Publicis Sapient Senior Data Analyst Interview Questions and Answers

Sample data and its transformations

• Sample data can be in the form of CSV, JSON, or database tables

• Transformations include cleaning, filtering, aggregating, and joining data

• Examples: converting date formats, removing duplicates, calculating averages

Seeking new challenges and opportunities for growth in a more dynamic environment.

• Looking for new challenges and opportunities for growth

• Seeking a more dynamic work environment

• Interested in expanding skill set and knowledge

• Want to work on more innovative projects

Spark architecture is based on a master-slave architecture with a cluster manager to coordinate tasks.

• Spark architecture consists of a driver program that communicates with a cluster manager to coordinate tasks.

• The cluster manager allocates resources and schedules tasks on worker nodes.

• Worker nodes execute the tasks and return results to the driver program.

• Spark supports various cluster managers like YARN, Mesos, and s...read more

There will be 4 stages created in total for the spark job.

• Wide transformations trigger a shuffle and create a new stage.

• Narrow transformations do not trigger a shuffle and do not create a new stage.

• In this case, 3 wide transformations will create 3 new stages and 2 narrow transformations will not create new stages.

• Therefore, a total of 4 stages will be created.

SQL statements are executed in a specific order to ensure accurate results.

• SQL statements are executed in the following order: FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY.

• The FROM clause specifies the tables involved in the query.

• The WHERE clause filters the rows based on specified conditions.

• The GROUP BY clause groups the rows based on specified columns.

• The HAVING clause filters the groups based on specified condi...read more

Identifying root cause of slow executor compared to others

• Check resource utilization of the slow executor (CPU, memory, disk)

• Look for any specific tasks or stages that are taking longer on the slow executor

• Check for network latency or communication issues affecting the slow executor

• Monitor garbage collection and JVM metrics for potential bottlenecks

• Consider data skew or unbalanced data distribution causing slow perform

Enhanced optimization in AWS Glue improves job performance by automatically adjusting resources based on workload

• Enhanced optimization in AWS Glue automatically adjusts resources like DPUs based on workload

• It helps improve job performance by optimizing resource allocation

• Users can enable enhanced optimization in AWS Glue job settings

Optimizing querying in Amazon Redshift involves proper table design, distribution keys, sort keys, and query optimization techniques.

• Use appropriate distribution keys to evenly distribute data across nodes for parallel processing.

• Utilize sort keys to physically order data on disk, reducing the need for sorting during queries.

• Avoid using SELECT * and instead specify only the columns needed to reduce data transfer.

• Use AN...read more

I am a Senior Data Engineer with 5+ years of experience in designing and implementing data pipelines for large-scale projects.

• Experienced in ETL processes and data warehousing

• Proficient in programming languages like Python, SQL, and Java

• Skilled in working with big data technologies such as Hadoop, Spark, and Kafka

• Strong understanding of data modeling and database management

• Excellent problem-solving and communication sk

Developing a real-time data processing system for analyzing customer behavior on e-commerce platform.

• Utilizing Apache Kafka for real-time data streaming

• Implementing Spark for data processing and analysis

• Creating machine learning models for customer segmentation

• Integrating with Elasticsearch for data indexing and search functionality