TCS Senior Data Engineer Interview Questions and Answers

Q1. Can you introduce yourself and describe your current project experience?
Ans. 
I am a Senior Data Engineer with experience in building scalable data pipelines and optimizing data processing workflows.
• Experience in designing and implementing ETL processes using tools like Apache Spark and Airflow

• Proficient in working with large datasets and optimizing query performance

• Strong background in data modeling and database design

• Worked on projects involving real-time data processing and streaming analytic...read more
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Q2. Decorators in python
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Decorators in Python are functions that modify the behavior of other functions or methods.
• Decorators are defined using the @decorator_name syntax before a function definition.

• They can be used to add functionality to existing functions without modifying their code.

• Decorators can be used for logging, timing, authentication, and more.

• Example: @staticmethod decorator in Python is used to define a static method in a class.
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Q3. What is the SQL query to group by employee ID in order to combine the first name and last name with a space?
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SQL query to group by employee ID and combine first name and last name with a space
• Use the GROUP BY clause to group by employee ID

• Use the CONCAT function to combine first name and last name with a space

• Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name
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Q4. What are constructors in Python?
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Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.
• Constructors are defined using the __init__() method in a class.

• They are used to initialize instance variables of a class.

• Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)
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Q5. Indexing in sql
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Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
• Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.

• Indexes can be created using CREATE INDEX statement in SQL.

• Types of indexes include clustered indexes, non-clustered indexes, unique indexes, an...read more
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Q6. Why spark works well with parquet files?
Ans. 
Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.
• Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.

• Parquet files support efficient compression, reducing storage space and improving read performance in Spark.

• Spark can push down filters to Parquet files, allowing f...read more
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Q1. Tell me about your project.
Ans. 
Developed a real-time data processing system for analyzing customer behavior on an e-commerce platform.
• Used Apache Kafka for real-time data streaming

• Implemented data pipelines using Apache Spark for processing large volumes of data

• Designed and optimized data models in PostgreSQL for storing and querying customer data
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Q2. What are the Types of SCD?
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Types of SCD include Type 1, Type 2, and Type 3.
• Type 1 SCD: Overwrites old data with new data, no history is maintained.

• Type 2 SCD: Maintains historical data by creating new records for changes.

• Type 3 SCD: Creates separate columns to store historical and current data.

• Examples: Type 1 - Employee address updates overwrite old address. Type 2 - Employee salary changes create new record with effective date. Type 3 - Employ...read more
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Q3. Delta Lake vs Data Lake?
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Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
• Delta Lake provides ACID transactions, schema enforcement, and data versioning on top of data lakes.

• Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale.

• Delta Lake is optimized for big data workloads and is built on top of Apache Spark.

• Data Lake...read more
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Q4. How to write a file in a delta table?
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To write a file in a delta table, you can use the Delta Lake API or Spark SQL commands.
• Use Delta Lake API to write data to a delta table

• Use Spark SQL commands like INSERT INTO to write data to a delta table

• Ensure that the data being written is in the correct format and schema
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Q5. What are optimisation techniques used in the project?
Ans. 
Optimisation techniques used in the project include indexing, query optimization, caching, and parallel processing.
• Indexing: Creating indexes on frequently queried columns to improve search performance.

• Query optimization: Rewriting queries to make them more efficient and reduce execution time.

• Caching: Storing frequently accessed data in memory to reduce the need for repeated database queries.

• Parallel processing: Distri...read more
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Q6. What is the difference between Tasks, and stages? About Spark UI?
Ans. 
Tasks and stages are components of the execution plan in Spark UI.
• Tasks are the smallest unit of work in Spark, representing a single operation on a partition of data.

• Stages are groups of tasks that are executed together as part of a larger computation.

• Tasks within a stage can be executed in parallel, while stages are executed sequentially.

• Tasks are created based on the transformations and actions in the Spark applicat...read more
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Q7. Role of DAG ins aprk ?
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DAG (Directed Acyclic Graph) in Apache Spark is used to represent a series of data processing steps and their dependencies.
• DAG in Spark helps optimize the execution of tasks by determining the order in which they should be executed based on dependencies.

• It breaks down a Spark job into smaller tasks and organizes them in a way that minimizes unnecessary computations.

• DAGs are created automatically by Spark when actions a...read more
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Q8. What transformation techniques you have used in your project?
Ans. 
I have used various transformation techniques such as data cleaning, normalization, aggregation, and feature engineering in my projects.
• Data cleaning to remove missing values and outliers

• Normalization to scale numerical features

• Aggregation to summarize data at different levels

• Feature engineering to create new relevant features
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Q1. About my project?
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Q2. Some scenario-based questions related to project and work?
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Q3. How do I keep myself updated with the latest technologies?
Ans. 
I keep myself updated by regularly attending conferences, workshops, online courses, and reading industry blogs.
• Attend conferences and workshops related to data engineering

• Take online courses on platforms like Coursera, Udemy, or DataCamp

• Read industry blogs and follow thought leaders on social media

• Join online communities and forums to discuss latest technologies and trends
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Questions related to GCP and cloud functions

Q1. Introduce yourself, what role are you working as and what are your responsibilities.
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Q2. Could you please explain GCP architecture?
Ans. 
GCP architecture refers to the structure and components of Google Cloud Platform for building and managing applications and services.
• GCP architecture is based on a global network of data centers that provide secure, scalable infrastructure for cloud services.

• Key components include Compute Engine for virtual machines, Cloud Storage for object storage, and BigQuery for data analytics.

• GCP architecture also includes networ...read more
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Q3. How can you optimize your queries for efficiency in BQ?
Ans. 
Optimizing queries in BigQuery involves using partitioned tables, clustering, and optimizing joins.
• Partition tables by date or another relevant column to reduce the amount of data scanned

• Use clustering to group related rows together, reducing the amount of data scanned for queries

• Avoid unnecessary joins and denormalize data where possible to reduce query complexity
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Q4. Do you have experience in Dataflow, Dataproc, cloud composer?
Ans. 
Yes, I have experience in Dataflow, Dataproc, and cloud composer.
• I have worked with Dataflow to process and analyze large datasets in real-time.

• I have used Dataproc to create and manage Apache Spark and Hadoop clusters for big data processing.

• I have experience with cloud composer for orchestrating workflows and managing data pipelines.
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Q5. What are the different joins in SQL? Please give an example to elaborate.
Ans. 
Different types of joins in SQL include inner join, left join, right join, and full outer join.
• 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 rows when there is a match in either table.

• Example: SELECT ...read more
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Q6. Explain cloud functions like cloud build, cloud run in GCP.
Ans. 
Cloud functions like Cloud Build and Cloud Run in GCP are serverless computing services for building and running applications in the cloud.
• Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. It automatically builds and tests your code in the cloud.

• Cloud Run is a managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It automati...read more
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Q1. How do you deploy spark application
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Spark applications can be deployed using various methods like standalone mode, YARN, Mesos, or Kubernetes.
• Deploy Spark application in standalone mode by submitting the application using spark-submit command

• Deploy Spark application on YARN by setting the master to yarn and submitting the application to the YARN ResourceManager

• Deploy Spark application on Mesos by setting the master to mesos and submitting the application...read more
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Q2. How do you decide Spark configuration for a job
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Spark configuration for a job is decided based on factors like data size, cluster resources, and job requirements.
• Consider the size of the data being processed to determine the number of partitions and memory requirements.

• Evaluate the available cluster resources such as CPU cores, memory, and storage to optimize performance.

• Adjust parameters like executor memory, executor cores, and driver memory based on the complexit...read more
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