HDFS still does one thing really well store large files across multiple machines with solid fault tolerance. It’s great for batch workloads and works like a charm when paired with Spark, Hive, or traditional Hadoop jobs. Once you’ve set it up right, it’s stable and does its job quietly in the background. For old school, on prem big data pipelines, it’s a dependable workhorse. Review collected by and hosted on G2.com.
Let’s be real, HDFS is not keeping up with the times. In today’s world of cloud-native, serverless, auto-scaling storage, HDFS feels like using a Nokia in an iPhone world. Scaling means more hardware, more headaches. Managing NameNode/SecondaryNameNode is like babysitting one wrong move and your cluster throws a tantrum.
It handles large files well, but feed it too many small files and it chokes. It also lacks the flexibility and cost efficiency of cloud storage, no managed service feel, and don’t even ask about object-level access.
Security, upgrades, and maintenance? A whole job in itself. You’ll end up needing a dedicated team just to keep things smooth. Review collected by and hosted on G2.com.
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Organic review. This reviewer was offered a nominal incentive as thanks for completing this review.
