Jarvis Technology and Strategy Consulting Data Engineer Interview Questions and Answers

Q1. Spark architecture
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Q2. Difference between coalesce and reparation
Ans. 
Coalesce is used to return the first non-null value among its arguments, while reparation is not a standard function in SQL.
• Coalesce is a standard SQL function, while reparation is not.

• Coalesce returns the first non-null value among its arguments.

• Reparation is not a standard SQL function and may refer to a custom function or process specific to a certain system or application.
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Q3. SQL question using Lag window function
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Q4. Same question in Pyspark
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Q5. Project and day to day activities
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Q1. Scenario based questions
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Q2. Gave details about work
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Q1. Sql order of execution
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SQL order of execution determines the sequence in which different clauses are processed in a query.
• SQL query is parsed and validated first

• Next, the query optimizer creates an execution plan

• Execution plan includes steps like table scans, index scans, joins, etc.

• Finally, the query is executed and results are returned
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Q2. Sql question on finding highest salary for each employee where employees and department table are given in each department.
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Some multiple choice, 2 sql and 2 python questions were asked

Q1. Tell me about you project
Ans. 
Developed a real-time data processing system for analyzing customer behavior
• Used Apache Kafka for streaming data ingestion

• Implemented data pipelines using Apache Spark for processing and analysis

• Utilized Elasticsearch for storing and querying large volumes of data

• Developed custom machine learning models for predictive analytics
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Q2. Optimising technique that you have used
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I have used partitioning and indexing to optimize query performance.
• Implemented partitioning on large tables to improve query performance by limiting the data scanned

• Created indexes on frequently queried columns to speed up data retrieval

• Utilized clustering keys to physically organize data on disk for faster access
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Q1. Window partition question was asked
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Q2. Project related question
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Q1. Project related discussions
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Q2. Meduim level SQl and DSA
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Q1. This was data modelling round
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Q2. Design a uber data model
Ans. 
Uber data model design for efficient storage and retrieval of ride-related information.
• Create tables for users, drivers, rides, payments, and ratings

• Include attributes like user_id, driver_id, ride_id, payment_id, rating_id, timestamp, location, fare, etc.

• Establish relationships between tables using foreign keys

• Implement indexing for faster query performance
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Q1. Project explaining
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Q2. Write code to generate csv file with note pad data
Ans. 
Code to generate a CSV file with notepad data
• Open a text file and read the data

• Parse the data and write it to a CSV file

• Use libraries like pandas in Python for easier CSV handling
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Maxium sub string and reverse a string

Q1. Data warehouse concepts
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Round 1 was online Test where one basic coding question was there and few aptitiude , verbal ability and python based question.

Q1. What is Data WareHouse..?
Ans. 
A Data Warehouse is a centralized repository that stores integrated data from multiple sources for analysis and reporting.
• Data Warehouses are designed for query and analysis rather than transaction processing.

• They often contain historical data and are used for decision-making purposes.

• Data Warehouses typically use a dimensional model with facts and dimensions.

• Examples of Data Warehouse tools include Amazon Redshift, Sn
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Q2. Nested Queries in Bigquery..?
Ans. 
Nested queries in BigQuery allow for querying data from within another query, enabling complex data analysis.
• Nested queries are queries that are embedded within another query

• They can be used to perform subqueries to filter, aggregate, or manipulate data

• Nested queries can be used in SELECT, FROM, WHERE, and HAVING clauses
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