10+ MILEAGE LOGISTICS Interview Questions and Answers

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

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

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

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 reducing the number of partitions without a full shuffle.

• Exampl...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 a cluster management system like Apache Mesos or Kubernet...read more

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 better performance.

• Example: Coalesce can be used after a filter...read more

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: Partitioning sales data by region to analyze sales perform...read more

Use the find command with the -mtime option to find files that are 30 days old in Linux.

• Use the find command with the -mtime option to specify the number of days.

• For example, to find files that are exactly 30 days old: find /path/to/directory -mtime 30

• To find files that are older than 30 days: find /path/to/directory -mtime +30

• To find files that are newer than 30 days: find /path/to/directory -mtime -30

Transformations and actions in Spark, reducing shuffling, and project-related questions.

• Transformations in Spark are operations that create a new RDD from an existing one, while actions are operations that return a value to the driver program.

• Examples of transformations include map, filter, and reduceByKey, while examples of actions include count, collect, and saveAsTextFile.

• To reduce shuffling in Spark, you can use techniques like partitioning, caching, and using appropriate...read more

Vaccum in delta tables helps improve performance by reclaiming space and optimizing file sizes.

• Vaccum operation helps optimize file sizes by removing small files and compacting larger files.

• It helps improve query performance by reducing the amount of data that needs to be scanned.

• Vaccum operation can be scheduled to run periodically to maintain optimal performance.

• It is recommended to run Vaccum on delta tables after major data deletions or updates.

• Example: VACCUM delta.`tabl...read more