Explain about Pig and Hive in Hadoop and their differences

1. Downloaded from: justpaste.it/4a60o 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 high- level 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.

2. To be more specific, Pig is sort of an ETL (extract-transform-load) for Big Data. It's also quite useful and can handle large sets of data. Additionally it helps developers to adopt several question approaches. This reduces the iteration of the data scan. You can also use several nested datatypes. Much like Maps, Tuples, and Bags. You also use it for the Filter, Pig Enter, and Ordering operations. Nevertheless, there are several businesses that use Pig for most research related to MapReduce. In short, Apache Pig can be summarized as follows- ● In other words, Pig is a language of high standard, Pig Latin ● Essentially, those programmers who learn the scripting language tend to use pig ● Also, there is no need to create a schema to store the data. ● Additionally, Pig's compiler translates Pig Latin into MapReduce program sequences Difference between Pig and Hive in Hadoop Used Language ● Apache Hive There is a declarative language named HiveQL in Hive that is like SQL. ● Apache Pig There is a procedural language named Pig Latin in Pig. Use of Apache Pig and Hive ● Apache Hive Data scientists mainly use the Apache Hive. ● Apache Pig Researchers and programmers mainly make use of Apache Pig. Data ● Apache Hive Hive essentially allows for structured data. ● Apache Pig Apache Pig does allow both structured and semi-structured data, however. Works on ● Apache Hive Hive portion essentially operates on a cluster side of the server. ● Apache Pig Pig server however resides on the cluster's client side. ETL (Transform-Load extractor) ● Apache Hive You may claim that Apache Hive is an asset to ETL. ● Apache Pig Though Pig itself is a Big Data ETL device. Support for Avro Date Format Apache Hive Apache Hive usually does not support the Avro file format. However it can be achieved with Serge 's help "Org. Apache. Hadoop. Hive.serde2.Avro." Apache Pig Hive does Avro File support. Developer support Apache Hive It was Facebook that first created Hive. It was Yahoo who first developed Pig. Splitting

3. Apache Hive Apache Hive allows partitioning. Pig Apache Pig does not back Partition. Loading Tempo Apache Hive Rapidly executed Hive but can't load it quickly. Apache Pig Pig can load the data fast and efficiently. UDFs (Defined User Functions) Apache Hive It does support UDFs but is very difficult to debug. Apache Pig In Pig, the computation of matrices is very easy to write UDFs. Linked subject — Best Hive books for studying Hive Usage — Pig vs Hive a. Using Hive In the examples below you will see the use of Hive. ● You can use Hive while the SQL queries and definitions are familiar to us. ● Though you do systematic analysis of historical data ● Hive needs structured data to completely unleash its computing and analytical capabilities. ● Hive does not, however, accept the Real-time analysis. So, HBase is the real-time analytics option. ● In specific, for the data analysts ● If you need to imagine it after the data analysis and create reports, you can use Hive. ● Hive is then comparatively slower than Pig. b. Using Pig As we discussed above, Pig is a scripting language so in the following scenarios you can use it. ● Although you know the language of scripting very well, and are a programmer. ● Especially for all the work related to loading data While you don't want to create the schema. ● Because it has many SQL-related functions, and you also have cogroup functions ● It does support the format of Avro Hadoop files ● Pig is swifter than Hive Conclusion As a consequence, you have seen all of the Pig vs Hive arguments. You also learned Hive Use as well as Pig Use. I hope you get a good understanding of the difference between Pig and Hive, though. You can learn more through big data online training.