Coforge Big Data Engineer Lead Interview Questions and Answers

Q1. How do you optimize SQL queries?
Ans. 
Optimizing SQL queries involves using indexes, avoiding unnecessary joins, and optimizing the query structure.
• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Use EXPLAIN to analyze query performance and make necessary adjustments
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Q2. How do you do performance optimization in Spark. Tell how you did it in you project.
Ans. 
Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.
• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my projec...read more
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Q3. What is SparkContext and SparkSession?
Ans. 
SparkContext is the main entry point for Spark functionality, while SparkSession is the entry point for Spark SQL.
• SparkContext is the entry point for low-level API functionality in Spark.

• SparkSession is the entry point for Spark SQL functionality.

• SparkContext is used to create RDDs (Resilient Distributed Datasets) in Spark.

• SparkSession provides a unified entry point for reading data from various sources and performing
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Q4. When a spark job is submitted, what happens at backend. Explain the flow.
Ans. 
When a spark job is submitted, various steps are executed at the backend to process the job.
• The job is submitted to the Spark driver program.

• The driver program communicates with the cluster manager to request resources.

• The cluster manager allocates resources (CPU, memory) to the job.

• The driver program creates DAG (Directed Acyclic Graph) of the job stages and tasks.

• Tasks are then scheduled and executed on worker nodes ...read more
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Q5. Calculate second highest salary using SQL as well as pyspark.
Ans. 
Calculate second highest salary using SQL and pyspark
• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• In pyspark, use orderBy() and take() functions to achieve the same result
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Q6. 2 types of modes for Spark architecture ?
Ans. 
The two types of modes for Spark architecture are standalone mode and cluster mode.
• Standalone mode: Spark runs on a single machine with a single JVM and is suitable for development and testing.

• Cluster mode: Spark runs on a cluster of machines managed by a cluster manager like YARN or Mesos for production workloads.
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Q7. If you want very less latency - which is better standalone or client mode?
Ans. 
Client mode is better for very less latency due to direct communication with the cluster.
• Client mode allows direct communication with the cluster, reducing latency.

• Standalone mode requires an additional layer of communication, increasing latency.

• Client mode is preferred for real-time applications where low latency is crucial.
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Q1. Scenario based. Write SQL and pyspark code for a dataset.
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Q2. If you have to find latest record based on latest timestamp in a table for a particular customer(table is having history) , how will you do it. Self join and nested query will be expensive. Optimized query...read more
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Q1. What is the architecture of Apache Spark?
Ans. 
Apache Spark architecture includes a cluster manager, worker nodes, and driver program.
• Apache Spark architecture consists of a cluster manager, which allocates resources and schedules tasks.

• Worker nodes execute tasks and store data in memory or disk.

• Driver program coordinates tasks and communicates with the cluster manager.

• Spark applications run as independent sets of processes on a cluster, coordinated by the SparkCon...read more
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Q2. What is the difference between the reduceBy and groupBy transformations in Apache Spark?
Ans. 
reduceBy is used to aggregate data based on key, while groupBy is used to group data based on key.
• reduceBy is a transformation that combines the values of each key using an associative function and a neutral 'zero value'.

• groupBy is a transformation that groups the data based on a key and returns a grouped data set.

• reduceBy is more efficient for aggregating data as it reduces the data before shuffling, while groupBy shu...read more
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Q3. What is the difference between RDD (Resilient Distributed Datasets) and DataFrame in Apache Spark?
Ans. 
RDD is a low-level abstraction representing a distributed collection of objects, while DataFrame is a higher-level abstraction representing a distributed collection of data organized into named columns.
• RDD is more suitable for unstructured data and low-level transformations, while DataFrame is more suitable for structured data and high-level abstractions.

• DataFrames provide optimizations like query optimization and code...read more
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Q4. What are the different modes of execution in Apache Spark?
Ans. 
The different modes of execution in Apache Spark include local mode, standalone mode, YARN mode, and Mesos mode.
• Local mode: Spark runs on a single machine with one executor.

• Standalone mode: Spark runs on a cluster managed by a standalone cluster manager.

• YARN mode: Spark runs on a Hadoop cluster using YARN as the resource manager.

• Mesos mode: Spark runs on a Mesos cluster with Mesos as the resource manager.
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Q1. About your self
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Q2. Communication skills
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Q1. Programming language
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Q2. What tools do you utilize for data analysis?
Ans. 
I utilize tools such as Excel, Python, SQL, and Tableau for data analysis.
• Excel for basic data manipulation and visualization

• Python for advanced data analysis and machine learning

• SQL for querying databases

• Tableau for creating interactive visualizations
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Q3. Pandas numpy seaborn matplot
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Data analysis of code in the context of data analysis.

Coding logical question paper.

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?
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 ??
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
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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. DAX Related Syntax and Codes
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Q2. Data Modelling, SQL, Python
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Q1. No Response from HR after calling of selection after Round 1
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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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Basics of mathematical ability and verbal ability

Q1. Introduction - explain projects
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Q2. Data analytics explain
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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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