5 Advantages and Disadvantages of Apache Hive | Drawbacks & Benefits of Apache Hive

 

Structured data from the Hadoop Distributed File System (HDFS) can be queried and analyzed using Apache Hive, a data storage system. This program is capable of efficiently querying even Petabytes of data.

Hive is a Hadoop-based tool that was first launched in 2010. It was created by Facebook at first. To save the data in one location, it is acquired from several sources. We call this procedure "data warehousing."

Hive is written in HQL, which is also referred to as Hive-QL. There are syntactic similarities between SQL and HQL. Therefore, if you already have some familiarity with SQL, using Hive will be easier for you. Hive was primarily created to address the difficulties encountered when writing Java code. Consequently, structured data querying is a simple operation.

Although Hive is favored by developers for a variety of reasons, it does have some disadvantages. It is important for developers to consider all of Hive's advantages and disadvantages.

I'll be talking about the 5 Advantages and Disadvantages of Apache Hive | Drawbacks & Benefits of Apache Hive in this post. You'll learn about the pros and cons of utilizing Apache Hive from this post.


Now let's get started,

Advantages of Apache Hive

1. Cost

If making money is the main priority for your company, Apache Hive is a reasonably priced solution. It offers large data analysis choices at substantially lower costs.


But it's also important to note that Hive makes use of a few cutting-edge technologies and tools for software development. 

 

When these are used, an organization's total profit may decrease. In this situation, companies ought to think about going with less expensive solutions.


2. Reliability

Compared to competing software solutions, Apache Hive offers significantly greater reliability for big data processing. 

 

Hive is compatible with the HDFS file system, as is well known. Together, they can accomplish the shared objective of producing replicas. 

 

Every time the huge data is analyzed, it gets reproduced. There is no loss of data, even in the event of a machine failure. 

 

 

3. Speed

Apache Hive's batch processing capability allows it to perform data analysis quickly. In batch processing, each piece of data is separated and examined independently. 

 

They are then joined together to enable them to arrive at Apache Hadoop's destination. It is a more sophisticated tool than other conventional ones.



Additionally, Apache Hive's batch processing functionality enables it to handle massive volumes of data at once.


4. Efficiency

Hive is a user-friendly program suitable for both novice and experienced programmers. Hive is simple to use for anyone with some familiarity with SQL. 

 

Additionally, Hive makes it possible to divide the work for tasks demanding complicated code. in order to prevent any developer from being given more duties. 

 

The developers must complete the jobs that have been specifically delegated to them using the filtering approach.


5. Customer Support

One appealing feature of Hive is its customer service. They have people on their team that are prepared to answer questions from clients. 

 

In addition, they make sure Hive is updated with the enhancements that are required to enhance the user experience.

Disadvantages of Apache Hive 

1. Mobile Functionality

An further restriction on Apache Hive is its mobile capabilities. Hive is not as responsive on mobile devices as it is on desktops. 

 

This makes navigating challenging, which is one of the reasons why customers prioritize the desktop version more.


2. User Friendliness

It might be difficult to learn Hive, especially for newcomers. It will take them some time to get used to this application. 

 

The software is challenging due to features like customization and configuration. 

 

 

3. Notification

Many people find the notifications that appear in the UI's corner to be annoying. They are never set up to receive emails. 

 

Alternatively, there isn't a way to designate them as read. Users need to prepare for these warnings unless they are using the concentrate mode.


4. Task Creation

Users may need to build dependent tasks for some projects because they may repeat. 

 

Despite having an automated workflow, it is still unable to generate dependent tasks. Hive requires them to construct each recurring job manually.


5. Unstructured Data Support

Hive stores data in tabular form, which is always processed using structured data. Hive is unable to support SQL queries used to write unstructured data. 

 

Moreover, Hive is not advised for tasks like online analytical processing (OLAP) and online transaction processing (OLTP).