HDFS vs Apache Cassandra

Developers should learn and use HDFS when building big data applications that require storing and processing petabytes of data, such as in data lakes, log analysis, or machine learning pipelines meets developers should learn and use apache cassandra when building applications that demand high write throughput, fault tolerance, and global scalability, such as iot platforms, real-time analytics, messaging systems, and recommendation engines. Here's our take.

HDFS

Nice Pick

Developers should learn and use HDFS when building big data applications that require storing and processing petabytes of data, such as in data lakes, log analysis, or machine learning pipelines

Pros

• It is particularly valuable in scenarios involving massive, sequential reads and writes, as it provides reliability through replication and scalability by adding more nodes to the cluster
• Related to: apache-hadoop, apache-spark

Cons

• Specific tradeoffs depend on your use case

Apache Cassandra

Developers should learn and use Apache Cassandra when building applications that demand high write throughput, fault tolerance, and global scalability, such as IoT platforms, real-time analytics, messaging systems, and recommendation engines

Pros

• It is ideal for scenarios where data is time-series or event-driven, and when strong consistency can be traded for eventual consistency to achieve better performance and availability
• Related to: nosql, distributed-systems

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. HDFS is a platform while Apache Cassandra is a database. We picked HDFS based on overall popularity, but your choice depends on what you're building.

The Bottom Line

HDFS wins

Based on overall popularity. HDFS is more widely used, but Apache Cassandra excels in its own space.