PDE Designing Data Processing Systems Practice Question

A startup is building a data lake on Google Cloud. They need to store raw JSON, CSV, and Parquet files from various sources. The files will be accessed by multiple analytics tools, including BigQuery and Dataproc. The startup wants a cost-effective, durable, and highly available storage solution that integrates natively with these services. Which Google Cloud service should they use?

⚠ Common exam trap

The trap here is considering Bigtable or Cloud SQL because they are managed services, but they are not designed for storing raw files in a data lake architecture and lack native integration with analytics tools for file-based access.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Cloud Storage with a Standard storage class.

Cloud Storage is the correct choice for a data lake because it is a durable, highly available, and cost-effective object store that integrates natively with BigQuery, Dataproc, and other Google Cloud analytics services. It supports all file formats and can be organized into buckets with lifecycle policies for cost optimization. The Standard storage class is appropriate for frequently accessed data.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Cloud SQL for MySQL with a large SSD and binary large object (BLOB) storage.
Why it's wrong here
Cloud SQL is a managed relational database, not a data lake storage solution. Storing files as BLOBs in a relational database is inefficient for analytics and does not integrate natively with BigQuery or Dataproc for direct file access. It also scales poorly and incurs higher costs for large volumes of data.
✗
Cloud Bigtable with a column family for each data format.
Why it's wrong here
Bigtable is a NoSQL wide-column database optimized for high-throughput, low-latency workloads, not for storing and analyzing raw files in various formats. It does not natively serve as a data lake for JSON, CSV, or Parquet files, and it would require custom ingestion. It is also more expensive for bulk storage than Cloud Storage.
✗
Persistent Disk attached to a Compute Engine instance, shared via NFS.
Why it's wrong here
Persistent Disk provides block storage for Compute Engine VMs, but it is not a globally accessible object store. Sharing via NFS introduces a single point of failure and does not integrate natively with BigQuery or Dataproc. It lacks the durability, availability, and scalability of Cloud Storage for a data lake.
✓
Cloud Storage with a Standard storage class.
Why this is correct
Cloud Storage is the foundational object store for data lakes on Google Cloud. It provides durable, highly available, and cost-effective storage with native integration into BigQuery, Dataproc, and other analytics services. The Standard class is suitable for frequently accessed data, and lifecycle policies can transition older data to colder classes to optimize cost.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

PDE Designing Data Processing Systems Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question