Data Engineer 3
Data Engineer 3 Interview Questions and Answers
Q1. Spark optimization techniques
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
Q2. Implementation of database join algorithms
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.
Q3. Spark Optimization on JOIN queries
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
Q4. Python program to parse JSON
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
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