TCS Big Data Engineer Interview Questions, Process, and Tips

Easy only so prepare well that's it

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
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Q2. Why you lleave current company
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Q3. What skill you have
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Q4. Tell about ur project
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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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Q1. Basic Big Data architecture and coding questions
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Q1. External and internal table difference
Ans. 
External tables are stored outside the database while internal tables are stored within the database.
• External tables are created using the LOCATION clause to specify the data location.

• Internal tables are created using the CREATE TABLE statement.

• External tables can be accessed by multiple databases while internal tables are specific to a single database.

• External tables are not managed by the database and can be deleted ...read more
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Q1. Salary expectation
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Q1. What is the difference between lineage and directed acyclic graphs (DAG)?
Ans. 
Lineage tracks the history of data transformations, while DAG is a graph structure with nodes representing tasks and edges representing dependencies.
• Lineage focuses on the history of data transformations, showing how data has been derived or modified.

• DAG is a graph structure where nodes represent tasks and edges represent dependencies between tasks.

• Lineage helps in understanding the data flow and ensuring data quality ...read more
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Q2. What optimization techniques have you utilized in your projects? Please explain with specific use cases.
Ans. 
I have utilized optimization techniques such as indexing, caching, and parallel processing in my projects.
• Implemented indexing on large datasets to improve query performance

• Utilized caching to store frequently accessed data and reduce load times

• Implemented parallel processing to speed up data processing tasks
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Q3. What is the difference between cache and persistence?
Ans. 
Cache is temporary storage used to store frequently accessed data for quick retrieval, while persistence refers to storing data permanently.
• Cache is temporary and volatile, while persistence is permanent and non-volatile

• Cache is typically faster to access than persistence

• Examples of cache include browser cache, CPU cache, and in-memory cache systems like Redis

• Examples of persistence include databases like MySQL, Postgr
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