300+ Senior Data Engineer Interview Questions and Answers

Use a SQL query with a subquery to find the 2nd most ordered item in a category.

• Use a subquery to rank items within each category based on the number of orders

• Select the item with rank 2 within each category

• Order the results by category and rank to get the 2nd most ordered item in each category

In a logic puzzle, only one among A, B, and C is lying. Analyzing their statements reveals the liar's identity.

• If A is telling the truth, then B is a liar, which makes C's statement true, leading to a contradiction.

• If B is telling the truth, then C is a liar, which makes A's statement false, leading to a contradiction.

• If C is telling the truth, then B is a liar, which aligns with A's statement, making A truthful.

• Thus, the only consistent scenario is that B is the liar.

Cores and worker nodes are decided based on the workload requirements and scalability needs of the data processing system.

• Consider the size and complexity of the data being processed

• Evaluate the processing speed and memory requirements of the tasks

• Take into account the parallelism and concurrency needed for efficient data processing

• Monitor the system performance and adjust cores and worker nodes as needed

Query to find employees absent for more than 10 consecutive days in attendance records.

• Use a window function to identify consecutive absence days.

• Filter results where the count of consecutive absences exceeds 10.

• Example SQL: SELECT employee_id FROM attendance WHERE status = 'absent' GROUP BY employee_id HAVING COUNT(*) > 10;

• Consider using ROW_NUMBER() to assign a sequence to attendance records.

Designing a data model for Amazon orders with facts and dimensions for effective order management.

• Fact Table: Order Facts - Contains metrics like order_id, total_amount, order_date, and customer_id.

• Dimension Table: Customer Dimension - Includes customer_id, name, email, and address.

• Dimension Table: Product Dimension - Contains product_id, product_name, category, and price.

• Dimension Table: Time Dimension - Includes order_date, week, month, quarter, and year for time-based anal...read more

Use Spark and SQL to find the top 5 countries with the highest population.

• Use Spark to load the data and perform data processing.

• Use SQL queries to group by country and sum the population.

• Order the results in descending order and limit to top 5.

• Example: SELECT country, SUM(population) AS total_population FROM table_name GROUP BY country ORDER BY total_population DESC LIMIT 5

Use Spark program to find word with maximum length in a given string

• Split the string into words using space as delimiter

• Map each word to its length

• Find the word with maximum length using reduce operation

Shuffling is the process of redistributing data across partitions in a distributed computing environment.

• Shuffling is necessary when data needs to be grouped or aggregated across different partitions.

• It can be handled efficiently by minimizing the amount of data being shuffled and optimizing the partitioning strategy.

• Techniques like partitioning, combiners, and reducers can help reduce the amount of shuffling in MapReduce jobs.

Identify anagrams by counting character occurrences without using sorted or counter functions.

• Create a frequency dictionary for each string's characters.

• Compare the frequency dictionaries of the two strings.

• Example: 'listen' and 'silent' both have 1 'l', 1 'i', 1 's', 1 't', 1 'e', 1 'n'.

• If the dictionaries match, the strings are anagrams.

Handle missing data in pyspark dataframe by using functions like dropna, fillna, or replace.

• Use dropna() function to remove rows with missing data

• Use fillna() function to fill missing values with a specified value

• Use replace() function to replace missing values with a specified value

Use SQL pivot function to change rows into columns and vice versa

• Use the PIVOT function in SQL to transform rows into columns

• Use the UNPIVOT function in SQL to transform columns into rows

• Example: SELECT * FROM table_name PIVOT (SUM(value) FOR column_name IN (value1, value2, value3))

• Example: SELECT * FROM table_name UNPIVOT (value FOR column_name IN (value1, value2, value3))

When a spark is submitted in Databricks, several backend processes are triggered to execute the job.

• The submitted spark job is divided into tasks by the Spark driver.

• The tasks are then scheduled to run on the available worker nodes in the cluster.

• The worker nodes execute the tasks and return the results to the driver.

• The driver aggregates the results and presents them to the user.

• Various optimizations such as data shuffling and caching may be applied during the execution proc...read more

To find different records for different joins using two tables

• Use the SQL query to perform different joins like INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• Identify the key columns in both tables to join on

• Select the columns from both tables and use WHERE clause to filter out the different records

Incremental data is handled by identifying new data since the last update and merging it with existing data.

• Identify new data since last update

• Merge new data with existing data

• Update data warehouse or database with incremental changes

Enforcing schema ensures that data conforms to a predefined structure and rules.

