Big Data is one of the fastest-growing sectors. From tech giants such as Facebook to financial institutions, everyone is using big data to enhance their operations. And one of the most popular big data solutions is Hadoop.
To learn about Hadoop, you’ll need to learn about all of its major components. That’s why in this article, we’ll be discussing HBase, an essential part of Hadoop. We’ll discuss HBase basics such as its architecture, history, and applications. You can bookmark this article for future reference.
Let’s get started.
What is HBase?
Similar to Google’s Big Table, HBase is a data model that provides you with quick access to large quantities of structured data. It’s a product of the Apache Software Foundation and is a part of the Hadoop project. It’s written in Java and is a non-relational and open-source distributed database. It runs on the Hadoop Distributed File System (HDFS), the storage component of Hadoop.
HBase is distributed, consistent, multi-dimensional, and sparse. You can use it with vast quantities of data, variable schema, and many other requirements.
You might wonder what Sparse data is. Well, it’s similar to looking for a needle in a haystack.
History of HBase
Before we talk about its features and functions, you should know about its history. Google had released its paper on BigTable in 2006, and after that, developers created the first HBase prototype in 2007.
The first version of HBase arrived in the market in October of 2007 alongside Hadoop. In 2008, it became the subproject of Hadoop, and in 2010, it became an Apache top-level project. You can say that it developed side by side with Hadoop and its other major components.
Why Do We Need HBase?
Before big data, RDBMS used to be the leading solution for data storage problems. But as the amount of data increased, companies felt the need for a better data storage and management solution. That’s when Hadoop arrived.
It uses a distributed storage system and has MapReduce for processing the data. Hadoop has multiple components, such as HDFS and MapReduce.
HBase is among those essential components. Its features make it a crucial member of the Hadoop ecosystem. It allows you to work on vast quantities of data quickly. It also gives you the highly secure management of your data. You can back MapReduce jobs with HBase Tables as well.
Moreover, Hadoop is capable of performing batch processing only. It only sequentially accesses data. Tools like HBase and MongoDB enable Hadoop to access the data randomly and not in a sequential manner.
Differences Between HDFS and HBase
As both HDFS and HBase are components of Hadoop, it can be a little confusing for anyone to understand the differences among them, even though they are very different and perform separate tasks.
HDFS is the distributed file system of Hadoop, and you use it for storing vast amounts of data. HBase, on the other hand, is a database that’s based on HDFS. You can’t look up individual records fast in HDFS, but you can with HBase.
HDFS offers high latency batch processing, while HBase gives low latency access. You get sequential access to your files in HDFS, but with HBase, you get random access. Overall, HBase increases the speed of specific operations you can perform with HDFS.
Architecture of HBase
We can define HBase architecture as a column-focused key-value store of data. As we’ve established before, it works perfectly on top of HDFS by enhancing its accessibility and speed of operation. The three primary parts of HBase are:
- Region Servers
- HMaster Server
HMaster is responsible for administrative functions and coordination of Region servers. Zookeeper is responsible for the configuration information and distributed synchronization.
Storage in HBase
This HBase training blog would be incomplete without discussing its storage mechanism. We’ve mentioned already that HBase is a column-oriented database, and it sorts its tables by rows. The schema in HBase defines column families that are key-value pairs. One table can have many column families, and a column family can have multiple columns. Every cell on the table has a timestamp.
We can break it down in the following way:
- A table has multiple rows
- A row has multiple column families
- A column family has various columns
- A column has different key-value pairs
Row Oriented vs. Column Oriented
You know that HBase is a column-oriented database, but you might what that means. Well, a row-oriented database is excellent for Online Transaction Processes, whereas a column-oriented database is excellent for Online Analytical Processing. Similarly, the former is suitable to work with small quantities of rows and columns, while the latter is suitable for large amounts of the same.
Due to the ability of HBase to enhance accessibility and speed of data storage, it finds applications in many industries. You’ve read in the history of HBase already that it has been available in the market for long. With over a decade of updates and advancement, it has become a vital tool for any big data professional.
Following are the applications of HBase:
- We use HBase when we need to write heavy applications
- When we need to perform online log analytics to create compliance reports
- When we need fast and random access to our data stored in HDFS
- When we need real-time read/write access to vast quantities of data (Big Data)
Many significant organizations such as Google and Facebook use HBase for their internal operations. Big data is prevalent everywhere, and that’s why the requirement of HBase has also increased relatively.
With the demand of Hadoop experts at an all-time high, it’d be suitable for big data professionals to learn as much as possible about this solution. HBase has many applications and that too, in a variety of sectors. That’s why learning about HBase basics and its advanced aspects is necessary.
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