Atos Senior Data Engineer Interview Questions and Answers

Q1. Windows function-related questions.
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Q2. Join Related Questions.
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Q1. Join related Questions
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Q2. Subqueries related queations.
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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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Q1. How do you utilize the enhanced optimization option in AWS Glue?
Ans. 
Enhanced optimization in AWS Glue improves job performance by automatically adjusting resources based on workload
• Enhanced optimization in AWS Glue automatically adjusts resources like DPUs based on workload

• It helps improve job performance by optimizing resource allocation

• Users can enable enhanced optimization in AWS Glue job settings
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Q2. What are the best practices for optimizing querying in Amazon Redshift?
Ans. 
Optimizing querying in Amazon Redshift involves proper table design, distribution keys, sort keys, and query optimization techniques.
• Use appropriate distribution keys to evenly distribute data across nodes for parallel processing.

• Utilize sort keys to physically order data on disk, reducing the need for sorting during queries.

• Avoid using SELECT * and instead specify only the columns needed to reduce data transfer.

• Use AN...read more
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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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Spark Optimization, Transformation, DLT, DL, Data Governance
Python
SQL