Hive HBase Comparison: 7 Critical Differences

The amount of electronic data that exists today is enormous and is growing at an extremely high pace– more than 2.7 zettabytes (2.7*10²¹) of data exists today. This is projected to develop to multiple times over that number in under twenty years. Big Data technology is the answer and the best approach to deal with this huge volume of data.

Apache Hadoop is a popular framework that can help you seamlessly manage the huge volume of data, your organization generates. In this article, you will learn about Big Data, Hadoop, Hive, and HBase. You will also learn about the 5 critical factors for Hive HBase comparison.

Table of Contents

• Understanding Big Data
• Understanding the Importance of Big Data
  • Cost Optimization
  • Increase Customer Retention
  • Understand People’s Sentiment for the Company
  • Understanding Market Conditions
  • Optimize Marketing Efforts
• Understanding Hadoop
• Understanding the key Features of Hadoop
  • Open Source Framework
  • High Scalability
  • Cost-Effective
  • High Fault Tolerance
  • Integrations
• Understanding the Limitations of Hadoop
• Understanding Hive
• Understanding HBase
• Hive HBase Comparison
  • Hive HBase Comparison: Integration with Hadoop
  • Hive HBase Comparison: Query Performance
  • Hive HBase Comparison: Type of Operations
  • Hive HBase Comparison: Data Models
  • Hive HBase Comparison: Support for Real-time Processing
  • Hive HBase Comparison: Support for Basic Functionality
  • Hive HBase Comparison: Latency
• Conclusion

Understanding BigData

Big Data is a term that depicts a large volume of data, consisting of both Structured and Unstructured data, and is highly resourceful to a business. However, it’s not the amount of data that is of significance but how businesses manage that kind of data that is important. You can analyze Big Data for data-driven insights that lead to efficient business decisions and strategic business moves.

The utilization of Big Data by an organization to gain an advantage over its competitors. is the new normal. In many industries, existing contenders and new contenders utilize the results obtained from the Big Data analysis to compete, innovate and capture value in the industry. Even though the expression “Big Data” is moderately new, the process of gathering and storing huge amounts of data for eventual analysis is ages old. The idea acquired popularity in the mid-2000s when industry examiner Doug Laney explained the standardized meaning of Big Data as the three V-  Volume, Velocity, and Variety.

1) Volume

Businesses gather data from an assortment of sources, including deals, social media, and data from sensor or machine-to-machine data. Previously, storing the old data elsewhere would’ve been an issue but new technologies (like Hadoop) allow you to perform these tasks with ease. The name ‘Big Data” is self-explanatory and is identified with tremendous size.

2) Velocity

The term ‘Velocity’ refers to the speed of data generation. The data is being generated exponentially and processed to meet the growing demands of the industry. Big Data Velocity manages the speed at which data streams from sources like business processes, application logs, networks, social media sites, sensors, Mobile devices, etc. The progression of data is huge and constant.

3) Variety

Data can be present in a wide range of formats – from structured data like datasets, numeric data in traditional databases to unstructured data like content records, email, video, audio, stock ticker data, and monetary transactions. A few years back, spreadsheets and databases were the only sources of data used by many applications. These days data can be anything like messages, photos, videos, monitoring devices, PDFs, audio, website information, lead tracing, etc are additionally being considered in the analysis applications. This variety of unstructured data is challenging for storage, mining, and examining data.

Understanding the Importance of Big Data

It is important to realize that Big Data becomes valuable only when companies utilize their data to the maximum extent by analyzing it and making decisions based on the results obtained. Let us look at some of the benefits obtained from the analysis of your Big Data:

• Cost Optimization
• Increase Customer Retention
• Understand People’s Sentiment for the Company
• Understanding Market Conditions
• Optimize Marketing Efforts

1) Cost Optimization

Hadoop can help you reduce overhead costs through the analysis of data. You can store huge amounts of data which can save you a lot of money and also the type of data that can be stored is not just restricted to structured data.

2) Increase Customer Retention

It is fundamental for businesses to hold on to their customers no matter what(customer retention). This can be intense, however, with the approach of Big Data analytics, companies have an easy time understanding their customers and learning better approaches to keep them within their company. Carrying out better Big Data analytics assists you with improving customer loyalty. You will want to follow up on the insights to get quick updates, offering you a chance to address the issues of the consumer easily.

3) Understand People’s Sentiment for the Company

Big Data technologies can help you perform sentimental analysis that can help you understand how people outside of your company feel about your company. It can help you understand your company’s reputation and help you make changes accordingly.

