Sevasys Technologies Senior Data Engineer Interview Questions and Answers

Q1. Can you introduce yourself and describe your current project experience?
Ans. 
I am a Senior Data Engineer with experience in building scalable data pipelines and optimizing data processing workflows.
• Experience in designing and implementing ETL processes using tools like Apache Spark and Airflow

• Proficient in working with large datasets and optimizing query performance

• Strong background in data modeling and database design

• Worked on projects involving real-time data processing and streaming analytic
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Q2. Decorators in python
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Decorators in Python are functions that modify the behavior of other functions or methods.
• Decorators are defined using the @decorator_name syntax before a function definition.

• They can be used to add functionality to existing functions without modifying their code.

• Decorators can be used for logging, timing, authentication, and more.

• Example: @staticmethod decorator in Python is used to define a static method in a class.
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Q3. What is the SQL query to group by employee ID in order to combine the first name and last name with a space?
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SQL query to group by employee ID and combine first name and last name with a space
• Use the GROUP BY clause to group by employee ID

• Use the CONCAT function to combine first name and last name with a space

• Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name
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Q4. What are constructors in Python?
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Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.
• Constructors are defined using the __init__() method in a class.

• They are used to initialize instance variables of a class.

• Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)
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Q5. Indexing in sql
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Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
• Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.

• Indexes can be created using CREATE INDEX statement in SQL.

• Types of indexes include clustered indexes, non-clustered indexes, unique indexes, an...read more
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Q6. Why spark works well with parquet files?
Ans. 
Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.
• Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.

• Parquet files support efficient compression, reducing storage space and improving read performance in Spark.

• Spark can push down filters to Parquet files, allowing f...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
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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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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
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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
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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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Q1. How the files will be read in AWS GLUE
Ans. 
Files can be read in AWS Glue using Data Catalog, crawlers, and Glue ETL jobs.
• Use AWS Glue Data Catalog to store metadata information about the files.

• Crawlers can automatically infer the schema of the files and populate the Data Catalog.

• Glue ETL jobs can then be used to read the files from various sources like S3, RDS, etc.

• Supports various file formats like CSV, JSON, Parquet, etc.
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Q1. How to get duplicate records
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Duplicate records can be identified using SQL queries by comparing columns and using aggregate functions.
• Use GROUP BY clause with COUNT() function to identify duplicate records

• Use HAVING clause to filter out records with count greater than 1

• Join the table with itself on specific columns to find duplicates
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Q2. How to get the second highest salary
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Q1. Previous projects
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Q2. Basic questions on python, azure cloud
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Q1. Why company change
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Q2. Salary negotiations
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Q1. Tell me about yourself; Projects done; What is Columnar format file in Spark; Internals of Spark, Difference between OLAP and OLTP; About Datawarehouse- facts, dimensions
Ans. 
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 re...read more
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