Koantek Senior DBA and Data Engineer Interview Questions and Answers

Python and SQL questions were asked

Q1. I was asked about the project
Add your answer

Q1. ACID properties of DBMS
Ans. 
ACID properties ensure data integrity in DBMS: Atomicity, Consistency, Isolation, Durability.
• Atomicity ensures that all operations in a transaction are completed successfully or none at all.

• Consistency ensures that the database remains in a consistent state before and after the transaction.

• Isolation ensures that multiple transactions can be executed concurrently without affecting each other.

• Durability ensures that once...read more
Answered by AI
Add your answer

Q1. HR question and family background
Add your answer

This was my first campus drive,& it was one of the hardest one too.We had 3 coding problems for 60 mins.It was held on HackerRank & it was not so hard but not so easy for the beginners.
1)Lambda Sort
2) and 3) ques on SQL
It was different ques for each one.

Q1. The interviewer asked all the concepts of pyspark, ETL and a code on dsa
Add your answer

Q1. It’s general type of question
Add your answer
Q2. Experience n all
Add your answer

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
Answered by AI
Add your answer
Q2. When we use ssis packages? Difference between union merge
Ans. 
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
Answered by AI
Add your answer

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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
Q3. Spark Architecture
Add your answer
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.
Answered by AI
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
Q8. What is SCD ??
Ans. 
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
Answered by AI
Add your answer
Q9. Scenerio based questions related to Spark ?
Add your answer
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.
Answered by AI
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer

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
Answered by AI
Add your answer
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.
Answered by AI
Add your answer
Q3. Pyspark question related to merging two schemas?
Add your answer
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')
Answered by AI
Add your answer
Q5. Spark Architecture
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer