We’re planning to migrate our analytics platform to IBM Cloud and need to decide between Cloud Object Storage and File Storage for our datasets. We have about 50TB of data that grows by 2-3TB monthly. The data is primarily used for batch analytics jobs that run daily, with occasional ad-hoc queries. Cost is important but so is integration with analytics tools like Spark and our data science workflows. I’ve read that COS is cheaper for large volumes, but I’m concerned about performance for frequent access patterns. File Storage seems more straightforward for mounting to compute instances, but the pricing scales differently. What have others experienced with these storage options for analytics workloads? Any gotchas or best practices to consider?
Performance-wise, COS retrieval can be slower than File Storage for small files, but for analytics workloads dealing with larger files it’s not an issue. We partition our data in COS using a date-based structure which helps with query performance. One thing to watch: COS charges for API calls, so if you have millions of tiny files, those GET requests add up. File Storage might be better if you need true filesystem semantics with lots of random access. But for batch analytics reading large Parquet or CSV files, COS is the way to go.
Having implemented both, here’s my take on COS vs File Storage for analytics. For pricing, COS is clearly more cost-effective at scale - you’re looking at 3-4x cost savings for 50TB+ workloads. The storage tier flexibility (Standard, Vault, Cold Vault) lets you optimize costs based on access patterns. For analytics integration methods, native S3 API connectors are the best approach. Spark’s S3A connector with COS endpoints provides excellent performance for parallel reads. Python’s boto3 and pandas can read directly from COS using s3fs or native methods. Most modern analytics tools support S3 APIs natively. For durability and retrieval performance, COS provides built-in geo-redundancy and 99.99% availability SLA. Retrieval performance depends on your access patterns - sequential reads of large files are fast, but random access to many small files can be slower than File Storage. For batch analytics reading large Parquet files, COS performance is excellent. The key consideration is your workload pattern: if you need true POSIX filesystem semantics with heavy random I/O, File Storage might be worth the premium. But for typical analytics with sequential reads of large datasets, COS offers better economics and scales effortlessly. Use lifecycle policies to automatically move older data to cheaper tiers, and consider Smart Tier for datasets with unpredictable access patterns. The combination of cost efficiency, durability, and S3 API ecosystem makes COS the better choice for most analytics platforms.
The S3 API integration is definitely appealing. How do you handle the analytics integration methods? Do you mount COS as a filesystem or use native S3 connectors in your tools? We’re using a mix of Python pandas, Spark, and some custom R scripts. I’m wondering if there’s a performance difference between accessing COS through s3fs versus using the native boto3 client.