1000+ Data Engineer Interview Questions and Answers

To create a Kafka topic with replication factor 2, use the command line tool or Kafka API.

• Use the command line tool 'kafka-topics.sh' with the '--replication-factor' flag set to 2.

• Alternatively, use the Kafka API to create a topic with a replication factor of 2.

• Ensure that the number of brokers in the Kafka cluster is greater than or equal to the replication factor.

• Consider setting the 'min.insync.replicas' configuration property to 2 to ensure that at least two replicas are ...read more

Enforcing the schema ensures data consistency and validation, while manually defining the schema in code allows for more flexibility and customization.

• Enforcing the schema ensures that all data conforms to a predefined structure and format, preventing errors and inconsistencies.

• Manually defining the schema in code allows for more flexibility in handling different data types and structures.

• Enforcing the schema can be done using tools like Apache Avro or Apache Parquet, while m...read more

The probability of drawing 3 balls of the same color from a box containing 4 balls of each color (Red, Green, Blue).

• Calculate the total number of ways to draw 3 balls out of 12 balls

• Calculate the number of ways to draw 3 balls of the same color

• Divide the number of favorable outcomes by the total number of outcomes to get the probability

Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.

• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my project, I optimized Spark performance by increasing executor me...read more

UDF stands for User-Defined Function in Spark. It allows users to define their own functions to process data.

• UDFs can be written in different programming languages like Python, Scala, and Java.

• UDFs can be used to perform complex operations on data that are not available in built-in functions.

• PySpark code to check the validity of mobile_number column can be written using regular expressions and the `regexp_extract` function.

• Example: `df.select('mobile_number', regexp_extract('...read more

The number of stages created from the code provided depends on the specific code and its functionality.

• The number of stages can vary based on the complexity of the code and the specific tasks being performed.

• Stages may include data extraction, transformation, loading, and processing.

• It is important to analyze the code and identify distinct stages to determine the total number.

Merge two unsorted lists into a sorted list using inbuilt sorting functions.

• Use inbuilt sorting functions to sort the input lists

• Merge the sorted lists using a merge algorithm

• Return the merged and sorted list

Internal tables store data within Hive's warehouse directory while external tables store data outside of it.

• Internal tables are managed by Hive and are deleted when the table is dropped

• External tables are not managed by Hive and data is not deleted when the table is dropped

• Internal tables are faster for querying as data is stored within Hive's warehouse directory

• External tables are useful for sharing data between different systems

• Example: CREATE TABLE my_table (col1 INT, col2...read more

Utilize data science to analyze demographics, competition, and location factors for optimal store placement.

• Analyze demographic data: Use census data to identify population density and income levels in potential areas.

• Evaluate competition: Map existing stores and assess their performance to find underserved locations.

• Consider foot traffic: Use mobile data or sensors to measure foot traffic in different areas at various times.

• Assess accessibility: Analyze transportation networ...read more

Data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed.

• Data lake stores raw, unstructured data from various sources.

• Data lake allows for storing large amounts of data without the need for a predefined schema.

• Data lake is cost-effective for storing data that may not have a clear use case at the time of storage.

• Data warehouse stores structured data for querying and analysis.

• Data warehouse requires a predefined schema for d...read more

Find the highest salary for each employee in their respective departments using SQL queries.

• Use the 'employees' table to get employee details including salary.

• Join the 'employees' table with the 'departments' table on department ID.

• Use the SQL 'GROUP BY' clause to group results by department.

• Utilize the 'MAX()' function to find the highest salary within each department.

• Example SQL query: SELECT department_id, MAX(salary) FROM employees GROUP BY department_id;

Data flow is a visual representation of data movement and transformation. ADF pipeline is a set of activities to move and transform data.

• Data flow is a drag-and-drop interface to design data transformation logic

• ADF pipeline is a set of activities to orchestrate data movement and transformation

• Data flow is more flexible and powerful than ADF pipeline

• Data flow can be used to transform data within a pipeline or as a standalone entity

DBMS is a software system to manage databases while RDBMS is a type of DBMS that stores data in a structured manner.

• DBMS stands for Database Management System while RDBMS stands for Relational Database Management System.

• DBMS can manage any type of database while RDBMS manages only relational databases.

• DBMS does not enforce any specific data model while RDBMS enforces the relational data model.

