TCS Data Engineer Interview Questions, Process, and Tips

Q1. Indexing in SQL
Ans. 
Indexing in SQL improves query performance by creating a data structure that allows for faster retrieval of data.
• Indexes are created on columns in a table to speed up SELECT queries.

• Types of indexes include clustered, non-clustered, unique, and composite indexes.

• Examples of creating an index: CREATE INDEX idx_name ON table_name(column_name);
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Q2. Optimizations in pyspark
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Optimizations in pyspark involve techniques to improve performance and efficiency of data processing.
• Use partitioning to distribute data evenly across nodes for parallel processing

• Utilize caching to store intermediate results in memory for faster access

• Avoid unnecessary shuffling of data by using appropriate join strategies

• Optimize the execution plan by analyzing and adjusting the stages of the job

• Use broadcast variabl...read more
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Q1. What is Databricks?
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Databricks is a unified analytics platform that provides collaborative environment for data scientists, engineers, and analysts.
• Databricks allows users to write and run Apache Spark code in a collaborative environment.

• It integrates with popular programming languages like Python, Scala, and SQL.

• Databricks provides tools for data visualization, machine learning, and data engineering.

• It offers automated cluster management...read more
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Q2. What are integration runtime?
Ans. 
Integration runtimes are compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.
• Integration runtimes can be self-hosted or Azure-hosted.

• They are used to move data between cloud and on-premises data stores.

• Integration runtimes provide connectivity to various data sources and destinations.

• Examples include Azure Integration Runtime and Self-hosted I
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Q1. Explain ur project?
Ans. 
Developed a data pipeline to ingest, clean, and analyze customer feedback data for product improvements.
• Used Apache Kafka for real-time data streaming

• Implemented data cleaning and transformation using Python and Pandas

• Utilized SQL for data analysis and visualization

• Collaborated with product managers to identify key insights for product enhancements
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Q2. Challenges Faced
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Q1. Why do u want to join
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Q2. What is your Expected CTC?
Ans. 
My expected CTC is based on industry standards, my experience, and the responsibilities of the role.
• My expected CTC is in line with the market rates for Data Engineers with similar experience and skills.

• I have taken into consideration the responsibilities and requirements of the role when determining my expected CTC.

• I am open to negotiation based on the overall compensation package offered by the company.
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Q1. Yes, your will give question
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Q2. I have worked for the data migration project, the project name called data myfire. Whereas we migrate data from the on-prem sql server to adls gen2.
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Q3. Project details
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Q4. I will complete my interview in my feed back compalsary
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Q5. I don't any thing my fell
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Pyspark coding join df union etc

Q1. Writing pyspark codes
Ans. 
Writing pyspark codes involves using PySpark API to process big data in a distributed computing environment.
• Use PySpark API to create SparkContext and SparkSession objects

• Utilize transformations like map, filter, reduceByKey, etc. to process data

• Implement actions like collect, count, saveAsTextFile, etc. to trigger computation

• Optimize performance by caching RDDs and using broadcast variables

• Handle errors and exceptions
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Q2. SQL codes with live examples
Ans. 
SQL codes with live examples
• Use SELECT statement to retrieve data from a database table

• Use WHERE clause to filter data based on specific conditions

• Use JOIN to combine rows from two or more tables based on a related column
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