Why Is Hive Important In The Field Of Big Data?

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In this article, we will discuss Hive in Hadoop. For this, a basic understanding of Big Data is required. We’ll look into the definition of data. The data that a computer uses to perform operations can be stored and transmitted as electrical signals, and recorded on magnetic, optical, or mechanical media.

Big Data
What is Big Data? Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. However, there are now new tools and technologies that can help us manage and make use of big data. Data sets with many fields offer greater statistical power, while data sets with higher complexity may lead to a higher false discovery rate.

Types of Big Data
Big data is classified into three types. They are,
i) Structured
ii) Unstructured
iii) Semi-Structured

i) Structured
Working with data that is structured in a fixed format (where the format is known in advance) is termed 'structured' data. Over time, computer science professionals have developed successful techniques for working with this ...
... type of data and extracting value from it.

ii) Unstructured
Unstructured data is any data that doesn't have a known form or structure. In addition to being huge in size, unstructured data poses multiple challenges in terms of processing it to extract value from it. Unstructured data is a type of data that doesn't have a predefined structure. This means that it can be made up of different types of data, like text files, images, and videos.

iii) Semi-Structured
Semi-structured data can be a mix of both structured and unstructured data. It's usually not as rigidly defined as structured data, like in a relational database management system.

What is Hive?
Hive is a data storage system that was originally developed by Facebook with the purpose of analyzing organized data. Apache now owns Hive, and it works under an open-source data platform called Hadoop. Apache Hive was released in 2010 (October). Data is stored in the Apache Hadoop Distributed File System (HDFS), and Apache Hive helps to process and analyze this data, producing patterns and trends. Apache Hive is extremely helpful for organizations dealing with big data and its ever-changing growth.

Importance of Hive
Hive has been a big innovation in the world of big data, eventually leading to large-scale data analysis. Big organizations need lots of data to record the information they collect over time. Organizations gather data and use software applications to analyze it in order to produce data-driven analysis. This data can be used for reading, writing, and managing information with Apache Hive. Data storage has been a trending topic ever since data analytics came into being. Small organizations could manage medium-sized data and analyze it with traditional data analytics tools, but big data was too much for those applications. This created a need for more advanced software.

Data Flow in Hive
i) The data analyst executes a query using the User Interface (UI).
ii) The driver interacts with the query compiler in order to retrieve the plan, which contains information on the query execution process and metadata. The driver also parses the query to check syntax and requirements.
iii) The compiler creates the job plan (metadata) to be executed and communicates with the metastore in order to retrieve a metadata request.
iv) The metastore sends metadata information back to the compiler, which then relays the proposed query execution plan to the driver. The driver sends the execution plans to the execution engine.
v) The execution engine is responsible for processing queries by acting as a bridge between Hive and Hadoop. The job process executes in MapReduce. The execution engine sends the job to the JobTracker in the Name node and assigns it to the TaskTracker in the Data node. While this is happening, the execution engine executes metadata operations with the metastore.
The results are retrieved from the data nodes once the job is completed.
vi) The results from your query are sent to the execution engine, which then sends the results back to the driver and the front end (UI).

Modes of Hive
Hadoop can operate in two different modes depending on the size of the data nodes: Local mode and Map-reduce mode.

Local mode is best when:
- Hadoop is installed in pseudo mode, with only one data node
- The data size is smaller and limited to a single local machine
- Users expect faster processing since the local machine has smaller datasets

Map-reduce mode is best when:
- Hadoop is installed in a distributed mode, with multiple data nodes
- The data size is larger and needs to be distributed across multiple machines
- Users expect more reliable processing since multiple machines are involved

When you have multiple data nodes and your data is distributed across them, Map Reduce mode is the way to go. If you're dealing with massive data sets, this is the mode for you.

Benefits of Hive Big Data
A hive is a great option for data optimization and analysis, with plenty of advantages that outweigh its few drawbacks.
Some of the advantages are:
i) Easy-to-Use
Hive in Big Data is an easy-to-use software application that lets you analyze large-scale data through the batch processing technique. This program is efficient and easy to use, thanks to its familiar software interface that uses HiveQL. HiveQL is very similar to SQL, which is the standard language for interacting with databases.
ii) Faster Experience
The technique of batch processing refers to analyzing data in bits and pieces, which are then combined. The data that is analyzed is sent to Apache Hadoop. The schemas or derived stereotypes stay with Apache Hive.
iii) Fault-Tolerant Software
Most software used to handle Big Data today doesn't have fault tolerance built in. However, Apache Hive and HDFS work together in a fault-tolerant way. As soon as data is analyzed in Hive, it's replicated to other machines. This prevents the loss of data or schemas if a machine fails.
iv) Productive Software
Apache Hive is a great software for data analysis because it enables users to read and write data in an organized way. Oozie defines specific schemas related to data analysis and stores them in the Hadoop Distributed File System (HDFS) for future use. This makes it easy to access and reuse this information whenever you need it.

Future of Hive Big Data
As more and more cloud-based software options become available, Apache Hive is slowly losing its value. Google Bigquery is more efficient in terms of instant data tracking, so Hive is taking a back seat in the market. Although Hive has been a big player in the big data game for some time, predictions for its future don't seem too positive. Many experts are predicting that Hive will eventually be replaced by more modern data processing technologies. However, it is still one of the leading software options available today. Hive is a slightly slower process than others when it comes to contemporary big data distribution.

Conclusion
In this article, we have discussed what Big Data is. We have also discussed Hive in Big Data, and the importance of the same. We have looked at the advantages and disadvantages. Big Data is used in Full-Stack development. Full-Stack developers are the most sought-after in the IT industry. How can a candidate be equipped with the knowledge of Full-Stack? Skillslash also offers Data Science Course In Hyderabad and a Full Stack Developer Course In Bangalore. Apart from these, they offer a guaranteed job referral program. Get in touch with the student support team to know more. They provide 1:1 mentorship and also guaranteed job referrals.

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