• Examples of DBMS include MongoDB and Cassandra while examples of RDBMS include MySQL...read more

Answering questions related to data engineering

• Issues faced in project: data quality, scalability, performance, integration

• Global parameter: a parameter that can be accessed across multiple components in a system

• Parameters in ADF: used to pass values between activities in a pipeline

• APIs in Spark: Spark SQL, Spark Streaming, MLlib, GraphX

Discussion on project flow, HBase, Hive, external partitioned tables, and optimization in a Data Engineer interview.

• Explained project flow and the reason for using HBase in the project

• Discussed querying data in HBase and the purpose of Hive

• Described external partitioned tables and optimization techniques used in the project

Set spark configuration with appropriate memory and cores for efficient processing of 2 GB data

• Increase executor memory and cores to handle larger data size

• Adjust spark memory overhead to prevent out of memory errors

• Optimize shuffle partitions for better performance

Python program to convert a number to words.

• Use a dictionary to map numbers to words.

• Divide the number into groups of three digits and convert each group to words.

• Handle special cases like zero, negative numbers, and numbers greater than or equal to one billion.

Data warehouses are designed for analytical queries and reporting, while transactional databases are optimized for transactional processing.

• Data warehouses are optimized for read-heavy workloads, allowing for complex queries and reporting.

• Transactional databases are optimized for write-heavy workloads, ensuring data integrity and consistency.

• Data warehouses often store historical data for analysis, while transactional databases focus on current data for operational purposes.

• D...read more

Treating categorised values based on frequency involves grouping rare values together.

• Identify rare values based on their frequency distribution

• Group rare values together to reduce complexity

• Consider creating a separate category for rare values

Use Databricks provided tools like databricks-connect and databricks-cli to migrate Hive metadata to Unity catalog.

• Use databricks-connect to connect to the Databricks workspace from your local development environment.

• Use databricks-cli to export the Hive metadata from the existing Hive metastore.

• Create a new Unity catalog in Databricks and import the exported metadata using databricks-cli.

• Validate the migration by checking the tables and databases in the Unity catalog.

Our project architecture uses Spark and Hive for data processing and storage respectively. We utilize AWS services such as S3, EMR, and Glue for scalability and cost-effectiveness.

• Spark is used for distributed data processing and analysis

• Hive is used for data warehousing and querying

• AWS S3 is used for storing large amounts of data

• AWS EMR is used for running Spark and Hive clusters

• AWS Glue is used for ETL (Extract, Transform, Load) jobs

• The purpose of using these services is to...read more

Find the greatest number for same key in a Python dictionary.

• Use max() function with key parameter to find the maximum value for each key in the dictionary.

• Iterate through the dictionary and apply max() function on each key.

• If the dictionary is nested, use recursion to iterate through all the keys.

Columnar storage is a data storage format that stores data in columns rather than rows, improving query performance.

• Columnar storage stores data in a column-wise manner instead of row-wise.

• It improves query performance by reducing the amount of data that needs to be read from disk.

• Parquet is a columnar storage file format that is optimized for big data workloads.

• It is used in Apache Spark and other big data processing frameworks.

• Delta is an open-source storage layer that prov...read more

To read JSON data using Spark, use the SparkSession.read.json() method.

• Create a SparkSession object

• Use the read.json() method to read the JSON data

• Specify the path to the JSON file or directory containing JSON files

• The resulting DataFrame can be manipulated using Spark's DataFrame API

SQL query to retrieve student names with marks > 45 in each subject

• Use GROUP BY and HAVING clauses to filter students with marks > 45 in each subject

• Join Student table with Marks table on student_id to get marks for each student

• Select student names from Student table based on the conditions

Efficiently process 1 million records in ADF by batching and parallelism.

• Use Data Flow in ADF to create a pipeline that processes data in batches of 10,000 records.

• Implement a ForEach activity to iterate over the batches, allowing parallel execution.

• Utilize the 'Batch Size' setting in the Copy Data activity to control the number of records processed at once.

• Consider partitioning the data source if applicable, to improve performance and reduce processing time.

• Monitor and optim...read more

Design a generic tool in pyspark to connect to multiple databases, fetch results, handle null values, and store output in another database

• Use pyspark to create a tool that can connect to databases like mysql, s3, or api

• Implement functions to fetch data from the databases and perform transformations like handling null values

• Utilize pyspark to store the transformed data in another database

• Consider using pyspark SQL functions for data transformations