Mining Big Data with Hive

Big Data mining

Big data mining refers to the collection of data mining or extraction processes conducted on enormous volumes of data or big data. Big data mining is done primarily to extract and recover desired information or patterns from massive amounts of data.

Now let’s learn about Hive in detail.

Hive

Hive is a batch-oriented data-warehousing layer based on Hadoop fundamental components (HDFS and MapReduce). It offers SQL-savvy users a simple SQL-lite implementation called HiveQL without sacrificing access via mappers and reducers. Hive gives you the best of both worlds:

MapReduce provides SQL-like access to structured data and advanced big data analysis. Unlike most data warehouses, Hive is not intended to provide speedy replies to queries. Depending on the intricacy of the query, it could take several minutes or even hours. As a result, Hive is best suited for data mining and deeper analytics that do not require real-time activity. It is incredibly extendable, scalable,  and resilient because it is built on the Hadoop base, which the conventional data warehouse is not.

Source: sqlauthority.com

Mining Big Data with Hive

Hive organises data using three mechanisms:

Tables: Hive tables are similar to RDBMS tables because they contain rows and columns. Tables are mapped to file system directories since Hive is layered on the Hadoop HDFS. Hive also supports tables saved in different native file systems.

Partitioning: A Hive table can have one or more partitions. These partitions indicate data distribution across the table and are mapped to subdirectories in the underlying file system.

Buckets: Data can be separated into buckets. Buckets are saved as files in the underlying file system's partition directory. The hash of a table column determines the buckets. In the prior example, you might have a bucket called Focus that contains all of the characteristics of a Ford Focus vehicle.

Big data management

The efficient handling, categorisation, and utilisation of huge volumes of structured and unstructured data belonging to an organisation are called big data management.

Big data management enables a firm to better understand its customers, develop new products, and make critical financial decisions by analysing vast amounts of corporate data.

Big data management entails several processes, including the following:

• A centralised interface/dashboard is used to monitor and ensure the availability of all big data resources.
• Maintaining the database for better results.
• Big data analytics, reporting, and other comparable solutions must be implemented and monitored.
• Ensuring that data life-cycle processes are designed and implemented efficiently to produce the highest quality results.
• Controlling access and ensuring the security of massive data repositories.
• Using data virtualisation approaches minimises data volume and improves big data operations with faster access and less complexity.
• Using data virtualisation techniques allows a single data set to be used by several applications/users simultaneously.
• Assuring that data is gathered and saved as desired from all resources.

Read about Batch Operating System here.

Let’s move on to Frequently asked questions.

Frequently Asked Questions

What is Big Data?

Big Data is data that is massive in volume and size. Big Data is a term used to describe a massive collection of data rising exponentially over time.

What are the characteristics of big data?

Big data has three characteristics: diversity, velocity, and volume. Diversity refers to the sources from which the data is received, and velocity refers to the rate of processing the data. Volume is referred to as the amount of data generated.

What is a Hive?

Hive is a batch-oriented data-warehousing layer based on Hadoop's fundamental components (HDFS and MapReduce). It offers SQL-savvy users a simple SQL-lite implementation called HiveQL without sacrificing access via mappers and reducers. 

Give some real-life applications of big data?

Industries have seen tremendous development due to the rise of Big Data.

Example: Banking, Manufacture, Technology, Consumers.

What are the mechanisms used by Hive?

Hive uses three mechanisms to organize the data: table, partitioning, and buckets.

Conclusion

This article extensively discussed Big Data, Hive, and mining big data with the Hive. We learned the concept of big data with its examples. We learned about Hive and its uses and mechanism to organize big data.

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