Luxoft Data Engineer Interview Questions, Process, and Tips

Q1. What is Pyspark?
Ans. 
Pyspark is a Python API for Apache Spark, a powerful open-source distributed computing system.
• Pyspark is used for processing large datasets in parallel across a cluster of computers.

• It provides high-level APIs in Python for Spark programming.

• Pyspark allows seamless integration with other Python libraries like Pandas and NumPy.

• Example: Using Pyspark to perform data analysis and machine learning tasks on big data sets.
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Q2. What is Pyspark SQL?
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Pyspark SQL is a module in Apache Spark that provides a SQL interface for working with structured data.
• Pyspark SQL allows users to run SQL queries on Spark dataframes.

• It provides a more concise and user-friendly way to interact with data compared to traditional Spark RDDs.

• Users can leverage the power of SQL for data manipulation and analysis within the Spark ecosystem.
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Q3. How to merge 2 dataframes of different schema?
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To merge 2 dataframes of different schema, use join operations or data transformation techniques.
• Use join operations like inner join, outer join, left join, or right join based on the requirement.

• Perform data transformation to align the schemas before merging.

• Use tools like Apache Spark, Pandas, or SQL to merge dataframes with different schemas.
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Q4. What is Pyspark streaming?
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Pyspark streaming is a scalable and fault-tolerant stream processing engine built on top of Apache Spark.
• Pyspark streaming allows for real-time processing of streaming data.

• It provides high-level APIs in Python for creating streaming applications.

• Pyspark streaming supports various data sources like Kafka, Flume, Kinesis, etc.

• It enables windowed computations and stateful processing for handling streaming data.

• Example: C...read more
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Q1. Spark Architecture
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Q2. Explain the Spark architecture with example
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Spark architecture includes driver, cluster manager, and worker nodes for distributed processing.
• Spark architecture consists of a driver program that manages the execution of tasks on worker nodes.

• Cluster manager is responsible for allocating resources and scheduling tasks across worker nodes.

• Worker nodes execute the tasks and store data in memory or disk for processing.

• Example: In a Spark application, the driver progr...read more
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Q1. Questions on SQL complex queries
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Q2. Handling ADF pipelines
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Handling ADF pipelines involves designing, building, and monitoring data pipelines in Azure Data Factory.
• Designing data pipelines using ADF UI or code

• Building pipelines with activities like copy data, data flow, and custom activities

• Monitoring pipeline runs and debugging issues

• Optimizing pipeline performance and scheduling triggers
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Q3. Schedules and triggers
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Q4. About project work and complexity faced and handling issues
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Q1. Salary discussion
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Technical questions from hive , spark Scala and azure

Q1. Technical questions on spark Scala hive
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Q2. Project architecture related questions
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