LTIMindtree Data Engineering Specialist Interview Questions and Answers

Q1. Spark memory management
Ans. 
Spark memory management optimizes resource allocation for efficient data processing in distributed computing environments.
• Spark uses a unified memory management model that divides memory into execution and storage regions.

• The default memory fraction for execution is 60%, while 40% is allocated for storage, but these can be configured.

• Spark employs a mechanism called 'Tungsten' for off-heap memory management, which redu...read more
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Q2. Performance optimization in spark
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Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing best practices.
• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions

• Optimize code by reducing unnecessary shuffling, using efficient transformations, and caching intermediate results

• Utilize best practices like using data partitioning, avoiding unnecessary data movements, and leveragi...read more
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Q1. Transformations and actions in spark
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Transformations and actions are key concepts in Apache Spark for processing data.
• Transformations are operations that create a new RDD from an existing one, like map, filter, and reduceByKey.

• Actions are operations that trigger computation and return a result to the driver program, like count, collect, and saveAsTextFile.
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Q2. Glue context and spark context
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Q1. About your career so far
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Q2. Previous organizations and projects worked
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I have worked at ABC Company as a Data Engineer, where I led projects on data pipeline development and optimization.
• Led projects on data pipeline development and optimization

• Worked at ABC Company as a Data Engineer
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Q1. Write a query to identify the duplicate record and delete it using SQL.
Ans. 
Query to identify and delete duplicate records in SQL
• Use a combination of SELECT and DELETE statements

• Identify duplicates using GROUP BY and HAVING clauses

• Delete duplicates based on a unique identifier or combination of columns
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Q2. Spark Architecture ?
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Spark architecture enables distributed data processing using resilient distributed datasets (RDDs) and a master-slave model.
• Spark consists of a driver program that coordinates the execution of tasks across a cluster.

• The cluster manager (like YARN or Mesos) allocates resources for Spark applications.

• Data is processed in parallel using RDDs, which are immutable collections of objects.

• Spark supports various data sources, ...read more
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Q3. Optimisation techniques used in your project?
Ans. 
Various optimisation techniques were used in my project to improve performance and efficiency.
• Implemented indexing to speed up database queries

• Utilized caching to reduce redundant data retrieval

• Applied parallel processing to distribute workloads efficiently

• Optimized algorithms to reduce time complexity

• Used query optimization techniques to improve database performance
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Q4. What Is AWS Lambda? How does it work?
Ans. 
AWS Lambda is a serverless computing service provided by Amazon Web Services.
• AWS Lambda allows you to run code without provisioning or managing servers.

• It automatically scales based on the incoming traffic.

• You only pay for the compute time you consume.

• Supports multiple programming languages like Node.js, Python, Java, etc.

• Can be triggered by various AWS services like S3, DynamoDB, API Gateway, etc.
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Q5. How do you handle incremental data?
Ans. 
Handle incremental data by using tools like Apache Kafka for real-time data streaming and implementing CDC (Change Data Capture) for database updates.
• Utilize tools like Apache Kafka for real-time data streaming

• Implement CDC (Change Data Capture) for tracking database updates

• Use data pipelines to process and integrate incremental data

• Ensure data consistency and accuracy during incremental updates
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Q6. Project Architecture?
Ans. 
Project architecture defines the structure and components of a data engineering project, ensuring scalability and efficiency.
• Define data sources: Identify where data will come from, e.g., databases, APIs, or IoT devices.

• Choose a data storage solution: Options include data lakes (e.g., AWS S3) or data warehouses (e.g., Snowflake).

• Implement data processing: Use ETL (Extract, Transform, Load) tools like Apache Spark or Ap...read more
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Q7. What is a Catalyst optimiser?
Ans. 
Catalyst optimizer is a query optimization framework in Apache Spark that improves performance by applying various optimization techniques.
• It is a query optimization framework in Apache Spark.

• It improves performance by applying various optimization techniques.

• It leverages techniques like predicate pushdown, column pruning, and constant folding to optimize queries.

• Catalyst optimizer generates an optimized logical plan a...read more
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Conducted by CITI Karat. 2 SQL coding was there. Total interview was 1 hr. First 15 min was introduction. 25 min for 1st question and 20 min for 2nd question. The questions are little bit tricky.

Q1. Types of indexes and uses
Ans. 
Indexes in databases help improve query performance by allowing faster data retrieval.
• Types of indexes include clustered, non-clustered, unique, and composite indexes.

• Clustered indexes physically reorder the data in the table based on the index key.

• Non-clustered indexes create a separate structure that includes the indexed columns and a pointer to the actual data.

• Unique indexes ensure that no two rows have the same val...read more
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Q2. Query optimization and performance tuning
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Q3. Ddl, dml, TCL, scl, dql
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Q4. Views, function, sp, trigger
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Q1. Explain you last project end to end
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Q2. Spark and sql questions
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Q1. Sql and pyspark coding questions
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Q2. Spark Architecture questions
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