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TCS Data Engineer Interview Questions and Answers
72 Interview questions
Distribution keys in Amazon Redshift determine how data is distributed across nodes for efficient query performance.
Distribution keys help optimize data storage and query performance by controlling data distribution across nodes.
Choosing the right distribution key can minimize data movement during query execution, improving performance.
For example, if you frequently join two tables on a common column, setting that...
Pyspark is a powerful tool for big data processing using Python, enabling efficient data manipulation and analysis.
Pyspark allows distributed data processing using RDDs (Resilient Distributed Datasets). Example: rdd = spark.parallelize([1, 2, 3])
DataFrames in Pyspark provide a higher-level abstraction for structured data. Example: df = spark.read.csv('file.csv')
Pyspark supports SQL queries through the Spark SQL mo...
Lists are mutable, ordered collections; tuples are immutable, ordered collections in Python.
Lists are defined using square brackets: `my_list = [1, 2, 3]`.
Tuples are defined using parentheses: `my_tuple = (1, 2, 3)`.
Lists can be modified (add/remove elements): `my_list.append(4)`.
Tuples cannot be modified after creation: `my_tuple[0] = 10` raises an error.
Lists have more built-in methods (e.g., sort, reverse) comp...
List comprehension is a concise way to create lists in Python using a single line of code.
It consists of brackets containing an expression followed by a for clause.
Example: squares = [x**2 for x in range(10)] creates a list of squares from 0 to 9.
You can also include conditions: evens = [x for x in range(10) if x % 2 == 0] for even numbers.
List comprehension is often more readable and faster than traditional loops...
What people are saying about TCS
Data engineer with 5 years of experience in building scalable data pipelines and optimizing data workflows for analytics.
5 years of experience in data engineering, focusing on ETL processes and data warehousing.
Proficient in Python and SQL for data manipulation and analysis.
Experience with cloud platforms like AWS and Azure for deploying data solutions.
Implemented a real-time data pipeline using Apache Kafka, impr...
Worked on a data pipeline project to streamline ETL processes for a retail analytics platform.
Designed and implemented ETL processes using Apache Airflow.
Utilized AWS services like S3 for storage and Redshift for data warehousing.
Optimized data transformation scripts in Python for performance improvements.
Collaborated with data scientists to ensure data quality and accessibility.
SQL joins combine rows from two or more tables based on related columns, enabling complex queries and data retrieval.
INNER JOIN: Returns records with matching values in both tables. Example: SELECT * FROM A INNER JOIN B ON A.id = B.id;
LEFT JOIN: Returns all records from the left table and matched records from the right table. Example: SELECT * FROM A LEFT JOIN B ON A.id = B.id;
RIGHT JOIN: Returns all records from ...
Query optimization improves database performance by enhancing query execution efficiency and reducing resource consumption.
Use indexes to speed up data retrieval. For example, indexing a 'last_name' column can improve search performance in large tables.
Avoid SELECT *; specify only the columns needed to reduce data transfer and processing time.
Use WHERE clauses to filter data early in the query process, minimizing ...
Apache Spark architecture includes a cluster manager, worker nodes, and driver program.
Apache Spark architecture consists of a cluster manager, such as YARN or Mesos, which allocates resources and schedules tasks.
Worker nodes execute the tasks and store data in memory or disk.
The driver program coordinates the execution of the application and interacts with the cluster manager to distribute tasks.
Spark application...
To find the 2nd highest salary in SQL, use the 'SELECT' statement with 'ORDER BY' and 'LIMIT' clauses.
Use the 'SELECT' statement to retrieve the salary column from the table.
Use the 'ORDER BY' clause to sort the salaries in descending order.
Use the 'LIMIT' clause to limit the result to the second row.
TCS Data Engineer Interview Experiences
98 interviews found
Anonymous
I appeared for an interview in Apr 2025, where I was asked the following questions.
- Q1. TCS Bhubaneswar Absolutely the Worst Walk-in Interview Experience Ever! I’ve never seen such a poorly organized and mismanaged interview process in my life. The entire setup was a disaster from the start. ...
- Q2. The chaos, the waiting time, the heat, and the indifferent attitude of the panel made it one of the most frustrating and humiliating experiences ever. If you value your time, energy, and dignity — avoid th...
Interview Preparation Tips
Selected
I appeared for an interview in Jan 2025, where I was asked the following questions.
- Q1. Scd in snowflake
- Q2. Data modeling example
I applied via Walk-in
(2 Questions)
- Q1. Difference between rank and dense_rank, Left vs Left anti join
- Ans.
Rank assigns unique ranks to rows, while dense_rank handles ties by assigning the same rank to tied rows. Left join includes all rows from the left table and matching rows from the right table, while left anti join includes only rows from the left table that do not have a match in the right table.
Rank assigns unique ranks to rows based on the specified order, while dense_rank handles ties by assigning the same rank to ...
- Q2. Python list comprehension, SQL query
(1 Question)
- Q1. Project related questions
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Recruitment Consulltant and was interviewed in Aug 2024. There were 2 interview rounds.