• Ensures data integrity by validating incoming data against predefined schema

• Helps in maintaining consistency and accuracy of data

• Prevents data corruption and errors in data processing

• Can lead to rejection of data that does not adhere to the schema

Spark Submit command is used to submit Spark applications to a cluster.

• Spark Submit command is used to launch applications on a Spark cluster.

• It allows users to specify application parameters like main class, jars, and arguments.

• Users can also configure properties like memory allocation and number of executors.

• Example: spark-submit --class com.example.Main --master yarn --deploy-mode cluster myApp.jar

End to end project architecture involves designing and implementing a data pipeline to process and analyze data from various sources.

• Define project requirements and goals

• Design data architecture including data sources, storage, processing, and analytics tools

• Implement data pipeline to extract, transform, and load data

• Ensure data quality and consistency throughout the pipeline

• Monitor and optimize performance of the data pipeline

• Examples: Using Apache Kafka for real-time data s...read more

Common file formats used in data storages include CSV, JSON, Parquet, Avro, and ORC. Parquet is best for compression.

• CSV (Comma-Separated Values) - simple and widely used, but not efficient for large datasets

• JSON (JavaScript Object Notation) - human-readable and easy to parse, but can be inefficient for storage

• Parquet - columnar storage format that is highly efficient for compression and query performance

• Avro - efficient binary format with schema support, good for data serial...read more

I would design a review analytics dashboard by integrating data from various sources, creating visualizations for key metrics, and allowing for customization and interactivity.

• Integrate data from various sources such as customer reviews, ratings, and feedback.

• Create visualizations for key metrics like average rating, sentiment analysis, and review volume over time.

• Allow for customization by letting users filter and drill down into specific data points.

• Enable interactivity by ...read more

Apache Spark is a distributed computing framework for big data processing, enabling fast data analysis and transformation.

• Resilient Distributed Datasets (RDDs): Spark's core abstraction for distributed data, allowing fault tolerance and parallel processing.

• DataFrames and Datasets: Higher-level abstractions built on RDDs, providing optimized execution and easier data manipulation with SQL-like operations.

• Spark SQL: Enables querying structured data using SQL, allowing integrati...read more

SQL query to fetch duplicate rows in a table

• Use GROUP BY and HAVING clause to identify duplicate rows

• Select columns to check for duplicates

• Example: SELECT column1, column2, COUNT(*) FROM table_name GROUP BY column1, column2 HAVING COUNT(*) > 1;

SQL conditions are used to filter data based on specified criteria. Common conditions include WHERE, AND, OR, IN, BETWEEN, etc.

• Common SQL conditions include WHERE, AND, OR, IN, BETWEEN, LIKE, etc.

• Conditions are used to filter data based on specified criteria in SQL queries.

• Examples: WHERE salary > 50000, AND department = 'IT', OR age < 30

To find duplicate items in a list

• Iterate through the list and compare each item with the rest of the list

• Use a hash table to keep track of seen items

• Sort the list and compare adjacent items

Spark architecture is based on a master-slave architecture with a cluster manager to coordinate tasks.

• Spark architecture consists of a driver program that communicates with a cluster manager to coordinate tasks.

• The cluster manager allocates resources and schedules tasks on worker nodes.

• Worker nodes execute the tasks and return results to the driver program.

• Spark supports various cluster managers like YARN, Mesos, and standalone mode.

• Spark applications can run in standalone mo...read more

I am a Senior Data Engineer with experience in various projects involving columnar format files in Spark, understanding Spark internals, OLAP vs OLTP, and data warehousing concepts.

• Projects: Developed ETL pipelines using Spark for processing large datasets, implemented data quality checks, and optimized query performance.

• Columnar format file in Spark: It stores data in columnar format to improve query performance by reading only the required columns, like Parquet or ORC files...read more

First step in use case debugging for existing architecture with error

• Review the existing architecture documentation to understand the flow and components involved

• Check the error logs to identify the specific error message and its source

• Verify the data inputs and outputs to ensure they are correct and consistent

• Run tests to reproduce the error and gather more information for analysis

• Consult with team members or stakeholders for insights or previous experiences with similar iss...read more

This task involves printing even numbers within a specified range using Python.

• Use the range() function to define the range of numbers.

• Utilize a for loop to iterate through the numbers in the range.

• Check if a number is even using the modulus operator (%).

• Print the number if it is even.

• Example: for i in range(1, 11): if i % 2 == 0: print(i) will print 2, 4, 6, 8, 10.