Publicis Sapient Data Engineer Interview Questions and Answers

Implementing strategies to prevent data loss in ETL pipelines is crucial for data integrity and reliability.

• Implement data validation checks at each stage of the ETL process to ensure data integrity.

• Use logging mechanisms to track data flow and identify any discrepancies or failures.

• Incorporate retry mechanisms for failed data transfers to ensure data is not lost.

• Utilize data backups and snapshots to restore data in ca...read more

Dataproc clusters offer flexibility and control for complex workloads, while serverless jobs simplify management for straightforward tasks.

• Dataproc allows for custom configurations, such as specific versions of Spark or Hadoop, which may be necessary for certain applications.

• For large-scale data processing tasks that require fine-tuning of resources, a Dataproc cluster can be more efficient than serverless options.

• Data...read more

If a job fails in the pipeline and data processing cycle is over, it can lead to incomplete or inaccurate data.

• Incomplete data may affect downstream processes and analysis

• Data quality may be compromised if errors are not addressed

• Monitoring and alerting systems should be in place to detect and handle failures

• Re-running the failed job or implementing error handling mechanisms can help prevent issues in the future

Repartition increases the number of partitions in a DataFrame, while coalesce reduces the number of partitions without shuffling data.

• Repartition involves a full shuffle of the data across the cluster, which can be expensive.

• Coalesce minimizes data movement by only creating new partitions if necessary.

• Repartition is typically used when increasing parallelism or evenly distributing data, while coalesce is used for reduc...read more

SQL code to get the city1 city2 distance of table with repeating city1 and city2 values

• Use a self join on the table to match city1 and city2

• Calculate the distance between the cities using appropriate formula

• Consider using a subquery if needed

Data partitioning in a pipeline involves dividing data into smaller chunks for processing and analysis.

• Data can be partitioned based on a specific key or attribute, such as date, location, or customer ID.

• Partitioning helps distribute data processing tasks across multiple nodes or servers for parallel processing.

• Common partitioning techniques include range partitioning, hash partitioning, and list partitioning.

• Example: ...read more

I have handled terabytes of data in my POCs, including data from various sources and formats.

• Handled terabytes of data in POCs

• Worked with data from various sources and formats

• Used tools like Hadoop, Spark, and SQL for data processing

Repartition is used for increasing partitions for parallelism, while coalesce is used for decreasing partitions to reduce shuffling.

• Repartition is used when there is a need for more partitions to increase parallelism.

• Coalesce is used when there are too many partitions and need to reduce them to avoid shuffling.

• Example: Repartition can be used before a join operation to evenly distribute data across partitions for bette...read more

Designing/configuring a cluster for 10 petabytes of data involves considerations for storage capacity, processing power, network bandwidth, and fault tolerance.

• Consider using a distributed file system like HDFS or object storage like Amazon S3 to store and manage the large volume of data.

• Implement a scalable processing framework like Apache Spark or Hadoop to efficiently process and analyze the data in parallel.

• Utilize...read more