Skill Guide
The operational knowledge of provisioning, managing, and utilizing cloud services (compute, storage, databases) from providers like AWS, GCP, or Azure specifically to build, run, and scale data processing and analytics workloads.
Scenario
You have a CSV file with sales data and need to host a simple web dashboard (e.g., using Streamlit or Dash) that visualizes it, making it accessible to your team.
Scenario
Your company needs a daily report from a third-party API (e.g., weather data, social media metrics). The data must be cleaned, transformed, and stored in a queryable database for analysis.
Scenario
As a data architect, you must design a system that ingests batch (database extracts) and real-time (clickstream) data into a central repository, enabling both SQL analytics and machine learning, while enforcing fine-grained access control and data cataloging.
These are the primary managed services for storage, compute, analytics, and ML. The choice depends on existing provider ecosystem, specific feature needs (e.g., BigQuery's serverless SQL), and team expertise. A practitioner must know equivalent services across providers to evaluate trade-offs.
IaC tools are non-negotiable for reproducible, version-controlled cloud environments. Orchestration tools (like managed Airflow) are critical for scheduling, monitoring, and managing complex data pipelines across multiple services.
Cloud cost is a direct operational expense. These tools are used proactively to set budgets, analyze spending by service/tag, and set alerts. Monitoring tools track resource utilization and application performance, which is essential for optimizing cost and ensuring pipeline reliability.
Answer Strategy
Test knowledge of NoSQL vs. SQL trade-offs and service selection. Use a decision framework: 1) Identify data model (semi-structured -> NoSQL). 2) Identify access pattern (low latency, key-value -> DynamoDB/Cosmos DB/Bigtable). 3) Discuss scaling: provisioned vs. on-demand capacity, partition key design to avoid hotspots, and indexing strategies for query patterns. Sample: 'I would choose a managed NoSQL database like DynamoDB. It's optimized for JSON document storage and single-digit millisecond latency. For scaling, the critical factor is partition key design to distribute traffic evenly. I'd start with on-demand capacity mode for unpredictable workloads, then move to provisioned capacity with auto-scaling once patterns are clear, while using Global Tables if multi-region replication is required.'
Answer Strategy
Tests systematic debugging and cost-optimization skills. Use a structured approach: 1) **Isolate the bottleneck**: Was it ingestion, transformation, or loading? Use cloud monitoring to identify slow stages. 2) **Diagnose root cause**: For cost, was it idle resources, data scanning in queries, or data egress? For speed, was it undersized compute, poor serialization, or lack of partitioning? 3) **Implement & Validate**: Apply fix (e.g., change file format from CSV to Parquet, right-size instance, add filtering earlier in the pipeline) and measure improvement. Sample: 'Our nightly Spark job on EMR was taking 6 hours. Using CloudWatch and Spark UI, I found the shuffle stage was the bottleneck due to skewed joins. I implemented salting on the join key to distribute the load evenly and switched the output format from CSV to Parquet with Snappy compression. This reduced runtime to 45 minutes and cut S3 storage costs by 70%.'
1 career found
Try a different search term.