Everestdx Azure Data Engineer Interview Questions and Answers

Activities in Azure Data Factory (ADF) are the building blocks of a pipeline and perform various tasks like data movement, data transformation, and data orchestration.

• Activities can be used to copy data from one location to another (Copy Activity)

• Activities can be used to transform data using mapping data flows (Data Flow Activity)

• Activities can be used to run custom code or scripts (Custom Activity)

• Activities can be u...read more

Dataframes in pyspark are distributed collections of data organized into named columns.

• Dataframes are similar to tables in a relational database, with rows and columns.

• They can be created from various data sources like CSV, JSON, Parquet, etc.

• Dataframes support SQL queries and transformations using PySpark functions.

• Example: df = spark.read.csv('file.csv')

Use DISTINCT keyword in SQL to remove duplicates from a dataset.

• Use SELECT DISTINCT column_name FROM table_name to retrieve unique values from a specific column.

• Use SELECT DISTINCT * FROM table_name to retrieve unique rows from the entire table.

• Use GROUP BY clause with COUNT() function to remove duplicates based on specific criteria.

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.

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 ...read more

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

Challenges in production deployment include scalability, data consistency, and monitoring.

• Ensuring scalability to handle increasing data volumes and user loads

• Maintaining data consistency across different databases and systems

• Implementing effective monitoring and alerting to quickly identify and resolve issues

Developed a data pipeline to ingest, process, and analyze customer feedback data for a retail company.

• Used Azure Data Factory to orchestrate data flow

• Implemented Azure Databricks for data processing and analysis

• Utilized Azure Synapse Analytics for data warehousing

• Generated visualizations using Power BI for insights

• Implemented machine learning models for sentiment analysis

Polybase is a feature in Azure SQL Data Warehouse that allows users to query data stored in Hadoop or Azure Blob Storage.

• Polybase enables users to access and query external data sources without moving the data into the database.

• It provides a virtualization layer that allows SQL queries to seamlessly integrate with data stored in Hadoop or Azure Blob Storage.

• Polybase can significantly improve query performance by levera...read more

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.

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

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.