Adidas Data Engineer Interview Questions and Answers

Case study is shared two days before the interview process

Q1. General spark optimization techniques
Ans. 
Spark optimization techniques improve performance and efficiency of data processing.
• Partitioning data to distribute workload evenly

• Caching frequently accessed data to avoid recomputation

• Using appropriate data formats like Parquet for efficient storage and retrieval

• Optimizing shuffle operations by tuning parameters like shuffle partitions

• Avoiding unnecessary data shuffling by using broadcast joins
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Q1. Optimizations in Databricks
Ans. 
Optimizations in Databricks involve tuning cluster configurations, optimizing code, and utilizing caching.
• Tune cluster configurations to allocate resources efficiently

• Optimize code by using efficient algorithms and data structures

• Utilize caching to store intermediate results and avoid recomputation
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Q2. Where broadcast join is used and why
Ans. 
Broadcast join is used when one of the tables is small enough to fit in memory of all the nodes in the cluster, reducing shuffle and improving performance.
• Broadcast join is used to optimize join operations by broadcasting the smaller table to all nodes in the cluster.

• It is beneficial when one of the tables is small enough to fit in memory of all nodes, reducing the need for shuffling data across the network.

• Broadcast j...read more
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