10+ ShopSe Interview Questions and Answers

Data flow is a visual representation of data movement and transformation. ADF pipeline is a set of activities to move and transform data.

• Data flow is a drag-and-drop interface to design data transformation logic

• ADF pipeline is a set of activities to orchestrate data movement and transformation

• Data flow is more flexible and powerful than ADF pipeline

• Data flow can be used to transform data within a pipeline or as a standalone entity

AFD is not a commonly used term in data engineering. Can you provide more context?

Challenges faced during data migration include data loss, compatibility issues, downtime, and security concerns.

• Data loss: Ensuring all data is successfully transferred without any loss or corruption.

• Compatibility issues: Ensuring data formats, structures, and systems are compatible for seamless migration.

• Downtime: Minimizing downtime during migration to avoid disruption to operations.

• Security concerns: Ensuring data security and privacy are maintained throughout the migratio...read more

Yes, nested for each is possible in ADF.

• Nested for each can be used to iterate through nested arrays or objects.

• It can be used in mapping data flows and pipelines.

• Example: For each customer, for each order, for each item in order.

• It can improve performance by reducing the number of activities in a pipeline.

Coalesce is used to return the first non-null value among its arguments, while reparation is not a standard function in SQL.

• Coalesce is a standard SQL function, while reparation is not.

• Coalesce returns the first non-null value among its arguments.

• Reparation is not a standard SQL function and may refer to a custom function or process specific to a certain system or application.

To delete duplicates from a database, you can use SQL queries to identify and remove duplicate records.

• Use the DISTINCT keyword in a SELECT query to retrieve unique records

• Identify duplicate records using GROUP BY and HAVING clauses

• Delete duplicate records using DELETE statement with subquery to keep only one instance

Explanation of repartition vs coalesce and dag vs lineage in data engineering

• Repartition: increases or decreases the number of partitions in a DataFrame or RDD

• Coalesce: decreases the number of partitions in a DataFrame or RDD

• DAG (Directed Acyclic Graph): a graph that represents the flow of data and operations in a Spark job

• Lineage: the history of transformations that were applied to a RDD or DataFrame

• Repartition is a shuffle operation and can be expensive, while coalesce is a...read more

Code to print reverse of string

• Use a loop to iterate through the characters of the string in reverse order

• Append each character to a new string to build the reversed string

• Return the reversed string

Dataframes in Pyspark are distributed collections of data organized into named columns.

• Dataframes are similar to tables in a relational database.

• They can be created from various data sources like CSV, JSON, Parquet, etc.

• Dataframes support SQL queries and transformations using PySpark functions.

Yes, I am ready to travel on site for data engineering projects.

• I am willing to travel for client meetings, project kick-offs, and on-site troubleshooting.

• I understand the importance of face-to-face interactions in project delivery.

• I have previous experience traveling for work, such as attending conferences or training sessions.

• I am flexible with my schedule and can accommodate last-minute travel if needed.

Repartition is used to increase or decrease the number of partitions in a DataFrame, while coalesce is used to decrease the number of partitions without shuffling data.

• Repartition involves shuffling data across the network, which can be expensive in terms of performance and resources.

• Coalesce is a more efficient operation as it minimizes data movement by only creating new partitions if necessary.

• Example: Repartition(10) will create 10 partitions in a DataFrame, while coalesce...read more