HBase: Features, Advantages, and Disadvantages
The potential of Big Data continues to grow, and the ecosystem of applications and tools used by professionals in this field is also becoming increasingly extensive. Apache Hadoop was born in this context as a framework designed to process large volumes of data. This includes HBase, a non-relational distributed database.
In this article, we will look at what HBase is, including some aspects of its history, its integration into Hadoop, as well as the advantages and disadvantages of its use in the Big Data field. This is a growing discipline that is constantly looking for new specialists.
What is Apache HBase?
Apache HBase started as a project designed to solve the tasks of a specific company: Powerset. This company needed a tool capable of massively processing large volumes of data for later use in natural language search. Over time, the scope of this system expanded so much that HBase became integrated into HDFS, one of the components of the Apache Hadoop ecosystem.
As we mentioned above, HBase is a distributed non-relational DBMS (NoSQL). This means that it is a set of databases located at different points in a single computer cluster, but maintaining a logical connection between them. On the other hand, the term NoSQL also means that these databases store all kinds of unstructured data and that they do not use SQL as a query language.
Facebook integrated HBase into its messaging services in 2010.
Apache HBase is not intended to replace traditional databases, but is widely used as a data management system for Big Data projects due to its easy integration with Apache Hadoop. In addition, the system includes an extension that provides an SQL layer for HBase and allows it to work with relational databases as well.
Thus, currently HBase is used by many companies for data processing and analysis. Its integration with Hadoop makes it easy to use within the distributed system of this framework. This facilitates the development of Big Data work for all types of companies, as it reduces costs and eliminates the need for a dedicated IT department.
Features and Applications of Apache HBase
Now that we have answered the question "what is HBase" and discussed its history, let's look at its main characteristics and some application areas in the Big Data field. The main characteristics of HBase include that this database manager is:
- Scalability: Big Data requires the use of easily scalable tools. HBase, being a distributed database manager, scales easily because increasing the number of computers in the cluster can increase its computing power.
- Integration: As we noted above, HBase integrates with Apache Hadoop, both on the source and destination cluster. This simplifies its use, as it can become part of the ecosystem of applications, tools, and technologies focused on Big Data.
- Automation: Apache HBase can be used automatically and helps automate processes that are part of data processing for Big Data.
- Compatibility: It provides a Java API for client-side use. This allows easy use of various Java elements to work with this tool.
On the other hand, among the application features of HBase, the following stand out:
- Developing heavy applications
- Providing fast and random access to data available in this database manager
- Social media platforms and Internet search engines often integrate it internally to provide various services
- Hosting large data tables.
Thus, this non-relational database manager has become a tool working as part of the upper application layers of Apache Hadoop, integrated into the distributed file system (HDFS) of this framework.
Apache HBase: Advantages and Disadvantages
After reviewing all the capabilities of Apache HBase, it is also important to see its advantages and disadvantages when working within data analysis. Let's start with the advantages of HBase:
- This type of distributed and non-relational database has a large data storage capacity. A table in HBase can consist of hundreds of millions of rows and millions of columns.
- HBase allows specialists to search across different versions as well as historical data. Thus, you can easily find the version of information that is needed.
- Under high information load, the system can be scaled up simply by adding new machines to the computing cluster.
- Integration with Hadoop ensures data reliability for Big Data analytics processes.
- Possible failures can be avoided because each part of the cluster has backups of all database elements, so if one fails, the others can continue working.