such as calculating average treatment costs per patient.

• Schedule ADF pipelines to...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.

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

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

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

To find the fourth highest salary from an employee table using SQL, we can use various methods like subqueries or the DISTINCT clause.

• Use the DISTINCT keyword to get unique salaries: SELECT DISTINCT salary FROM employees ORDER BY salary DESC LIMIT 1 OFFSET 3;

• Alternatively, use a subquery: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees)));

• Ano...read more

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

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