Here, we will discuss Azure Data Engineer Interview Questions that interviewers ask in Data Engineer job positions.
Table of Contents
1. What is an Azure Data Engineer?
An Azure Data Engineer designs, implements, and manages data solutions on Microsoft Azure, using services like Azure Data Factory, Synapse Analytics, Databricks, and SQL Database.
They ensure efficient data pipelines, ETL processes, and data storage for analytics and AI.
2. Azure Data Engineer Interview Topics
- Data Pipelines: How do you design a scalable pipeline in Azure Data Factory?
- ETL vs. ELT: Explain differences and when to use each.
- Partitioning: How do you optimize Delta Lake tables in Databricks?
- Security: How do you implement data masking in Azure SQL?
- Performance Tuning: How do you handle slow-running Spark jobs in Synapse?
Azure Data Engineer Interview Questions
1. Explain parameters and variables in ADF.
In Azure Data Factory (ADF), parameters are used to pass external values into pipelines, datasets, and linked services at runtime, allowing for dynamic configurations.
Variables are used within a pipeline to store and manipulate data during the execution of activities, enabling conditional logic and iteration.
2. Difference between lookup and get metadata activity?
The Lookup activity in ADF is used to retrieve a single row or a set of rows from a dataset, typically for use in subsequent activities.
The Get Metadata activity is used to extract metadata information about a dataset, such as file names, size, and structure, which can be used for conditional logic or validation.
3. What are the different kinds of joins available in PYSPARK?
PySpark supports various types of joins, including inner join, left outer join, right outer join, full outer join, and cross join. These joins allow combining datasets based on common keys.
4. How will you implement parallel processing activity in ADF?
To implement parallel processing in ADF, you can use the “ForEach” activity with the “Batch Count” property set to a value greater than one.
This allows multiple iterations of activities to run concurrently, improving performance and reducing execution time.
5. How to resume the pipeline from exactly the failed activity?
In ADF, you can resume a pipeline from a failed activity by enabling the “Continue on Failure” option or by using the “Retry” policy. Additionally, using the “Checkpoints” feature allows the pipeline to restart from the point of failure rather than from the beginning.
6. Difference between partitioning and bucketing?
Partitioning in data processing divides data into segments based on column values, which speeds up query performance by reducing the data scanned.
Bucketing distributes data into fixed-size buckets based on a hash of a column, optimizing joins and aggregations.
7. What is Delta Lake? What are the key features of Delta Lake? How to create Delta Lake tables?
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
Key features include support for ACID transactions, scalable metadata handling, and schema enforcement.
Delta Lake tables are created by specifying the format as “delta” when saving data using Spark.
8. What are workflows in DataBricks?
Workflows in Databricks allow you to create, manage, and schedule jobs, which can include running notebooks, JARs, or Python scripts. This facilitates the automation of complex data engineering and data science tasks.
9. What is time travel in data bricks?
Time travel in Databricks, a feature of Delta Lake, allows users to query previous versions of data by specifying a timestamp or version number. This is useful for auditing, debugging, and historical analysis.
10. Difference between narrow and wide transformations?
In PySpark, narrow transformations are operations where each input partition contributes to only one output partition (e.g., map, filter).
Wide transformations involve shuffling data across partitions, requiring data from multiple partitions to create a single output partition (e.g., groupBy, join).
11. What is Unity Catalog? How is it different from Hive Meta Store?
Unity Catalog is a unified governance solution for data and AI assets in Databricks, providing fine-grained access controls and auditing capabilities. Unlike the Hive Metastore, which primarily manages metadata for Hive tables, Unity Catalog extends governance across various data sources and formats.
12. Why are we not using MapReduce these days? What are the similarities between Spark and MapReduce?
MapReduce is less commonly used today due to its slower performance and complexity compared to Spark, which offers in-memory processing, faster data processing, and a more user-friendly API.
Both Spark and MapReduce are distributed computing frameworks that process large datasets in parallel.
