LTIMindtree Data Engineer Interview Questions and Answers

Q1. How do you optimize SQL queries?
Ans. 
Optimizing SQL queries involves using indexes, avoiding unnecessary joins, and optimizing the query structure.
• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Use EXPLAIN to analyze query performance and make necessary adjustments
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Q2. How do you do performance optimization in Spark. Tell how you did it in you project.
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Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.
• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my projec...read more
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Q3. What is SparkContext and SparkSession?
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SparkContext is the main entry point for Spark functionality, while SparkSession is the entry point for Spark SQL.
• SparkContext is the entry point for low-level API functionality in Spark.

• SparkSession is the entry point for Spark SQL functionality.

• SparkContext is used to create RDDs (Resilient Distributed Datasets) in Spark.

• SparkSession provides a unified entry point for reading data from various sources and performing ...read more
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Q4. When a spark job is submitted, what happens at backend. Explain the flow.
Ans. 
When a spark job is submitted, various steps are executed at the backend to process the job.
• The job is submitted to the Spark driver program.

• The driver program communicates with the cluster manager to request resources.

• The cluster manager allocates resources (CPU, memory) to the job.

• The driver program creates DAG (Directed Acyclic Graph) of the job stages and tasks.

• Tasks are then scheduled and executed on worker nodes ...read more
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Q5. Calculate second highest salary using SQL as well as pyspark.
Ans. 
Calculate second highest salary using SQL and pyspark
• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• In pyspark, use orderBy() and take() functions to achieve the same result
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Q6. 2 types of modes for Spark architecture ?
Ans. 
The two types of modes for Spark architecture are standalone mode and cluster mode.
• Standalone mode: Spark runs on a single machine with a single JVM and is suitable for development and testing.

• Cluster mode: Spark runs on a cluster of machines managed by a cluster manager like YARN or Mesos for production workloads.
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Q7. If you want very less latency - which is better standalone or client mode?
Ans. 
Client mode is better for very less latency due to direct communication with the cluster.
• Client mode allows direct communication with the cluster, reducing latency.

• Standalone mode requires an additional layer of communication, increasing latency.

• Client mode is preferred for real-time applications where low latency is crucial.
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Q1. Scenario based. Write SQL and pyspark code for a dataset.
Ans. 
SQL and PySpark code examples for data manipulation and analysis.
• Use SQL for structured queries: SELECT, JOIN, GROUP BY.

• Example SQL: SELECT name, COUNT(*) FROM patients GROUP BY name;

• Use PySpark for big data processing: DataFrame API, RDDs.

• Example PySpark: df.groupBy('name').count().show();

• Optimize queries with indexing in SQL and caching in PySpark.
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Q2. If you have to find latest record based on latest timestamp in a table for a particular customer(table is having history) , how will you do it. Self join and nested query will be expensive. Optimized query...read more
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It included questions related to aptitude and computer science.

Q1. The discussion primarily focused on the last semester project, including the technologies used and their details, as well as some Data Structures and Algorithms (DSA) questions.
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Q1. Asked about if willing for relocation and some other basic question
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It was amazing fabulous fantastic mind blowing

Q1. What was the question
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Q2. What was the question
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Q2. What was the question
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Q1. Tell about yourself
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I am a data engineer with a strong background in programming and data analysis.
• Experienced in designing and implementing data pipelines

• Proficient in programming languages like Python, SQL, and Java

• Skilled in working with big data technologies such as Hadoop and Spark
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Q2. Over all questions in snowflake
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Q1. Rate ur self in sql and snowflake
Ans. 
I rate myself highly in SQL and Snowflake, with extensive experience in both technologies.
• Proficient in writing complex SQL queries for data manipulation and analysis

• Skilled in optimizing queries for performance and efficiency

• Experienced in working with Snowflake for data warehousing and analytics

• Familiar with Snowflake's unique features such as virtual warehouses and data sharing
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Q2. Sql questions and snowflake questions
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