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

Apache Hive is a data storage system that is used for data
querying and analysis of structured data from the Hadoop Distributed File System (HDFS). Even Petabytes of data can be queried from this tool efficiently. 

Being released in the year 2010, Hive is a tool operating under Hadoop. Originally, it was developed by Facebook. The data from different sources is gathered so that it can be stored at one place. This process is known as Data Warehousing. 

The language used in Hive is HQL (also known as Hive-QL). HQL and SQL both of them share similarities in the form of syntax. Therefore, using Hive is easier if you already have a fair knowledge on SQL. Hive was mainly developed to overcome the challenges faced while coding in Java. Thus, querying of structured data is a hassle free process. 

Al though, there are many reasons why Hive is preferred among developers, it does come with few drawbacks. As a developer it is worth keeping in mind all the pros and cons of Hive.

In this article, I will be discussing about 5 Advantages and Disadvantages of Apache Hive | Drawbacks & Benefits of Apache Hive. From this post, you will know the pros and cons of using Apache Hive.

Let's get started,

Advantages of Apache Hive

1. Cost

Apache Hive is a cost effective option if your organization's prime focus is on profit. It provides much cheaper options for big data analysis. 

However, we must also mention that Hive uses some advanced software development tools and technologies. From the usage of these, the overall profit of organizations can reduce. In this case, they need to consider using other cheaper options.

2. Speed

Apache Hive is able to conduct data analysis in a fast manner due to its nature of batch processing. In batch processing, the data is divided as bits and analyzed separately. Then afterwards they are combined together so that they can reach the destination of Apache Hadoop. It is more advanced than other traditional tools.

 

Moreover, the feature of batch processing allows Apache Hive to handle enormous amount of data simultaneously.

3. Reliability

For bigdata analysis the reliability provided by Apache Hive is far superior than other software solutions. As we all know Hive works together with HDFS file system. When functioned together, they can achieve a common goal of replica creation. The bigdata is replicated each time when it is analyzed. Even in the case of machine malfunctioning, there is no data loss.

4. Efficiency

Hive is easy to use application for both beginners and experts in programming. Anyone who is familiar with SQL can work with Hive easily. And for projects involving complex coding, Hive allows to divide the work. So that there is no any developer is assigned with more tasks. The developers need to carry out the tasks specifically assigned to them through the method such as filtering.

5. Customer Support

Hive includes an attractive customer support service. Their team consists of members who is ready to respond customer queries. Besides that, they ensure Hive is modified with necessary improvements as to improve customer experience.

Disadvantages of Apache Hive

1. User Friendliness

Learning Hive is a challenging process especially for the beginners. They need to spend some time adopting to this application. The functions such as configuration and personalization makes it a difficult software.

2. Mobile Functionality

Mobile functionality is another limitation for Apache Hive. Compared to a desktop version, Hive is less responsive for mobiles. This makes it difficult to navigate which is one of the reasons for the users to give more priority to the desktop version.

3. Task Creation

Some projects can recur requiring the users to create dependent tasks. Even though it comes automated workflow still it cannot create dependent tasks. For each tasks that recur, Hive needs them to create manually.

4. Notifications

The notifications that pops up on the corner of UI annoys many users. They are never configured to reach inbox. Or neither there is any option to mark them as read. Unless you are using the focus mode, the users must be ready to deal with these notifications.

5. Unstructured Data Support

Hive uses table form to store data which always process structured data. Even if the unstructured data is written using SQL queries, Hive cannot support them. And also, Hive is not recommended for operations such as Online transaction Processing (OLTP)  or Online Analytical Processing (OLAP).