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Introduction
HBase is a column-oriented database that provides a dynamic database schema. Mainly it operates on top of the HDFS and also carries MapReduce jobs. Besides, for data processing, HBase also supports other high-level languages. Initially, it was Google Big Table; afterward, it was named HBase and is primarily written in Java.
Strictly consistent reads and writes. It can be used for high-speed requirements since it offers consistent reads and writes.
Automatic and configurable sharding of tables HBase offers automatic and manual splitting of regions into smaller subregions as soon as it reaches a threshold size to reduce I/O time and overhead.
Client API Easy to use Java API for client access.
Hadoop/HDFS integration Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
Linear and modular scalability. In both linear and modular forms, HBase supports scalability. In addition, we can say it is linearly scalable.
Thrift gateway and REST-ful Web services Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options.
Real-time Processing Block cache and Bloom Filters for real-time queries.
High Throughput Due to the high security and easy management characteristics of HBase, it offers unprecedented high write throughput.
Schema-less Due to the high security and easy management characteristics of HBase, it offers unprecedented high write throughput.
Importance of NoSQL Databases in Hadoop
Hadoop plays an important role in solving typical business problems in big data analytics by managing large data sets and giving the best solutions in this domain.
Data processing
Data validation
Data storing
It enables certain types of NoSQL distributed databases (such as HBase), allowing for data to be spread across thousands of servers, leading to a slight reduction in performance. Also, fetching results by applying queries on substantial data sets stored in Hadoop storage is challenging. NoSQL storage technologies give the best solution for faster querying on massive datasets.
Why HBase over other NoSQL models?
A table for a famous web application may have billions of rows if we want to locate a particular row from such a vast amount of data.
HBase is the perfect choice because its query fetch time is less, and therefore it's used by most online analytics applications.
Traditional relational data models don't meet the performance requirements of massive databases. Apache HBase can handle these performance and processing limitations.
HBase stores data in the map form as key/value pairs in the column method. Where all the columns are grouped as Column families.
HBase gives a flexible data model and low latency access to small amounts of data stored in large data sets.
HBase on top of Hadoop will enhance distributed cluster set up throughput and performance. It leads to provides faster random reads and writes operations.
Hive
It is a declarative SQL-based tool used to analyze structured data. It is built on top of Hadoop. It provides the functionality of reading, writing, and managing large datasets residing in distributed storage. It runs SQL-like queries called HQL (Hive query language), which gets internally converted to MapReduce jobs.
Hive is mainly used in performing operations like
Data encapsulation
Ad-hoc queries
Analysis of huge datasets
HBase Vs. Hive
Hive
HBase
Hive is a query engine.
HBase is a data storage mainly for storing unstructured data.
Hive(Apache) is mainly used for batch processing, i.e., OLAP(online analytical processing).
HBase is extensively used for translational processing.
Operations in Hive are transformed into MapReduce jobs.
Operations in HBase are run in real-time on the database.
Hive is for analytic queries.
HBase is for real-time querying.
RDBMS
RDBMS stands for Relational Database Management System. It moves data into a database, stores the data, retrieves it to be manipulated by applications, and keeps the data into a collection of tables related to standard fields, i.e., database table columns.
HBase Vs. RDBMS
HBase
RDBMS
Column-oriented
Row-oriented
Flexible schema, add columns on the fly.
Fixed schema
Joins using MR- not optimized
Optimized for joins.
Tight integration with MR
Loosely integration process.
Horizontal scalability, i.e., just add hardware
Hard to shard and scale
Good for semi-structured data as well as unstructured data.
Is HBase open source? Yes, HBase(Apache) Apache is an open-source, NoSQL, distributed big data store. It allows strictly, random consistent, real-time access to petabytes of data. It is very effective for handling large, sparse datasets.
Why is HBase NoSQL? Hbase is a NoSQL database because it runs on top of Hadoop and provides various benefits, including flexible data models, horizontal scaling, speedy queries, and ease of use for developers like any NoSQL database. It is suited for data represented in billions of rows that cannot be accommodated on the traditional RDBMS database.
Which of the features does HBase not support? HBase does not support structured query language like SQL becauseit is not a relational data store. Here a master node operates the cluster and region servers to store portions of the tables and performs the work on the data.
Key Takeaways
In this article, we learned about the critical features of HBase and how it provides unique features and will solve typical industrial use cases. We compared HBase with different NoSQL databases, i.e., Hive and RDBMS. We also the advantages of HBase over all other models.
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