Analysis of the Proposed Solution

Let's evaluate each component of the proposed solution against the stated requirements:

1. Data Scientists Cluster (Standard Cluster)

✅ MEETS REQUIREMENTS

• Requirement: Each data scientist must have their own cluster that terminates automatically after 120 minutes of inactivity.
• Standard Cluster Behavior: Standard clusters automatically terminate after 120 minutes of inactivity by default, which perfectly matches the requirement.
• Language Support: Data scientists need Scala and R support. Standard clusters support all languages including Scala, Python, SQL, and R.

2. Data Engineers Cluster (High Concurrency Cluster)

✅ MEETS REQUIREMENTS

• Requirement: Data engineers must share a cluster and use Python and SQL.
• High Concurrency Cluster Benefits: High Concurrency clusters are specifically designed for multiple users sharing resources efficiently.
• Language Support: Data engineers only need Python and SQL, which are fully supported by High Concurrency clusters.

3. Jobs Cluster (High Concurrency Cluster)

❌ DOES NOT MEET REQUIREMENTS

• Requirement: Jobs need to run notebooks using Python, Scala, and SQL.
• Critical Issue: High Concurrency clusters do not support Scala workloads. High Concurrency clusters can only run workloads developed in SQL, Python, and R.
• Technical Reason: The performance and security benefits of High Concurrency clusters are achieved by running user code in separate processes, which is not possible with Scala due to JVM limitations.

Why the Solution Fails

The solution fails because the job cluster configuration is incompatible with the Scala requirement. While:

• Standard clusters for data scientists ✓ Correct
• High Concurrency clusters for data engineers ✓ Correct
• High Concurrency clusters for jobs ✗ Incorrect (Scala not supported)

Correct Alternative

For the job cluster, a Standard cluster should be used instead of a High Concurrency cluster to ensure full Scala support alongside Python and SQL capabilities.

Conclusion

Despite two out of three cluster configurations being correct, the solution does not meet the goal due to the fundamental incompatibility between High Concurrency clusters and Scala workloads for the job cluster requirement.