Luxoft
Bosch Interview Questions and Answers
Q1. How to merge 2 dataframes of different schema?
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.
Q2. What is Pyspark streaming?
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
Q3. Explain the Spark architecture with example
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
Q4. What is Pyspark?
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.
Q5. What is Pyspark SQL?
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.
Q6. Handling ADF pipelines
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
More about working at Luxoft
Interview Process at Bosch
Top Data Engineer Interview Questions from Similar Companies
Reviews
Interviews
Salaries
Users/Month