Big data involves processing massive amounts of diverse information and delivering insights rapidly—often summed up by the four V's: volume, variety, velocity, and veracity. Data scientists and analysts need dedicated tools to help turn this raw information into actionable content, a potentially overwhelming task. Fortunately, some tools exist.
Hadoop is one of the most popular software frameworks designed to process and store Big Data information. Hive, in turn, is a tool designed to use with Hadoop. This article details the role of the Hive in big data, as well as Hive architecture and optimization techniques.
Let us now begin by understanding what is Hive in Hadoop.
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No one can better explain what Hive in Hadoop is than the creators of Hive themselves: "The Apache Hive™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. The structure can be projected onto data already in storage."
In other words, Hive is an open-source system that processes structured data in Hadoop, residing on top of the latter for summarizing Big Data, as well as facilitating analysis and queries.
Now that we have looked into what is Hive in Hadoop, let us take a look at the features and characteristics.
The following are Hive's chief characteristics to keep in mind when using it for data processing:
Since we have gone on at length about what Hive is, we should also touch on what Hive is not:
As we have looked into what is Hive, let us learn about the Hive modes.
Depending on the size of Hadoop data nodes, Hive can operate in two different modes:
User Local mode when:
Use Map Reduce mode when:
MapReduce is Hive's default mode.
In order to continue our understanding of what Hive is, let us next look at the difference between Pig and Hive.
Both Hive and Pig are sub-projects, or tools used to manage data in Hadoop. While Hive is a platform that used to create SQL-type scripts for MapReduce functions, Pig is a procedural language platform that accomplishes the same thing. Here's how their differences break down:
So, if you're a data analyst accustomed to working with SQL and want to perform analytical queries of historical data, then Hive is your best bet. But if you're a programmer and are very familiar with scripting languages and you don't want to be bothered by creating the schema, then use Pig.
In order to strengthen our understanding of what is Hive, let us next look at the difference between Hive and Hbase.
We've spotlighted the differences between Hive and Pig. Now, it's time for a brief comparison between Hive and Hbase.
Data analysts who want to optimize their Hive queries and make them run faster in their clusters should consider the following hacks:
There is a lot to learn in the world of big data and this article on what is Hive has covered some of it. Simplilearn has many excellent resources to expand your knowledge in these fields. For instance, this article often referenced Hadoop, which may prompt you to ask, "But what is Hadoop?" You can also learn more through the Hadoop tutorial and Hive tutorial. If you want a more in-depth look at Hadoop, check out this article on Hadoop architecture.
Finally, if you're applying for a position working with Hive, you can be better prepared by brushing up on these Hive interview questions.
After going through this article on "what is Hive", you can check out this video to extend your learning on Hive -
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