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Explain about Pig and Hive in Hadoop and their differences Pig hadoop and Hive hadoop have a similar function. They are tools that ease the difficulty of writing MapReduce java complex programs. Hadoop ecosystem components Apache HIVE and Apache PIG are briefed. If you take a look at the Hadoop ecosystem's diagrammatic representation, HIVE and PIG components cover the same verticals and this certainly raises the question which one is better. It is Pig vs Hive. There is no easy way to compare both Pig and Hive without looking further into each of them in more depth as to how they help process large quantities of information. This post compares some of Pig Hadoop and Hive Hadoop's popular features to help users understand their similarities and the difference between them.Until you talk about pig vs hive, let's explore in depth what Apache Pig and Hive in Hadoop. Let's speak in depth about Apache Hive Architecture & Components To more information visit:big data and hadoop course Blog. Apache Hive in Hadoop Essentially, Hive is an important part of the Hadoop Ecosystem for the data analysis. You can do this when you have the data organized. First of all, however, you need to format the data then you can only inject it into the Hive tables. For all those who are familiar with SQL, Hive can be simple though. You can also optimize Hive queries as similar to optimizing the SQL query. In addition, there are several other apps at Hive. Such as Bucketing and Partition. Particularly that makes analysis of your data easy and quick. It later became one of the top Apache projects but was built at first on Facebook. It also allows the user to be flexible by writing less code and doing more with it. It also transforms the queries into execution with MapReduce. You need not think much about the backend processes though. Hive also uses a query language quite similar to that of SQL known as HQL (Hive query language). Additionally, unlike SQL, which involves strict adherence to schemas when storing data, Apache Hive works well in processing data stored in a distributed manner. Even so, Hive has many features that you can use directly, which makes our work easy. In addition, in Hive, if anything is not usable, you always have the option to build UDFs (user-defined functions). Definitely, that will do the work. Business analysts, analysts mostly prefer Hive. In short, Apache Hive can be summarized as follows● It is the foundation for data warehouses ● Hive uses a language called HQL, and the language is very similar to SQL. ● It provides many methods for fast extraction, transformation, and data charging. ● You can use and describe custom mappers and reducers in Hive. ● It is preferred most for data analytics and work related to reporting. Apache Pig in Hadoop Basically, you can use Apache Pig to reduce the coding complexity with MapReduce. It renders as a highlevel data flow system to a simple language called Pig Latin. In particular, which is used for manipulating and querying data. Similarly, you don't need to build the schema in Pig to store the data. You can also load the files directly, and start using them. But you can also use semi-structured data in Pig which is Pig's advantage.