Egen Senior Data Engineer Interview Questions and Answers

Q1. It’s general type of question
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Q2. Experience n all
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It’s just reasoning type questions.

Q1. What is ssis? How we use
Ans. 
SSIS stands for SQL Server Integration Services, a tool provided by Microsoft for data integration and workflow applications.
• SSIS is a platform for building high-performance data integration and workflow solutions.

• It allows you to create packages that move data from various sources to destinations.

• SSIS includes a visual design interface for creating, monitoring, and managing data integration processes.

• You can use SSIS ...read more
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Q2. When we use ssis packages? Difference between union merge
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SSIS packages are used for ETL processes in SQL Server. Union combines datasets vertically, while merge combines them horizontally.
• SSIS packages are used for Extract, Transform, Load (ETL) processes in SQL Server.

• Union in SSIS combines datasets vertically, stacking rows on top of each other.

• Merge in SSIS combines datasets horizontally, matching rows based on specified columns.

• Union All in SSIS combines datasets vertica...read more
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Q1. ReduceByKey vs groupByKey
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reduceByKey is more efficient than groupByKey for aggregating data in Spark due to reduced shuffling.
• reduceByKey combines values for each key in each partition before shuffling data

• groupByKey shuffles all data to a single partition before combining values for each key

• reduceByKey is preferred for large datasets to minimize data movement and improve performance
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Q2. Word count in scala
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Scala provides a simple way to count words in a string using built-in functions.
• Use the split function to split the string into an array of words

• Use the length function to get the count of words in the array
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Q3. Second highest salary SQL
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Use SQL query with ORDER BY and LIMIT to find the second highest salary.
• Use ORDER BY clause to sort salaries in descending order

• Use LIMIT 1,1 to skip the first highest salary and get the second highest salary
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Q1. Tell me about yourself and Project
Ans. 
I am a Senior Data Engineer with experience in developing data pipelines and optimizing data storage for various projects.
• Developed data pipelines using Apache Spark for real-time data processing

• Optimized data storage using technologies like Hadoop and AWS S3

• Worked on a project to analyze customer behavior and improve marketing strategies
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Q2. What was you day-to-day job in your project
Ans. 
My day-to-day job in the project involved designing and implementing data pipelines, optimizing data workflows, and collaborating with cross-functional teams.
• Designing and implementing data pipelines to extract, transform, and load data from various sources

• Optimizing data workflows to improve efficiency and performance

• Collaborating with cross-functional teams including data scientists, analysts, and business stakeholde...read more
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Q3. Spark Architecture
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Q4. How DAG handle Fault tolerance?
Ans. 
DAGs handle fault tolerance by rerunning failed tasks and maintaining task dependencies.
• DAGs rerun failed tasks automatically to ensure completion.

• DAGs maintain task dependencies to ensure proper sequencing.

• DAGs can be configured to retry failed tasks a certain number of times before marking them as failed.
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Q5. What is shuffling? How to Handle Shuffling?
Ans. 
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 MapRed
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Q6. What is the difference between repartition and Coelsce?
Ans. 
Repartition increases or decreases the number of partitions in a DataFrame, while Coalesce only decreases the number of partitions.
• Repartition can increase or decrease the number of partitions in a DataFrame, leading to a shuffle of data across the cluster.

• Coalesce only decreases the number of partitions in a DataFrame without performing a full shuffle, making it more efficient than repartition.

• Repartition is typically...read more
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Q7. How do you handle Incremental data?
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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
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Q8. What is SCD ??
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SCD stands for Slowly Changing Dimension, a concept in data warehousing to track changes in data over time.
• SCD is used to maintain historical data in a data warehouse.

• There are three types of SCD - Type 1, Type 2, and Type 3.

• Type 1 SCD overwrites old data with new data.

• Type 2 SCD creates a new record for each change, preserving history.

• Type 3 SCD maintains both old and new values in the same record.

• SCD is important for...read more
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Q9. Scenerio based questions related to Spark ?
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Q10. Two SQL Codes and Two Python codes like reverse a string ?
Ans. 
Reverse a string using SQL and Python codes.
• In SQL, use the REVERSE function to reverse a string.

• In Python, use slicing with a step of -1 to reverse a string.
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Q11. Find top 5 countries with highest population in Spark and SQL
Ans. 
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
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Q12. Using two tables find the different records for different joins
Ans. 
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
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Q1. What is a catalyst optimiser? How it works?
Ans. 
A catalyst optimizer is a query optimization tool used in Apache Spark to improve performance by generating an optimal query plan.
• Catalyst optimizer is a rule-based query optimization framework in Apache Spark.

• It leverages rules to transform the logical query plan into a more optimized physical plan.

• The optimizer applies various optimization techniques like predicate pushdown, constant folding, and join reordering.