13. How to call one notebook from another notebook?
In Databricks, you can call one notebook from another using the %run magic command followed by the path of the notebook you wish to execute. This facilitates code reuse and modularization.
14. What is a surrogate key?
A surrogate key is a unique identifier for each record in a database table, typically used in data warehousing. It is not derived from application data and is usually a sequentially generated integer.
15. Difference between serverless pool and dedicated SQL pool?
A serverless SQL pool in Azure Synapse provides on-demand query capabilities without the need for infrastructure management, while a dedicated SQL pool offers provisioned resources for consistent performance and is ideal for predictable workloads.
16. Difference between managed tables and external tables? When do you go to create external tables?
Managed tables in a database have their data and metadata managed by the database system, while external tables store data outside the database, with only metadata managed internally.
External tables are used when data needs to be shared across different systems or when data resides in external storage.
17. How would you pass the data from data bricks to ADF?
Data can be passed from Databricks to ADF by writing the output to a storage location accessible by ADF, such as Azure Blob Storage or ADLS, and then using ADF to read from that location.
18. How are you fetching details from the key vault?
In ADF, you can fetch details from Azure Key Vault by creating a linked service to the Key Vault and using it to securely retrieve secrets, such as connection strings and passwords, during pipeline execution.
19. How to use nested JSON with data bricks?
Nested JSON data can be read and processed in Databricks using Spark’s built-in JSON functions, such as from_json and explode, to parse and flatten the hierarchical structure into a tabular format.
20. How do you implement data security in your project?
Data security in projects can be implemented through access controls, encryption, data masking, and auditing.
In cloud environments, services like Azure Active Directory and Azure Key Vault can be used to manage identities and secure sensitive information.
21. What is a mount point in data bricks?
A mount point in Databricks is a user-defined path that allows Databricks to access and manage external storage systems like Azure Blob Storage or ADLS as if they were part of the Databricks file system.
22. How do you read data from a URL in DataBricks?
Data can be read from a URL in Databricks using Spark’s read method with appropriate options, such as specifying the format and schema. This allows direct access to data hosted on web servers or cloud storage.
23. How can you optimize data bricks’ performance?
Performance in Databricks can be optimized through techniques like caching frequently accessed data, using optimized data formats like Parquet, tuning Spark configurations, and scaling clusters appropriately.
24. Why are data bricks better than dataflow?
Databricks is preferred over Dataflow for its interactive workspace, support for multiple languages, and advanced analytics capabilities. It provides a unified platform for data engineering, data science, and machine learning.
25. What is SCD? Explain SCD2,SCD3?
SCD is a technique used in data warehousing to manage changes in dimension data over time.
SCD2 tracks historical data by creating new records with versioning.
SCD3 stores changes by adding new columns to represent different states of data.
26. What are the prerequisites before the migration?
Before migration, it is essential to assess the current environment, identify dependencies, plan data transfer strategies, ensure data quality, and test the migration process to minimize risks and ensure a smooth transition.
27. What are the different types of clusters in data bricks?
Databricks offers different types of clusters, including interactive clusters for exploratory work, job clusters for scheduled tasks, and high concurrency clusters for serving multiple users or jobs simultaneously.
28. How are you fetching details from the key vault?
Details can be fetched from Azure Key Vault in Databricks by using the Azure Key Vault-backed secret scope, allowing secure access to secrets within notebooks or jobs.
29. How are you transferring data to Azure Synapse?
Data can be transferred to Azure Synapse from Databricks using connectors like Azure Synapse Connector for Spark, which allows writing data directly to Synapse tables, or by exporting data to a shared storage location and using Synapse pipelines to ingest it.
30. Explain the significance of the catalyst optimizer in PYSPARK.
The Catalyst optimizer is a key component of PySpark that enhances query performance by optimizing logical and physical plans.
It uses rule-based and cost-based optimization techniques to efficiently execute Spark SQL queries.
