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Basically, for querying and analyzing large datasets stored in Hadoop files we use Apache Hive. However, there are many more concepts of Hive, that all we will discuss in this Apache Hive Tutorial, you can learn about what is Apache Hive. So, in this Apache Hive Tutorial, we will learn Hive history. Further, we will see why the Hive is used reasons to learn Hive. Also, we will cover the Hive architecture or components to understand well. Afterwards, we will also cover its limitations, how does Hive work, Hive vs SparkSQL, and Pig vs Hive vs Hadoop MapReduce.

Apache Hive is an open source data warehouse system built on top of Hadoop Haused. Especially, we use it for querying and analyzing large datasets stored in Hadoop files. Moreover, by using Hive we can process structured and semi-structured data in Hadoop.
In other words, it is a data warehouse infrastructure which facilitates querying and managing large datasets which reside in the distributed storage system. Basically, it offers a way to query the data using a SQL-like query language called HiveQL(Hive Query Language).

As we know it is mainly used for data querying, analysis, and summarization. Moreover, it helps to improve the developer productivity. However, that comes at the cost of increasing latency and decreasing efficiency. In other words, Hive is a variant of SQL and a very good one indeed. Although, when compared to SQL systems implemented in databases, Hive stands tall. Hive has many User Defined Functions that makes it easy to contribute to the UDFs. Also, we can connect Hive queries to various Hadoop packages. Such as RHive, RHipe, and even Apache Mahout. However, when working for complex analytical processing and data formats that are challenging, it greatly helps the developer community.

To be more specific, Data warehouse means a system we use for reporting and data analysis. Basically, it refers to inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information and suggesting conclusions. Moreover, in the different business, science, and social science domains data analysis has multiple aspects and approaches, encompassing diverse techniques under a variety of names.

Features of Hive
In this section of Hive Tutorial, we study Apache Hive features. So, lets discuss all-The best feature is it offers data summarization, query, and analysis in much easier manner.However, to process data without actually storing in HDFS, Hive supports external tables.
Moreover, it fits the low-level interface requirement of Hadoop perfectly.Also, to improve performance it supports partitioning of data at the level of tables.While it comes to optimizing logical plans, Hive has a rule-based optimizer available.Hive is scalable, familiar, and extensible in nature.
For working with HiveQL Knowledge of basic SQL query is enough. We dont need any knowledge of programming language.
By using Hive, it is possible to process structured data in Hadoop.Hive makes Querying very simple, as same as SQL.By using Hive, it is possible to run Ad-hoc queries for the data analysis

Limitation of Hive
Apache Hive Tutorial discuss this following limitation of Hive. Lets discuss all We can not perform real-time queries with Hive. Also, it does not offer row-level updates.Moreover, for interactive data browsing Hive offers acceptable latency.Also, we can say Hive is not the right choice for online transaction processing.While it comes to latency, for Hive queries latency is generally very high.

Apache Hive Tutorial Usage

Here, we will look at following Hive usages.We use Hive for Schema flexibility as well as evolution.Moreover, it is possible to portion and bucket, tables in Apache Hive.Also, we can use JDBC/ODBC drivers, since they are available in Hive.