Redacción Tokio | 31/01/2023
The Big Data potential doesn’t stop growing, including the ecosystem of applications and tools used by professionals in the field, which is becoming more and more extensive. Apache Hadoop was born as part of this context as a framework dedicated to the processing of large amounts of data. Included here is HBase, a non-relational, distributed database management.
In this article, we are going to see what HBase is exactly, including some bits of its history, its integration into Hadoop and the kind of advantages and disadvantages its use can have in the field of Big Data. A growing discipline that is constantly on the look for new specialized professionals.
In this sense, in order to be able to find a good position in this field, it is necessary to access proper training. Thus, a Big Data course with a specialization in Apache Hadoop can open many doors for those who opt for this type of training. We will also talk about this throughout the article, but, for now, let’s focus on defining HBase.
What is Apache Hbase?
Apache HBase started as a project intended to address the needs of a specific company: Powerset. This company needed a tool that could massively process large amounts of data to be then used as part of searches involving natural language. In time, the applications of this system have extended so that HBase has ended up being integrated into HDFS, one of the components of the Apache Hadoop ecosystem.
As we’ve mentioned above, HBase is a distributed and non-relational database manager (NoSQL). This means that it’s a set of databases, all of which are located at different points in the same cluster of computers, but maintain a logical relationship between them. On the other hand, the term NoSQL also refers to these databases’ storage of all kinds of unstructured data and the fact that they don’t use SQL as a query language.
Facebook integrated HBase into its messaging services in 2010.
Apache HBase is not meant to replace traditional databases, but it is widely used as a data manager system for Big Data projects thanks to its easy integration with Apache Hadoop. Also, it includes an extension that provides an SQL layer to HBase and allows it to work with relational databases as well.
Thus, HBase is currently employed by a multitude of companies for processing, treating and analyzing data. Its integration with Hadoop allows for easy use within this framework’s distributed system. This facilitates the development of Big Data work for all types of companies, since it lowers costs and eliminates the need for a dedicated IT department.
Apache Hbase Features and Applications
Now we’ve provided an answer to ‘what is HBase’ and gone through some of its history, let’s see its main characteristics and some of its applications within the field of Big Data. Among the main characteristics of HBase we find that this database manager is:
- Scalable: Big Data requires the use of tools that are easily scalable. Being a distributed database manager, HBase is easily scalable since, by increasing the number of computers in the cluster, it’s possible to increase its processing capacity.
- Integration: as we’ve mentioned above, HBase integrates with Apache Hadoop and does so both at source and destination. This simplifies its use since it can become part of the ecosystem of applications, tools and technologies focused on working in Big Data.
- Automation: Apache HBase can be used automatically and can help automate processes that are part of data processing for Big Data.
- Compatibility: it presents an API for Java to be used on the client side. This makes it easy to use different Java elements as part of working with this tool.
On the other hand, the following stand out among the HBase applications:
- Writing heavy applications
- Providing quick and random access to data that is available within this database manager
- Social networking platforms and Internet search engines usually integrate it internally for different services
- Hosting large data tables
Thus, this non-relational database manager has become a tool that operates as part of the upper application layers of Apache Hadoop, becoming integrated into the distributed file system (HDFS) of this framework.
Apache Hbase: advantages and disadvantages
Once we have gone through all the Apache HBase features, it is also important to see its advantages and disadvantages in working within data analysis. Let’s start with the advantages of HBase:
- This type of distributed and non-relational databases presents a large storage capacity. A table within HBase can consist of hundreds of millions of rows and millions of columns.
- HBase allows professionals to search for different versions, as well as historical data. This way it’s easy to find the version of the information that is needed.
- When there is a high information load, the system can be escalated simply by adding new machines to the computer cluster.
- Integration with Hadoop ensures data reliability for Big Data analytics processes.
- Potential failures can be avoided since each part of the cluster has backup copies of all the elements in the database so that, if one fails, the rest can continue working.
However, despite all its advantages, HBase also has some disadvantages:
- Its Java-based implementation and Hadoop architecture makes this system’s API more suitable for projects developed using this programming language.
- The nodes’ development environment presents a number of dependencies, making its configuration problematic.
- It requires a large memory space and, as its batch analysis is configured and built for HDFS, its performance when it comes to reading data is not particularly high.
- It’s not particularly efficient compared to other non-relational databases.
Despite its drawbacks, HBase is still a key part of Hadoop and thus useful for companies looking for low-cost Big Data solutions in projects developed using Java architecture.
Get specialized training in Big Data!
Little by little, you’re learning and mastering more Big Data concepts, a field in which keeping up to date is crucial, as much as it is to get proper training if you want to develop your professional career in this field. Apache HBase is just one of the many tools used in Big Data. But there are more that you should know about.
As such, as we’ve mentioned above, Apache Hadoop represents one of the many tools that companies that are now focused on Big Data work with. You can learn to manage this ecosystem with the right training. At Tokyo School, we offer our course in Big Data with a Hadoop specialization where you will be trained by experts and professionals in the field. Find out first hand?
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