Verisk Analytics Data Engineer Interview Questions and Answers

Q1. SCD questions. Iceberg questions
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Q2. Basic python programing, pyspark architechture.
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Q1. Questions on Pyspark
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Q2. Questions on SQL
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Q3. Transformations
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Q4. Questions on Sql optimizations
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Q5. Questions About my current Project
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Q1. Incremental load in pyspark
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Incremental load in pyspark refers to loading only new or updated data into a dataset without reloading the entire dataset.
• Use the 'delta' function in pyspark to perform incremental loads by specifying the 'mergeSchema' option.

• Utilize the 'partitionBy' function to optimize incremental loads by partitioning the data based on specific columns.

• Implement a logic to identify new or updated records based on timestamps or uni...read more
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Q2. Drop duplicates
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Q1. What is Pyspark?
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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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Just focus on the basics of pyspark.

Q1. Normalisation of database, views,stored procedures
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Normalization is a process of organizing data in a database to reduce redundancy and improve data integrity.
• Normalization involves breaking down a table into smaller tables and defining relationships between them.

• It helps in reducing data redundancy and inconsistencies.

• Views are virtual tables that are created based on the result of a query. They can be used to simplify complex queries.

• Stored procedures are precompiled...read more
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