Atos Senior Data Engineer Interview Questions and Answers

Platform - Hackerank
Duration - 2 Hours
Topics - Spark and SQL

Q1. What are the common file formats used in data storages? Which one is best for compression?
Ans. 
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 - efficie...read more
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Q2. SQL Problem - Given the empoyee attendance table, write a query to print the employees who is abscent for more than cosecutive 10 days in their tenure.
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Q3. Given the list of words, write the Python program to print the most repeating substring out of all words.
Ans. 
Python program to find the most repeating substring in a list of words.
• Iterate through each word in the list

• Generate all possible substrings for each word

• Count the occurrences of each substring using a dictionary

• Find the substring with the highest count
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Python and sql tasks

Q1. Project related questions
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Q2. Coding questions on pyspark windows
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Q1. Managerial discussions, mostly in and around the previous projects
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Q1. Coding in pyspark
Ans. 
Pyspark is a Python library for big data processing using Spark framework.
• Pyspark is used for processing large datasets in parallel.

• It provides APIs for data manipulation, querying, and analysis.

• Example: Using pyspark to read a CSV file and perform data transformations.
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Q2. Databricks optimisation technique
Ans. 
Databricks optimisation techniques improve performance and efficiency of data processing on the Databricks platform.
• Use cluster sizing and autoscaling to optimize resource allocation based on workload

• Leverage Databricks Delta for optimized data storage and processing

• Utilize caching and persisting data to reduce computation time

• Optimize queries by using appropriate indexing and partitioning strategies
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Q3. Aqe details in databricks
Ans. 
Databricks is a unified data analytics platform that provides a collaborative environment for data engineers.
• Databricks is built on top of Apache Spark and provides a workspace for data engineering tasks.

• It allows for easy integration with various data sources and tools for data processing.

• Databricks provides features like notebooks, clusters, and libraries for efficient data engineering workflows.
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Q1. Adf,etl,python,adb
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Q1. More questions on spark and java
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Q2. More questions on sql
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Q1. Over all project architecture
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Q2. Sample data and its transformations
Ans. 
Sample data and its transformations
• Sample data can be in the form of CSV, JSON, or database tables

• Transformations include cleaning, filtering, aggregating, and joining data

• Examples: converting date formats, removing duplicates, calculating averages
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Q1. Why are u leaving current job
Ans. 
Seeking new challenges and opportunities for growth in a more dynamic environment.
• Looking for new challenges and opportunities for growth

• Seeking a more dynamic work environment

• Interested in expanding skill set and knowledge

• Want to work on more innovative projects
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Q1. Explain about yourself.
Ans. 
I am a Senior Data Engineer with 5+ years of experience in designing and implementing data pipelines for large-scale projects.
• Experienced in ETL processes and data warehousing

• Proficient in programming languages like Python, SQL, and Java

• Skilled in working with big data technologies such as Hadoop, Spark, and Kafka

• Strong understanding of data modeling and database management

• Excellent problem-solving and communication sk
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Q2. Explain about your current project.
Ans. 
Developing a real-time data processing system for analyzing customer behavior on e-commerce platform.
• Utilizing Apache Kafka for real-time data streaming

• Implementing Spark for data processing and analysis

• Creating machine learning models for customer segmentation

• Integrating with Elasticsearch for data indexing and search functionality
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Q3. Big data concept
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Q4. Spark related questions
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Spark Optimization, Transformation, DLT, DL, Data Governance
Python
SQL

Q1. How to work with nested json using pyspark
Ans. 
Working with nested JSON using PySpark involves using the StructType and StructField classes to define the schema and then using the select function to access nested fields.
• Define the schema using StructType and StructField classes

• Use the select function to access nested fields

• Use dot notation to access nested fields, for example df.select('nested_field.sub_field')
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Q2. How to implement scd2 step by step
Ans. 
Implementing SCD2 involves tracking historical changes in data over time.
• Identify the business key that uniquely identifies each record

• Add effective start and end dates to track when the record was valid

• Insert new records with updated data and end date of '9999-12-31'

• Update end date of previous record when a change occurs
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Q1. Write a SQL query to select data from table 2 where data exists in table 1
Ans. 
Use a SQL query to select data from table 2 where data exists in table 1
• Use a JOIN statement to link the two tables based on a common column

• Specify the columns you want to select from table 2

• Use a WHERE clause to check for existence of data in table 1
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Q2. After performing joins how many records would be retrieved for inner, left, right and outer joins
Ans. 
The number of records retrieved after performing joins depends on the type of join - inner, left, right, or outer.
• Inner join retrieves only the matching records from both tables

• Left join retrieves all records from the left table and matching records from the right table

• Right join retrieves all records from the right table and matching records from the left table

• Outer join retrieves all records from both tables, filling
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Q1. About previous company and reason for leaving
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