Relational database management systems for a long time in the past provided for the data needs of businesses adequately such that a good size SQL server would handle up to thousands of transactions storage and analysis. This is because traditional data was structured and easily organized within a fixed schema with structured query language (SQL). However, so much has changed in the past decade with the explosion of data in a wide range of formats. As a result, business demands have changed significantly. RDBMSs do not have the capacity to handle big data.
The need for a faster time-to-market for products; real-time data storage and analysis for structured, semi-structured, and unstructured data; as well as the need to scale fast and easily make non-relational database systems like HBase and MongoDB attractive. These are also commonly referred to as NoSQL database systems as they store data in a distributed non-tabular form. NoSQL systems are designed to handle massive volumes of data, process data in parallel across numerous servers, and scale easily horizontally. As such, NoSQL database systems are the most ideal for big data.
Overview of HBase
HBase, written in Java, is an open-source non-relational database management system that runs on Hadoop Distributed File System (HDFS). HBase stores data in columns and rows while HDFS is a files system in which data is stored in a distributable format. Thus HBase is designed to read and write massive volumes of data in rows and columns in a distributed storage using commodity hardware clusters. A Hadoop course will therefore provide the foundation knowledge for those who intend to use HBase.
HBase presents many advantages. It is linearly scalable, features automatic sharding, and offers consistent reads and writes. Also, HBase provides automatic support to failures as data is replicated across multiple nodes within a cluster. HBase is a great database management tool as it allows random read/write access and real-time processing of large volumes of data.
HBase features
- It is part of the Hadoop ecosystem and allows consistent random read/write access to data
- Auto failover helps in automatic recovery and high data availability for servers in the same region
- Supports both linear and modular scalability thanks to sharding capabilities
- Selective replication of data
- Schema-less column-based data structure
Overview of MongoDB
MongoDB is a non-relational document-oriented general-purpose distributed database that stores data in JSON-like documents known as BSON thus supporting a wide range of schema to enable rich indexing of values and real-time aggregation. Together with the ability to access data easily with the MongoDB ad-hoc query capabilities makes it a flexible and powerful database management system for application developers leveraging the power of the cloud. MongoDB’s distributed data structure also allows it to scale easily horizontally. This makes it a good database system for storing and processing large data volumes.
MongoDB features
- MongoDB supports ad-hoc and document-oriented queries for real-time analytics
- Data is automatically distributed across multiple databases which lead to automatic load balancing
- Thanks to the replication factor, data is highly available in MongoDB and the system fault-tolerant
- Supports sharding for enhanced performance and horizontal scaling.
HBase vs MongoDB
Non-relational database systems like HBase and MongoDB have much in common. They both offer flexibility, easy scaling with sharding, are hailed for great read/write capabilities, and are designed to store and process large volumes of data. However, they are unique in many ways and are designed for different purposes.
| Features | HBase | MongoDB |
| Implementation language | Java | JavaScript, C, C++ |
| Data model | Wide-column store. Stores data in columns and rows in tables. | Key-value store. Stores data in JSON-like documents and collections, and databases |
| Secondary indexes | Does not support secondary indexes | Supports secondary indexes for high performance |
| Supported data models | Data stored as byte arrays in tables. As such input data needs conversion to be stored | Supports multiples types of data formats allowing for efficient data processing and analysis |
| Query model | Uses key-value expressive query language featuring powerful query operators, filtering, projections, comparisons, aggregations, and equality | Uses MongoDB query language (MQL) to run complex queries for real-time analysis and advanced operations. |
| Data replication factor | Selective replication | Master-slave replication |
| Supported operating systems | Linux, Unix, and Windows | Linux, Windows, Solaris, and OS X |
| Application | Ideal for structured data | Ideal for unstructured data as it supports a wide range of data formats |
HBase or MongoDB?
With big data, characterized by volume, velocity, and variety, non-tabular databases have grown to become popular. However, with data security issues being critical now more than ever, organizations seek database management systems on which data can be stored and analyzed securely. Also, open-source software has gained preference over time as they get updated with new features from time to time to enhance their performance. Finally, one that is backed by a vibrant community comes with an added advantage in the form of community support.
While HBase is ideal for string sparse datasets in big data. It is designed to provide full tolerance for key-value workloads with random read/write access and real-time analysis for high volumes of data. HBase uses Zookeeper to maintain high performance. As it runs on HDFS, most organizations that have installed HDFS architecture prefer HBase.
MongoDB, on the other hand, is a great option for workloads with multiple data types. MongoDB is designed to store and process vast amounts of unstructured data thanks to its ability to perform searches by regular expression or by field and index in any field of documents. It features sharding and master-slave replication which makes it easily scalable horizontally across multiple clusters using additional shards. This makes it the developers’ favorite especially for developing social and applications with unpredictable scaling demands hence the need to scale faster horizontally. These applications manage very high volumes of data, typically with flexible data schema.
