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Infosys Big Data Engineer Interview Questions and Answers
Infosys Big Data Engineer Interview Experiences
5 interviews found
Anonymous
I applied via Other and was interviewed before Nov 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. What is bigdata
- Ans.
Big data refers to large and complex data sets that cannot be processed using traditional data processing tools.
Big data is characterized by the 3Vs - volume, velocity, and variety.
It requires specialized tools and technologies such as Hadoop, Spark, and NoSQL databases.
Big data is used in various industries such as healthcare, finance, and retail to gain insights and make data-driven decisions.
Interview Preparation Tips
Skills evaluated in this interview
I applied via Company Website and was interviewed in Apr 2024. There were 3 interview rounds.
(2 Questions)
- Q1. Mostly about spark topics like shuffeling joins, data skew, and pyspark scenarios
- Q2. Mostly about python and sql scenarios
(2 Questions)
- Q1. About behavioural questions
- Q2. Day to day work and workflow model based questions
(2 Questions)
- Q1. Why you need to join infosys
- Ans.
I want to join Infosys because of its reputation for innovation and growth opportunities.
Infosys is known for its cutting-edge technology solutions and innovative projects.
I am impressed by Infosys' commitment to employee development and career growth.
I believe that joining Infosys will provide me with the opportunity to work on challenging projects and enhance my skills.
- Q2. Self introduction
Interview Preparation Tips
I applied via Company Website and was interviewed in Nov 2023. There were 3 interview rounds.
(1 Question)
- Q1. Overall exp was good
(1 Question)
- Q1. Overall exp was good
(1 Question)
- Q1. Salary discussion
Selected
I applied via Referral and was interviewed before Apr 2023. There was 1 interview round.
(2 Questions)
- Q1. Tell about your last company project in depth
- Q2. What is spark why it is faster than Hadoop
- Ans.
Spark is a fast and distributed data processing engine that can perform in-memory processing.
Spark is faster than Hadoop because it can perform in-memory processing, reducing the need to write intermediate results to disk.
Spark uses DAG (Directed Acyclic Graph) for processing tasks, which optimizes the workflow and minimizes data shuffling.
Spark allows for iterative computations, making it suitable for machine learning...
Interview Preparation Tips
- SQL
- Spark
- Python
- boto3
Skills evaluated in this interview
Infosys interview questions for designations
pooja Y C
Interview Questionnaire
1 Question
- Q1. What is spark
- Ans.
Spark is an open-source distributed computing system used for big data processing and analytics.
Spark is built on top of Hadoop and provides faster processing of data due to in-memory computing.
It supports multiple programming languages like Java, Scala, Python, and R.
Spark has various components like Spark SQL, Spark Streaming, MLlib, and GraphX for different use cases.
It can be used for batch processing, real-time pr...
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Skills evaluated in this interview
What people are saying about Infosys
Interview questions from similar companies
Selected
I applied via Naukri.com and was interviewed in Aug 2024. There were 2 interview rounds.
(2 Questions)
- Q1. All type of joins with example
- Ans.
Different types of joins in SQL with examples
Inner Join: Returns rows when there is a match in both tables
Left Join: Returns all rows from the left table and the matched rows from the right table
Right Join: Returns all rows from the right table and the matched rows from the left table
Full Outer Join: Returns all rows when there is a match in either table
- Q2. How to handle large spark datasets
- Ans.
Large Spark datasets can be handled by partitioning, caching, optimizing transformations, and tuning resources.
Partitioning data to distribute workload evenly across nodes
Caching frequently accessed data to avoid recomputation
Optimizing transformations to reduce unnecessary processing
Tuning resources like memory allocation and parallelism for optimal performance
(2 Questions)
- Q1. How do you tune sparks configuration setting's to optimize query performance
- Ans.
Spark configuration settings can be tuned to optimize query performance by adjusting parameters like memory allocation, parallelism, and caching.
Increase executor memory and cores to allow for more parallel processing
Adjust shuffle partitions to optimize data shuffling during joins and aggregations
Enable dynamic allocation to scale resources based on workload demands
Utilize caching to store intermediate results and avo...
- Q2. What strategies do you use to handle data skew and partition imbalance in spark
- Ans.
To handle data skew and partition imbalance in Spark, strategies include using salting, bucketing, repartitioning, and optimizing join operations.
Use salting to evenly distribute skewed keys across partitions
Implement bucketing to pre-partition data based on a specific column
Repartition data based on a specific key to balance partitions
Optimize join operations by broadcasting small tables or using partitioning strategi
Skills evaluated in this interview
I applied via Company Website and was interviewed in Sep 2024. There was 1 interview round.
(3 Questions)
- Q1. Big Data fundamentals concepts on rdd, dataframe and datasets.
- Q2. Spark optimization techniques
- Ans.
Spark optimization techniques involve partitioning, caching, and tuning resource allocation.
Partitioning data to distribute workload evenly
Caching frequently accessed data to avoid recomputation
Tuning resource allocation for optimal performance
- Q3. SQL queries on windows function and joins.
Interview Preparation Tips
Skills evaluated in this interview
I applied via Naukri.com and was interviewed in Sep 2024. There were 2 interview rounds.
It was WeCP based test
(2 Questions)
- Q1. Explain Spark Architecture in detail
- Ans.
Spark Architecture is a distributed computing framework that provides high-level APIs for in-memory computing.
Spark Architecture consists of a cluster manager, worker nodes, and a driver program.
It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.
Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object.
It supports various data ...
- Q2. Explain higher order function, closure, anonymous function, map, flatmap, tail recursion
- Ans.
Higher order functions, closures, anonymous functions, map, flatmap, and tail recursion are key concepts in functional programming.
Higher order function: Functions that can take other functions as arguments or return functions as results.
Closure: Functions that capture variables from their lexical scope, even when they are called outside that scope.
Anonymous function: Functions without a specified name, often used as a...
Interview Preparation Tips
- SCALA
- Spark
- SQL
Skills evaluated in this interview
I applied via Company Website and was interviewed in Apr 2024. There were 3 interview rounds.
Questions on software and system designs
Helps employer identify particular personality traits like leadership, confidence, interpersonal and teamwork skills of potential employees
(2 Questions)
- Q1. Why do you want this job? Again, companies want to hire people who are passionate about the job, so you should have a great answer about why you want the ...
- Q2. This opportunity can help me in many ways to boost my confidence as well as drive my career forward
Interview Preparation Tips
- The role of technology in educat
Job Search Tips & Resources
Murugan R
Selected
I applied via Job Portal and was interviewed in Sep 2023. There were 3 interview rounds.
Easy only so prepare well that's it
(5 Questions)
- Q1. What method you use
- Ans.
I use a combination of programming languages, tools, and frameworks to analyze and process large datasets.
Utilize programming languages like Python, Java, or Scala for data processing
Leverage tools like Hadoop, Spark, or Kafka for distributed computing
Implement frameworks like MapReduce or Apache Flink for data analysis
Use SQL or NoSQL databases for data storage and retrieval
- Q2. Why you lleave current company
- Q3. What skill you have
- Q4. Tell about ur project
- Q5. What you implemented
- Ans.
Implemented a real-time data processing system using Apache Kafka and Spark for analyzing customer behavior.
Developed data pipelines to ingest, process, and analyze large volumes of data
Utilized Apache Kafka for real-time data streaming
Implemented machine learning algorithms for predictive analytics
Optimized data storage and retrieval for faster query performance
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Infosys Big Data Engineer Interview Process
based on 3 interviews in last 1 year
1 Interview rounds
- Technical Round
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