Nerdy Turtlez Interview Questions and Answers

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.

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: Creating a streaming application to process incoming data f...read more

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 program sends tasks to worker nodes for parallel processing of ...read more

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.

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.