Introduction to Apache HBase
Apache HBase is a Hadoop based storage NoSQL database which is one of the largest open-source and non- relational kind of a database which is modeled after the company Google’s Bigtable. It is written in the language Java. It runs on top of the Hadoop Distributed File System or popularly called as HDFS or Alluxio which is helpful in providing Bigtable like capabilities for the Hadoop system. It is helpful in providing a fault-tolerant mechanism that is used to store and keep large amounts of data especially the one which is in the sparse state. Sparse data means the kind of data which is available in small amounts or fragments and is caught within a huge collection of unimportant or empty data. For example, say finding the highest 100 records from a group of 2 billion records.
How Apache HBase Works?
Apache HBase is a kind of data model which stores the semi-structured form of data which has a different kind of data type with dynamic field size and varying column size. There are several logical components present inside the Hbase data model. Those include the row key, table name, column family, timestamp, etc. As the name suggests, the row key is specifically used to provide and identify the rows in HBase tables. The column families in the case of HBase are static whereas columns are themselves termed as dynamic in nature.
HBase is used to provide low latency based random reads as well as the writes which are present on top of HDFS. The tables in HBase are distributed in a dynamic fashion with the help of a system the moment they become too large for handling i.e. Auto Sharding starts. Another foundational unit in the region is used for horizontal scalability and is also a sorted, continuous set of rows that are stored together. Other than these it has a master node known as HBase Master and many slave nodes which are also called as the region servers. The HMaster is used to cater to the client’s write request and forward it to the corresponding region server.
What is the Use of Apache HBase?
The applications of Apache HBase includes many sectors wherever the need arises to handle large amounts of data and the use of a NoSQL database is required. Let us discuss some of the applications of Apache HBase in detail in this post.
1. Medical Field: HBase is used widely in the field of medicine as it can be used to store the genome sequences. It also runs MapReduce on top of it and therefore is also capable of storing history of people with chronic or non-chronic diseases which could be based on geographic or non-geographic region.
2. Web and Internet: Apache HBase is very helpful in bringing business to all those companies which are targeting user-specific and customer-centric data and therefore generate a lot of insights with the help of user activity and actions by storing history, cookies, and preferences and predicting later on with the huge dataset already present.
3. Sports: In the field of sports be it any sport, HBase serves the main purpose of storing the historical data of the players. By looking at the insights of this, the match forecast can be predicted along with the type of gameplay by the particular team.
4. E-Commerce: E-commerce market has been gaining much popularity with every passing day and more business means more data and therefore huge stock-keeping inventories are needed to be maintained for the high level of PSKUs and SKUs which are being shipped and for maintaining a track record of all the inventory present. Also, customer preferences and choices are also taken into consideration and huge data is easily stored in HBase.
5. Oil and Petroleum: HBase is also widely used in the petroleum, gas and oil industry as it is used to store the exploration data which can be used for analysis and also predict the probability of the prices surge along with the rigs and shores for where the oils can be found.
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6. Banks and Other Financial Institutions: Banks and other financial institutions related to the credit card industry or core financial banking also need to store customer’s crucial data which becomes high in volume and therefore HBase comes into play.
Other fields and domains: Anywhere where a huge variety and quantity of data is kept, HBase kind of a NoSQL database will be used Advantages of Apache HBase:
Advantages of Using HBase
Let us see some of the Advantages of Apache HBase which are as follows:
- It is a great tool for analytics along with the usage of Hadoop MapReduce
- It is used to support scaling along with the co-ordination of the Hadoop file system which can be done even on the commodity hardware.
- It is used to handle large volumes of data.
- License-free.
- Fault-tolerant.
- It is flexible when you talk about schema design.
- Multiple integrations such as with Hive for SQL like queries.
- Auto failover.
- Auto sharding.
- Simple client-side interface.
- Row-level of atomicity.
Why should we Use Apache HBase?
It features in-memory techniques and operations like compression and also bloom filters which are applied on a per column basis. The HBase tables serve the purpose of both the input and output for MR jobs. It can also be accessed by making use of Java API along with Avro, REST or thrift gateway based APIs. Whenever your data is column specific, going for HBase would not be a bad solution as it also has a wide lineage. It runs on top of HDFS and therefore is faster in processing for reading and write operations which could be done for high throughput as well as for low output latency.
How this Technology will help you in your Career Growth?
This technology becomes a major part of the Big data ecosystem and as you are well aware that all the technologies related to big data have a huge scope. Moreover, HBase talks about handling and storing huge data which is why a special kind of category of databases known as NoSql databases are designed which help organizations and business foster and HBase form a key integral component in it.
Learning Apache HBase is always a good decision as it is helpful in providing you many insights about the usage and handling of a high volume of data efficiently and it is also highly in demand in the market today.
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