Key Factors for Cluster Sizing:

1. Data Volume: Assess the total data to be processed hourly - in our case, a whopping 1 terabyte!

2. Task Complexity: Consider the complexity of ETL tasks to determine the required computational power.

3. Memory Requirements: Evaluate the memory needs of your Spark jobs for efficient processing.

4. Parallelism: Adjust the number of executors and cores per executor based on parallelism needs.

Step-by-Step Cluster Sizing Calculation:

1. Determine Executors: Divide the total data by the preferred executor memory to find the number of executors. Adjust for overhead and available resources.

Example: (1TB / 32GB (executor memory)) = 31.25 executors (round up to ensure sufficient resources).

2. Calculate Cores per Executor: Consider the task parallelism requirement and divide the total available cores accordingly.

Example: If 100 cores are available, \(100 cores / 31 executors\) = ~3 cores per executor.

3. Memory Overhead: Allow for memory overhead for non-executor JVM processes.

Example: Reserve 10% for overhead, so (32GB + 10%) = 35.2GB per executor.

4. Parallelism Adjustment: Fine-tune the configuration based on Spark job characteristics and required parallelism.

Example: Adjust cores per executor or executor count based on the nature of ETL tasks.

5. Testing and Optimization: Validate the chosen configuration with sample data to ensure efficiency. Tweak parameters based on job performance

When it comes to spark UI, you should be able to look at the stages and tasks which are running. Input /output shuffle data size, time takena by tasks are uniformly distributed or not.

Microsoft interview process usually comprises of 2-3 technical rounds. The first round is DSA coding, and other rounds related to data engineering coding in SQL, PySpark etc. and system design of a use case. After tech rounds there are behavioral rounds.

Deciding on the size of a Spark cluster and mastering the understanding of Spark UI, as well as preparing for a data engineer role at Microsoft, are all important aspects of working with big data and pursuing a career in data engineering. Let's break down each of these topics:

1. Deciding the Size of a Spark Cluster

The size of a Spark cluster is determined based on several factors, including the volume of data to be processed, the complexity of the computations, and the desired execution time. Here are key considerations:

• Data Volume: The amount of data you need to process is a primary factor. Larger datasets require more memory and computing power to process efficiently.
• Job Complexity: Complex operations (like joins, aggregations, and window functions) can increase the resource requirements.
• Performance Requirements: Desired execution times can dictate the need for more resources. Faster processing times might necessitate larger clusters.
• Resource Utilization: Monitoring existing jobs can help you understand how resources are used and identify bottlenecks.
• Cost Constraints: Budget limitations may also influence the size of the cluster you can afford to operate.

2. Mastering Spark UI

Spark UI is a web interface provided by Apache Spark that displays information about an application's execution in a structured format. Here's how to master it:

• Spend Time on it: Regularly review Spark UI while running different types of jobs to understand how resource utilization changes with job complexity.
• Understand the Tabs: Familiarize yourself with the different tabs (Jobs, Stages, Tasks, Storage, Environment, etc.) and the information each provides.
• Analyze Stage Details: Look into stage details to understand task execution, shuffle read/write operations, and potential bottlenecks.
• Optimization: Use Spark UI to identify opportunities for optimization, such as by modifying partitioning or caching data.
• Community Resources and Documentation: Leverage official documentation and community tutorials to deepen your understanding.