10+ Neudesic Technologies Interview Questions and Answers

Use left join for computationally efficient way to find customer names from customer profile and transaction tables.

• Use left join to combine customer profile and transaction tables based on customer id

• Left join will include all customers from profile table even if they don't have transactions

• Subquery may be less efficient as it has to be executed for each row in the result set

Transformation in pyspark is lazy evaluation while Actions trigger execution of transformations.

• Transformations are operations that are not executed immediately but create a plan for execution.

• Actions are operations that trigger the execution of transformations and return results.

• Examples of transformations include map, filter, and reduceByKey.

• Examples of actions include collect, count, and saveAsTextFile.

CTE is a temporary result set that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. It is different from a Stored Procedure as it is only available for the duration of the query.

• CTE stands for Common Table Expression and is defined using the WITH keyword.

• CTEs are mainly used for recursive queries, complex joins, and simplifying complex queries.

• CTEs are not stored in the database like Stored Procedures, they exist only for the duration of the query execu...read more

Use SQL query with window function to rank members by transaction amount in each city.

• Use SQL query with PARTITION BY clause to group members by city

• Use ORDER BY clause to rank members by transaction amount

• Select the second highest member for each city

Normalization in SQL is the process of organizing data in a database to reduce redundancy and improve data integrity.

• 1NF (First Normal Form) - Each column in a table must contain atomic values, and there should be no repeating groups.

• 2NF (Second Normal Form) - Table should be in 1NF and all non-key attributes are fully functional dependent on the primary key.

• 3NF (Third Normal Form) - Table should be in 2NF and there should be no transitive dependencies between non-key attribu...read more

Using self join to analyze customer behavior in an e-commerce platform.

• Identifying patterns in customer purchase history

• Analyzing customer preferences based on past purchases

• Segmenting customers based on their buying behavior

Lambda function is a serverless computing service that runs code in response to events and automatically manages the computing resources required.

• Lambda functions are event-driven and can be triggered by various AWS services such as S3, DynamoDB, API Gateway, etc.

• They are written in languages like Python, Node.js, Java, etc.

• Lambda functions are scalable and cost-effective as you only pay for the compute time you consume.

• They can be used for data processing, real-time file pro...read more

Alter is used to modify the structure of a table, while update is used to modify the data in a table.

• Alter is used to add, remove, or modify columns in a table.

• Update is used to change the values of existing records in a table.

• Alter can change the structure of a table, such as adding a new column or changing the data type of a column.

• Update is used to modify the data in a table, such as changing the value of a specific column in a specific row.

List comprehension is a concise way to create lists in Python by applying an expression to each item in an iterable.

• Syntax: [expression for item in iterable]

• Can include conditionals: [expression for item in iterable if condition]

• Example: squares = [x**2 for x in range(10)]

A generator function is a function that can pause and resume its execution, allowing it to yield multiple values over time.

• Generator functions are defined using the 'function*' syntax in JavaScript.

• They use the 'yield' keyword to return values one at a time.

• Generators can be iterated over using a 'for...of' loop.

• They are useful for generating sequences of values lazily, improving memory efficiency.

Broadcast Variables are read-only shared variables that are cached on each machine in a cluster for efficient data distribution.

• Broadcast Variables are used to efficiently distribute large read-only datasets to all nodes in a Spark cluster.

• They are useful for tasks like joining a small lookup table with a large dataset.

• Broadcast variables are cached in memory on each machine to avoid unnecessary data shuffling during computation.