4) Understanding Market Conditions

By analyzing your company’s Big Data you can improve your understanding of current economic situations. For instance, by investigating customers’ buy conduct, an organization can discover the products that are sold the most and produce products as per this pattern. By doing this you can regulate and automate your production depending on the results of the analysis.

5) Optimize Marketing Efforts

Big Data can assume a basic part in anticipating future Marketing and Sales. For instance, advertisers can acquire an understanding of which products and services are selling best all throughout the globe. Thus, this can give them insights regarding the customers to focus on straightaway, eventually bringing about better marketing and more sales.

Understanding Hadoop

The Apache Hadoop software library is a framework that allows you to perform distributed processing of large datasets across computer clusters by utilizing basic programming models. It is built to scale up from a single server to a huge number of machines, each offering local computation and storage. This library is built to identify and deal with any errors or failures at the application layer rather than rely on hardware to deliver high availability. As a result, delivering a highly-available service on top of a group of computers might be prone to errors.

Understanding the Key Features of Hadoop

Hadoop is very popular due to its attractive features. Let’s discuss some key features of them:

• Open Source Framework
• High Scalability
• Cost-Effective
• High Fault Tolerance
• Integrations

1) Open Source Framework

Hadoop is Open-source, which implies it allows you to work on it for free. Since it is an Open-source project, the source code is available online for any person to understand it or make a few adjustments according to their personal or industry requirement.

2) High Scalability

Hadoop is powerful in terms of scalability. Huge amounts of data are processed parallelly in a cluster and the number of nodes can be altered as per your requirements, unlike RDBMS systems where the data cannot be easily scaled up to huge amounts of data.

3) Cost-Effective

Hadoop, unlike traditional RDBMS, does not require high-end processors and expensive hardware to work with Big Data. Using Hadoop has 2 main benefits. One is that it is free of cost since it’s Open-source. The other benefit is that it uses commodity hardware which is also inexpensive.

4) High Fault Tolerance

Though Hadoop uses inexpensive hardware it ensures fault tolerance by data replication across various DataNodes in a Hadoop cluster. This ensures the availability of data. Even if any of your systems crash or experience failure. Hadoop makes 3 copies of each file block by default and stores it across different nodes. This replication factor is manually configurable and can be changed as per your requirements.

5) Integrations

The architecture of Hadoop ensures easy integration with other systems. Integration plays an important role because we can process the data efficiently just with Hadoop. Data has to be integrated with other systems to achieve interoperability and flexibility. Hadoop Can be integrated with BI tools, NoSQL, and ETL tools like:

• Hive
• HBase
• Flume 
• Cassandra
• Talend
• Tableau
• R, etc

Understanding the Limitations of Hadoop

• Apache Hadoop is built for performing batch processing, which means that it takes a large amount of data as input, processes it, and produces the output. Depending on the size of the data that it processes and the computational power of the system, producing the output can be delayed significantly. As a result, it is not suitable for Real-time data processing.
• Working with the MapReduce framework in Hadoop is comparatively slower since it built is for supporting different formats, structures, and huge volumes of data. In MapReduce, Map takes a set of data and breaks every individual element down to key-value pairs. Reduce receives the output from the Map as input and processes it further. Hence, MapReduce requires a lot of time to perform all the operations thereby increasing latency.
• In Hadoop, the MapReduce framework is not easy to work with because it has no interactive mode and you have to write code from scratch all by yourself for every operation.
• Though Hadoop supports Kerberos authentication it is hard to manage because Hadoop is missing encryption at some storage and network levels. This becomes a major concern especially since it has a huge volume of data.
• Hadoop does not have any type of in-built abstraction. Also, iteration operations cannot be efficiently performed on Hadoop.

Understanding Hive

Apache Hive acts as a Data Warehouse and ETL tool. Hive is built on top of Hadoop and operates similar to an SQL-like interface between the user and HDFS( Hadoop Distributed File System). It is a software application that facilitates data querying and analysis. It also allows the user to read, write and handle wide datasets stored in distributed storage which can be queried by Structure Query Language (SQL) syntax. It is not compatible with Online Transactional Processing (OLTP) workloads.

Data warehousing tasks like data encapsulation, Ad-hoc Queries, and analysis of huge datasets are the most commonly performed operations on Hive. Hive has enhanced scalability, extensibility, performance, and great fault tolerance. The only drawback here is that Hive cannot provide real-time support.

Understanding HBase

HBase is an Open Source, Column-oriented, Distributed, DBMS developed by Apache software foundations that also runs on top of HDFS( Hadoop Distributed File System). This works well when you have sparse datasets that are commonly found in many Big Data use cases. Initially, it was named after Google Big Table but later it was changed to HBase and is primarily written in Java. It can store a massive amount of data (Terabytes or Petabytes). It stores a large amount of data in the form of tables, built for low-latency operations like read and write.

