Apache Hive :: Apache Hive tutorial (What is Hive)

Apache Hive  :– Friends in this article I will tell you about Apache Hive   :: Apache Hive tutorial (What is Hive). So Friends please read this article till end carefully. Apache Hive   –

Apache Hive
Apache Hive  

Apache Hive and Apache HBase are two extraordinary Hadoop-based Big Data applied sciences that serve extraordinary functions in nearly all the use instances that can be virtually considered. Take an instance of a Social media state of affairs of Facebook – when you log in you would possibly see more than one matters on your Facebook touchdown web page like your friend’s list, a information feed, advert suggestions, buddy suggestions, etc.

With over two billion month-to-month customers getting access to Facebook on a day by day basis, how would you suppose that Facebook is in a position to load all such cluttered in a presentable manner – the reply is particularly simple, Apache Hadoop in conjunction with many different applied sciences that we are going to talk about nowadays in detail, that is, Apache Hadoop with Apache Hive vs Apache HBase.

What is Apache Hive?

Hive, centered through Facebook and later Apache, is a information storage device created for the cause of examining structured data. Operating beneath an open-source records platform known as Hadoop, Apache Hive is a software program utility launched in 2010 (October).
Introduced to facilitate an error-tolerant evaluation of large records on a everyday basis, Hive has been used in records evaluation and has been famous in the vicinity for over a decade now.
Although it has many rivals like Impala, Apache Hive differs from different packages in that it has its very own tolerant nature of the error in the records evaluation and translation process.

Why do we want it?

Apache Hive
Apache Hive 

Hive in huge statistics innovation is a milestone that ultimately led to statistics evaluation on a giant scale. Large corporations want massive information to document records gathered over time. To generate data-driven analysis, agencies acquire statistics and use such software program purposes to analyze their data. This data, contained in Apache Hive, can be used to read, write, and control saved records in an prepared way. Since the introduction of statistics analysis, records storage has been a trending topic.
Even if small corporations have been capable to manipulate medium-sized facts and analyze it with ordinary information evaluation tools, massive facts ought to now not be managed with such purposes and therefore, there was once a exquisite want for superior software.
As information series turns into a day by day venture and corporations extend in all aspects, information series is turning into extra and extra widespread. In addition, the information started to be managed with petabytes which describes the storage of giant data.
For this, agencies wanted large tools and that is probable why the launch of software program like Apache Hive used to be needed. Therefore, Apache Hive used to be launched for the reason of inspecting giant records and producing data-driven analogies.
Here are two examples of Airbnb and the guardian classes that can assist you apprehend the use of Hive in Big Data.

When to use Apache Hive?

Traditional RDBMS specialists would love to use Apache Hive, as they can actually map HDFS archives to Hive tables and question the data. Even the HBase tables can be mapped and Hive can be used to function on that data.

Apache Hive have to be used for facts warehousing requirements and when the programmers do no longer prefer to write complicated MapReduce code. However, now not all troubles can be solved the use of apache hive. For large records purposes that require complicated and fine-grained processing, Hadoop MapReduce is the first-class choice.

Features of Apache Hive

There are so many aspects of Apache Hive. Let’s talk about them one by means of one-

  • Hive offers facts summarization, query, and evaluation in a lot less complicated manner.
  • Hive helps exterior tables which make it feasible to manner statistics besides really storing in HDFS.
  • Apache Hive suits the low-level interface requirement of Hadoop perfectly.
  • It additionally helps partitioning of records at the stage of tables to enhance performance.
  • Hive has a rule based totally optimizer for optimizing logical plans.
  • It is scalable, familiar, and extensible.
  • Using HiveQL doesn’t require any know-how of programming language, Knowledge of fundamental SQL question is enough.
  • We can without difficulty procedure structured information in Hadoop the usage of Hive.
  • Querying in Hive is very easy as it is comparable to SQL.
  • We can additionally run Ad-hoc queries for the statistics evaluation the use of Hive.

Limitation of Apache Hive

Hive has the following limitations-

  • Apache does no longer provide real-time queries and row degree updates.
  • Hive additionally offers perfect latency for interactive records browsing.
  • It is no longer appropriate for on-line transaction processing.
  • Latency for Apache Hive queries is typically very high.

So, this was once all in Apache Hive Tutorial. Hope you like our explanation.

Also Read –  Apache Hive in 2023 : Tutorial, Uses, Benifits

Conclusion to Apache Hive

In summary, Apache Hive used to be launched in October 2010 with the purpose of assisting to analyze the massive records on hand to all organizations. Fast and standard, environment friendly and reliable, Hive has come to be one of the greatest facts software program equipment of its time.
While the future of software program may also now not appear so promising, it has definitely come to be a superstar in persevering with to analyze huge records up to a excessive degree over the previous decade. With extra rivals coming along, the software program stays special in phrases of its most famous features.
Big Data is nowhere to be found, so the greater superior variations of Apache Hive are what the science subject wishes these days to tackle a massive quantity of petabytes of statistics generated per second.








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