• By o...read more
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Q2. Tell me about the optimization you used in your project.
Ans. 
Used query optimization techniques to improve performance in database queries.
• Utilized indexing to speed up search queries.

• Implemented query caching to reduce redundant database calls.

• Optimized SQL queries by restructuring joins and subqueries.

• Utilized database partitioning to improve query performance.

• Used query profiling tools to identify and optimize slow queries.
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Q3. Pyspark question related to merging two schemas?
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Q4. What is the best approach to finding whether the data frame is empty or not?
Ans. 
Use the len() function to check the length of the data frame.
• Use len() function to get the number of rows in the data frame.

• If the length is 0, then the data frame is empty.

• Example: if len(df) == 0: print('Data frame is empty')
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Q5. Spark Architecture
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Q6. How do you decide on cores and worker nodes?
Ans. 
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
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Q7. What happens when we enforce schema ?
Ans. 
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
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Q1. After the First Round of MCQ they will schedule a Technical Video Call. Questions were more from SQL - Windowing Functions, Unix - Shell Scripting, Pattern Matching, Apache Spark - Dataframe, Streaming, Op...read more
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Q1. It was a audio call discussing about the salary structure, components and the rest.
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Q1. Introduction of urs
Ans. 
I am a Senior Data Engineer with expertise in data processing and analysis.
• Experienced in designing and implementing data pipelines

• Proficient in programming languages like Python and SQL

• Skilled in working with big data technologies like Hadoop and Spark

• Familiar with data warehousing and ETL processes

• Strong problem-solving and analytical skills
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Q2. Basic structured query language
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Q1. Introduction of urs
Ans. 
I am a Senior Data Engineer with expertise in data processing and analysis.
• Experienced in designing and implementing data pipelines

• Proficient in programming languages like Python and SQL

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

• Familiar with data warehousing concepts and ETL processes

• Strong problem-solving and troubleshooting skills

• Effective communication and collaboration with cross-functio
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Q1. 1) Snowflake architecture in your current project.
Ans. 
Snowflake architecture is used in our project for cloud-based data warehousing.
• Snowflake follows a multi-cluster shared data architecture.

• It separates storage and compute resources, allowing for independent scaling.

• Data is stored in virtual warehouses, which are compute clusters that can be scaled up or down based on workload.

• Snowflake uses a unique architecture called a multi-cluster, shared data architecture, which s...read more
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Q2. 2) Database roles in Snowflake.
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Database roles in Snowflake define permissions and access control for users and objects.
• Database roles in Snowflake are used to manage permissions and access control for users and objects.

• Roles can be assigned to users or other roles to grant specific privileges.

• Examples of roles in Snowflake include ACCOUNTADMIN, SYSADMIN, SECURITYADMIN, and PUBLIC.
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Q3. 3) Session Policy in Snowflake.
Ans. 
Session Policy in Snowflake defines the behavior of a session, including session timeout and idle timeout settings.
• Session Policy can be set at the account, user, or role level in Snowflake.

• Session Policy settings include session timeout, idle timeout, and other session-related configurations.

• Example: Setting a session timeout of 30 minutes will automatically end the session if there is no activity for 30 minutes.
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Q4. 4) Describe the SSO process between Snowflake and Azure Active Directory.
Ans. 
SSO process between Snowflake and Azure Active Directory involves configuring SAML-based authentication.
• Configure Snowflake to use SAML authentication with Azure AD as the identity provider

• Set up a trust relationship between Snowflake and Azure AD

• Users authenticate through Azure AD and are granted access to Snowflake resources

• SSO eliminates the need for separate logins and passwords for Snowflake and Azure AD
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Q5. 5) Network Policy in Snowflake.
Ans. 
Network Policy in Snowflake controls access to Snowflake resources based on IP addresses or ranges.
• Network Policies are used to restrict access to Snowflake resources based on IP addresses or ranges.

• They can be applied at the account, user, or role level.

• Network Policies can be used to whitelist specific IP addresses or ranges that are allowed to access Snowflake resources.

• They can also be used to blacklist IP addresse...read more
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Q6. 6) Automatic data loading from pipes in to Snowflake.
Ans. 
Automate data loading from pipes into Snowflake for efficient data processing.
• Use Snowpipe, a continuous data ingestion service provided by Snowflake, to automatically load data from pipes into Snowflake tables.

• Snowpipe monitors a stage for new data files and loads them into the specified table in real-time.

• Configure Snowpipe to trigger a data load whenever new data files are added to the stage, eliminating the need fo...read more
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Q7. 7) How does query acceleration speed up query processing?
Ans. 
Query acceleration speeds up query processing by optimizing query execution and reducing the time taken to retrieve data.
• Query acceleration uses techniques like indexing, partitioning, and caching to optimize query execution.

• It reduces the time taken to retrieve data by minimizing disk I/O and utilizing in-memory processing.

• Examples include using columnar storage formats like Parquet or optimizing join operations.
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