LTIMindtree Junior Data Analyst Interview Questions and Answers

Questions on big data, Hadoop, Spark, Scala, Git, project and Agile.

• Hadoop architecture and HDFS commands for copying and listing files in HDFS

• Spark architecture and Transformation and Action question

• What happens when we submit a Spark program

• Spark DataFrame coding question

• Scala basic program on List

• Git and Github

• Project-related question

• Agile-related

Vertices are nodes and edges are connections between nodes in a directed acyclic graph (DAG).

• Vertices represent the tasks or operations in a DAG.

• Edges represent the dependencies between tasks or operations.

• Vertices can have multiple incoming edges and outgoing edges.

• Edges can be weighted to represent the cost or time required to complete a task.

• Examples of DAGs include data processing pipelines and task scheduling syst

Calculating resources based on cores and memory given with overhead and driver memory

• Calculate the total memory available by multiplying the number of cores with memory per core

• Deduct the overhead memory required for the operating system and other processes

• Allocate driver memory for each executor based on the workload

• Consider the memory requirements for other services like Hadoop, Spark, etc.

• Example: For 16 cores with ...read more

I have successfully led the development of a real-time data processing system, resulting in a 30% increase in efficiency.

• Led the development of a real-time data processing system

• Achieved a 30% increase in efficiency

• Implemented data quality checks to ensure accuracy

I have faced challenges in optimizing data pipelines, handling large volumes of data, and ensuring data quality.

• Optimizing data pipelines to improve efficiency and performance

• Handling large volumes of data to prevent bottlenecks and ensure scalability

• Ensuring data quality by implementing data validation processes and error handling mechanisms

SCD (Slowly Changing Dimensions) manages historical data changes in data warehouses.

• SCD Type 1: Overwrite old data (e.g., updating a customer's address without keeping history).

• SCD Type 2: Create new records for changes (e.g., adding a new row for a customer's address change).

• SCD Type 3: Store current and previous values in the same record (e.g., adding a 'previous address' column).

• Implementation can be done using ETL ...read more

Handling multiple inputs in data sources requires effective integration, transformation, and validation strategies.

• Use ETL (Extract, Transform, Load) processes to consolidate data from various sources.

• Implement data validation checks to ensure data quality from each input source.

• Utilize data orchestration tools like Apache Airflow to manage workflows and dependencies.

• Consider using a message queue (e.g., Kafka) for rea...read more