(3 Questions)
- Q1. Questions from pyspark - theoritical
- Q2. Pyspark basic coding
- Q3. SQL easy level coding question
(1 Question)
- Q1. 2 nd round was managerial. The interviewer asked about projects worked and assessed how I fit for the company
Interview Preparation Tips
- pyspark
- databricks
- SQL
- projects
Focus of quantitative maths and aptitude a bit more
(3 Questions)
- Q1. Some basic questions on your comfortable programming knowledge
- Q2. Some behaviour oriented questions
- Q3. Some role related questions
(2 Questions)
- Q1. Some behaviour oriented
- Q2. Salary discussion
Mohammed Suboor Ahmed
Selected
I applied via LinkedIn and was interviewed in Oct 2024. There was 1 interview round.
(2 Questions)
- Q1. Reverse string in python list
- Ans.
Reverse strings in a Python list
Use list comprehension to iterate through the list and reverse each string
Use the slice notation [::-1] to reverse each string
Example: strings = ['hello', 'world'], reversed_strings = [s[::-1] for s in strings]
- Q2. 2nd highest salary sql
- Ans.
To find the 2nd highest salary in SQL, use the 'SELECT' statement with 'ORDER BY' and 'LIMIT' clauses.
Use the 'SELECT' statement to retrieve the salary column from the table.
Use the 'ORDER BY' clause to sort the salaries in descending order.
Use the 'LIMIT' clause to limit the result to the second row.
Skills evaluated in this interview
I appeared for an interview in Sep 2024.
(3 Questions)
- Q1. Spark architecture
- Q2. Databricks clusters
- Q3. Copy activity and all features
I applied via Approached by Company and was interviewed in Sep 2024. There was 1 interview round.
(2 Questions)
- Q1. SCD 1 vs SCD 2
- Ans.
SCD 1 overwrites old data with new data, while SCD 2 keeps track of historical changes.
SCD 1 updates existing records with new data, losing historical information.
SCD 2 creates new records for each change, preserving historical data.
SCD 1 is simpler and faster, but can lead to data loss.
SCD 2 is more complex and slower, but maintains a full history of changes.
- Q2. Corrupt Record Handling in Spark
- Ans.
Corrupt record handling in Spark involves identifying and handling data that does not conform to expected formats.
Use DataFrameReader option("badRecordsPath", "path/to/bad/records") to save corrupt records to a separate location for further analysis.
Use DataFrame.na.drop() or DataFrame.na.fill() to handle corrupt records by dropping or filling missing values.
Implement custom logic to identify and handle corrupt records...
Interview Preparation Tips
- Python
- PySpark
- SQL
- ETL
Skills evaluated in this interview
(3 Questions)
- Q1. What is oops concept
- Ans.
Object-oriented programming (OOP) is a programming paradigm based on the concept of objects, which can contain data in the form of fields and code in the form of procedures.
OOP focuses on creating objects that interact with each other to solve a problem
Key concepts include encapsulation, inheritance, polymorphism, and abstraction
Encapsulation involves bundling data and methods that operate on the data into a single uni...
- Q2. Explain data engineer life cycle and its tools
- Ans.
Data engineer life cycle involves collecting, storing, processing, and analyzing data using various tools.
Data collection: Gathering data from various sources such as databases, APIs, and logs.
Data storage: Storing data in databases, data lakes, or data warehouses.
Data processing: Cleaning, transforming, and enriching data using tools like Apache Spark or Hadoop.
Data analysis: Analyzing data to extract insights and mak...
- Q3. What types of spark join strategies
- Ans.
Spark join strategies include broadcast join, shuffle hash join, and shuffle sort merge join.
Broadcast join is used when one of the DataFrames is small enough to fit in memory on all nodes.
Shuffle hash join is used when joining two large DataFrames by partitioning and shuffling the data based on the join key.
Shuffle sort merge join is used when joining two large DataFrames by sorting and merging the data based on the j...
Interview Preparation Tips
Skills evaluated in this interview
(2 Questions)
- Q1. What is Spark? Why it is so popular
- Ans.
Spark is a fast and general-purpose cluster computing system for big data processing.
Spark is popular for its speed and ease of use in processing large datasets.
It provides in-memory processing capabilities, making it faster than traditional disk-based processing systems.
Spark supports multiple programming languages like Java, Scala, Python, and R.
It offers a wide range of libraries for diverse tasks such as SQL, strea...
- Q2. What is Clustering? what is difference between pods and nodes?
- Ans.
Clustering is the process of grouping similar data points together. Pods are groups of one or more containers, while nodes are individual machines in a cluster.
Clustering is a technique used in machine learning to group similar data points together based on certain features or characteristics.
Pods in a cluster are groups of one or more containers that share resources and are scheduled together on the same node.
Nodes ar...
Skills evaluated in this interview
TCS Interview FAQs
The duration of TCS Data Engineer interview process can vary, but typically it takes about less than 2 weeks to complete.
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