Hive HBase Comparison

Hive HBase comparison can help you understand both technologies better. Hive Hbase comparison can be done using 7 critical factors:

• Hive HBase Comparison: Integration with Hadoop
• Hive HBase Comparison: Query Performance
• Hive HBase Comparison: Type of Operations
• Hive HBase Comparison: Data Models
• Hive HBase Comparison: Support for Real-time Processing
• Hive HBase Comparison: Support for Basic Functionality
• Hive HBase Comparison: Latency

Hive HBase Comparison: Integration with Hadoop

MapReduce jobs are usually hard to implement using Hadoop only so Hive simplifies the task. Hive uses queries that are mostly similar to SQL queries and makes MapReduce jobs easy to implement/ work with.

 HBase is a data storage facility, particularly for unstructured data. Hadoop is a combination of the distributed file system and computational processing( Map Reduce). Like any File System, Hadoop also does not support random read and write access. This is where HBase comes into the picture. It stores data as key-value pairs making random access operations fast and efficient.

Hive HBase Comparison: Query Performance

Queries written in Hive are used for analytical purposes. Hive uses HiveQL for writing queries ( very similar to SQL). HiveQL queries are converted into Hadoops’ MapReduce jobs (Writing codes for MapReduce jobs on Hadoop directly is challenging even for experienced developers). These queries execute the MapReduce jobs(analysis) quite efficiently even though they take some time to execute.

HBase has no specific query language to perform CRUD( Create, Read, Update and Delete) operations. HBase has a Ruby-based shell where you can manipulate your data using Get, Put and Scan methods.

So for query performance, we can say that Hbase can be used for consistent data reads which facilitates faster analysis using Hive. 

Hive HBase Comparison: Type of Operations

Hive is primarily used for analysis and HBase is used for transactional operations.

Hive HBase Comparison: Data Models

Hive does not differ much from traditional Relational database tables. So Hive has tables with rows and columns just like RDBMS tables. The only difference is Hive tables are broken down into multiple row partitions called buckets for better management of data.

HBase stores data in the form of tables. It is a column-oriented database. HBase stores the column data as key-value pairs. Tables are identified with Row ID. Each table has multiple column families and each column family has multiple column data (key-value pairs).

Hive HBase Comparison: Support for Real-time Processing

Hive cannot be used for Real-time processing because it is impossible to get results of analysis immediately. But HBase can be used for Real-time processing because transactional operations take less time ( since HBase stores data in the form of key-value pairs).

Hive HBase Comparison: Support for Basic Functionality

The older versions of Hive had support only for analytical operations. So, it was a huge drawback initially when users weren’t able to update, insert or delete data with Hive. The newer versions from 0.14.0 provided limited transactional functionality. But it is still advisable to primarily use Hive for the analysis of Big Data.

As specified above, HBase only acts as a data storage facility on top of Hadoop.

Hive HBase Comparison: Latency

Hive takes a huge amount of data stored over a period of time and processes. To simply state, Hive performs batch processing operations that take a while to process and give a result. Whereas, Hbase is mostly used for fetching or writing data which is relatively faster than Hive.

We can summarize the Hive HBase comparison as follows:

Comparison Factor	Hive	HBase
Integration with Hadoop	Hive is a SQL-like query engine that runs MapReduce jobs on Hadoop.	HBase is a NoSQL key/value database on Hadoop.
Type of Operations	Hive is mainly used for batch processing functions and not OLTP operations.	HBase is largely used for its fast transactional processing capabilities on a large volume of data.
Support for Real-time Processing	Since the operations performed by Hive are batch processing that usually takes time to give output, Real-time processing cannot be achieved.	Transactions done by Hbase are fast and support Real-time processing.
Support for Basic Functionality	Mostly used for analyzing Big Data	Mostly used for querying Big Data.
Latency	Has high latency due to batch processing.	Hbase has low latency.

Conclusion

In this article, you learned about Big Data, Hadoop, Hive, and HBase and factors that can be considered for Hive HBase comparison. Both are incredible tools that run on top of Hadoop. Although some of the functions performed by Hive and HBase are similarly associated with Hadoop, they vary vastly. Hive is most commonly used for analytical queries while HBase is used for Real-time querying/processing. It is important to keep in mind that the functionalities that Hive lacks are facilitated by Hbase and vice versa. So ultimately, it all comes down to the use case since both Hive and HBase complement each other. Google, Twitter, Facebook, Adobe, and HubSpot use both Hive and HBase for their Hadoop stack. 

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