Birlasoft Data Engineer 1 Interview Questions and Answers

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
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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?
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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?
Ans. 
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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Basics of mathematical ability and verbal ability

Q1. Introduction - explain projects
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Q2. Data analytics explain
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Q1. Diff between Coalesce and repatriation
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Database Management system SQL and PlSQL

Database Base Management system SQL and PlSQL

Database Management system

Database Management system

Database Management system

Database Management system

Database Base Management system

Q1. Database Management system
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Q2. SQL and PlSQL Mango Data And Manu Database Management system
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Q3. C Language and C++Language and Java
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Q4. Data Analysis and Data entry
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Q5. DBMS C,C++ Java Data Entry Ms Excel Ms Word Ms PP
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Q1. ReduceByKey vs groupByKey
Ans. 
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
Ans. 
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 your current project? Have you used any AWS services?
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Q2. Can you explain about EMR andEC2 instance?
Ans. 
EMR is a managed Hadoop framework for processing large amounts of data, while EC2 is a scalable virtual server in AWS.
• EMR stands for Elastic MapReduce and is a managed Hadoop framework for processing large amounts of data.

• EC2 stands for Elastic Compute Cloud and is a scalable virtual server in Amazon Web Services (AWS).

• EMR allows for easy provisioning and scaling of Hadoop clusters, while EC2 provides resizable compute...read more
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Q3. What type of schemas did you use for your project. (Star schema, Snowflake Schema)
Ans. 
I have experience working with both Star and Snowflake schemas in my projects.
• Star schema is a denormalized schema where one central fact table is connected to multiple dimension tables.

• Snowflake schema is a normalized schema where dimension tables are further normalized into sub-dimension tables.

• Used Star schema for simpler, smaller datasets where performance is a priority.

• Used Snowflake schema for complex, larger dat...read more
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Q4. Have you used python, pyspark in your projects?
Ans. 
Yes, I have used Python and PySpark in my projects for data engineering tasks.
• I have used Python for data manipulation, analysis, and visualization.

• I have used PySpark for big data processing and distributed computing.

• I have experience in writing PySpark jobs to process large datasets efficiently.
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Q5. Do you have any experience with serverless schema?
Ans. 
Yes, I have experience with serverless schema.
• I have worked with AWS Lambda to build serverless applications.

• I have experience using serverless frameworks like Serverless Framework or AWS SAM.

• I have designed and implemented serverless architectures using services like AWS API Gateway and AWS DynamoDB.
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Q1. Tell. me about youself
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Q2. Sql question , pyspark questions
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1 ques of pyspark based on time series

Q1. Sql questions on window functions Question on List Project related ques
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Q2. Basic ques on Aws like glue lambda
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