Data Engineer 3 Interview Questions and Answers

Spark optimization techniques

• Partitioning data to optimize parallelism

• Caching frequently used data to avoid recomputation

• Using broadcast variables to reduce data shuffling

• Avoiding unnecessary transformations

• Tuning memory and executor settings

• Using efficient data formats like Parquet or ORC

• Using appropriate join strategies

Database join algorithms are used to combine data from multiple tables based on a common column.

• Different join algorithms include nested loop join, merge join, and hash join.

• Nested loop join is used for small tables, merge join for sorted data, and hash join for large tables.

• Join algorithms can impact query performance and should be chosen based on data size and distribution.

Optimizing JOIN queries in Spark involves partitioning data, using broadcast joins, and optimizing shuffle operations.

• Partition data to avoid shuffling unnecessary data across the network

• Use broadcast joins for small tables that can fit in memory of each executor

• Optimize shuffle operations by tuning shuffle partitions and memory settings

Python program to parse JSON

• Use the json module in Python to parse JSON data

• Use the loads() method to load JSON data into a Python dictionary

• Access the data in the dictionary using keys