Choosing the Right NoSQL Database: MongoDB, Cassandra, or HBase?

Hadoop has earned credibility for many big‑data applications, but in practice NoSQL databases are more widely deployed and continue to evolve. Although Hadoop can be a straightforward storage choice, selecting the appropriate NoSQL solution among over 100 options is a critical decision.

Selection bias

Martin Fowler notes that any reasonably sized enterprise uses multiple data‑storage technologies, and developers rarely have time to master many of them.

The market now concentrates on three major NoSQL databases: MongoDB, Cassandra (developed mainly by DataStax and originated at Facebook), and HBase (tightly coupled with Hadoop and developed by the same community). Redis is deliberately excluded because it serves primarily as a high‑speed in‑memory cache rather than a big‑data store.

LinkedIn’s 451 research shows MongoDB, Cassandra and HBase as the most attractive options, with Oracle, SQL Server and MySQL dominating the relational side.

Interviews with product leaders—Kelly Stirman (MongoDB), Patrick McFadin (DataStax Cassandra), and Justin Kestelyn (Cloudera HBase)—provide insight into why these three technologies stand out.

The world is built on unstructured data

Today’s data explosion cannot be neatly organized into rows and columns of a relational database. Mobile, social, and cloud computing generate massive volumes of unstructured data; estimates suggest 90 % of the world’s data was created in the past two years, and 80 % of commercial data is unstructured, growing twice as fast as structured data.

Consequently, data‑management requirements have outgrown the capabilities of traditional RDBMSs, prompting organizations to adopt NoSQL and Hadoop as alternatives for operational applications and data‑mining workloads.

MongoDB: built for developers

According to Stirman, MongoDB offers a balanced approach suitable for many applications, combining relational‑like functionality with horizontal scalability and flexible data models. Its ease of learning makes it the first NoSQL choice for many developers.

MongoDB’s document‑oriented storage simplifies query writing, understanding, and optimization, but it is optimized for OLTP workloads and does not support complex transactions, making it less suitable for heavy transactional processing.

Cassandra: scalable and reliable

Cassandra earns favor for its ability to handle massive clusters, providing built‑in replication and simple scaling—adding a new node is as easy as starting a machine and informing Cassandra of the new node.

Its strong scalability, high write throughput, and reasonable query performance make it a high‑performance solution, despite a learning curve that some claim requires a “PhD.” However, McFadin argues that core features can be mastered within hours.

HBase: Hadoop’s close partner

HBase, a column‑family key‑value store like Cassandra, shares a lineage with Hadoop and Google’s Bigtable. It offers fast random reads and writes, complementing Hadoop’s batch‑oriented throughput, and integrates with SQL‑like tools such as Impala, Phoenix, Hive, and Cloudera Search.

HBase scales horizontally across any number of servers, automatically sharding data while maintaining consistency and performance. Its tight integration with the Hadoop ecosystem makes data easily accessible for developers.

Each of these databases has distinct strengths and weaknesses, but together they dominate the NoSQL landscape for big‑data technologies. While future innovations may challenge their positions, many developers and enterprises have already committed to MongoDB, Cassandra, and HBase.