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We keep hearing the term Big Data in our surroundings and the framework that is used to handle this unstructured Data i.e. “Big Data” is termed as Hadoop. Pig, as well as Hive, is considered as the two most essential components of the Hadoop ecosystem. Just like SQL, Hadoop is also a tried and tested tool for its performance and analysis when it comes to Big Data. It’s just that, SQL is quite old and have gained the trust of many since years and Hadoop is still yet to achieve that level. But, it is great to see that numerous clients are using Hadoop data stores because data querying high-level languages in the Hadoop ecosystem has become essential these days. Now two key components are used the most, i.e. Pig & Hive. We will try to put some more light on the difference between both of them and address following topics under this blog.
Pigs as well as Hive, both of them are the tools that allow us to write complex Java MapReduce programs with an ease. Let’s gain some more information about both of them individually and then later we will see the basic difference between both of them. Apache Hadoop is a well-known framework that is used for processing, storing as well as analyzing large volumes of unstructured data that we term as Big Data. This technology deals with, big data that run into Terabytes, petabytes, and zeta bytes these days with numerous key components that makes Hadoop Ecosystem.
Pig Hadoop is a high-end data flow system that provides us a simple language platform that is named Pig Latin and can be used for manipulating saved data and even queries. The pig is used by Microsoft, Google and Yahoo to handle (collect and save) huge set of data. You must be aware that the SQL programmers usually work in languages that are relatively easy to learn from a person who is already known to SQL. Pig Latin is the Pig's language and is considered as one of the most simple query algebra. It enables to express data transformations like merging data sets, filtering as well as applying those functions to groups of records. Users can also create or write different functions to do the special-purpose processing.
Read: Apache Flink Tutorial Guide for Beginner
Pig Hadoop is best when you have to deal with plenty of unstructured as well as unorganized data. No deviation from the basic SQL foundation increases its demand many people do really like dealing with much of MapReduce tasks. Hence, if you are thorough with SQL then this is also easy to learn.
Developers that are not really comfortable and well-versed working with the MapReduce framework feel absolutely delighted while working with Hive Hadoop. Hive is like a Data Warehousing Package that is used to analyze huge volumes of data and is meant for those can work using SQL with an ease. There is no need for users to write MapReduce programs. So Hive is best for someone who is not comfortable with Java programming. So, here is how you can understand well about Hive Hadoop.
Whenever you wish to query and analyze historical data, then Hive is your thing. A well-organized data helps Hive totally to get into completing the processing as well as analyzing the entire process.
Read: Hadoop HDFS Commands Cheat Sheet
There is only one way through which we can differentiate well in between both of them and that is by having a deep understanding of their concepts and after knowing how exactly they help users to process a huge volume of data with an ease. We have already given you detailed information about
What is the Pig Hadoop and Hive Hadoop?
So, let’s begin with understanding the basic difference between both of them.
Read: What is Spark? Apache Spark Tutorials Guide for Beginner
| Apache Pig | Apache Hive |
| 1. Procedural Data Flow Language | Declarative SQLish Language |
| 2. Mainly used for a good level of Programming | Mainly used for creating accurate reports |
| 3. Used by Researchers and Programmers | Mainly used by Data Analysts |
| 4. Operates on the client side of a cluster. | Operates on the server side of a cluster. |
| 5. Does not have a dedicated metadata database. | Makes use of exact variation of dedicated SQL DDL language by defining tables beforehand. |
| 6. We are not pretty sure that accessing raw data is as fast as with HiveQL. | Hive has smart inbuilt features on accessing raw data |
| 7. The schemas or data types will always be defined in the script itself. | The schemes or other data are stored in the local database |
| 8. The Pig is SQL like, but varies to a great extent and hence it usually takes little extra time as well as efforts to master in the same. | Directly leverages SQL and hence unlike Pig, it is easy to learn from database experts. |
| 9. Pig supports Avro file format. | Hive does not support Avro file format. |
Conclusion
Choosing Pig Hadoop or Hive Hadoop totally depends on your purpose to use them and the type of data you are handling. Based on the above-mentioned differences, you can very well understand how you can use either of them effectively. After understanding the basic differences between Pig as well as Hive, you can use both of the components based on what you are trying to achieve. They will definitely help you achieve the desired goals. Both the Hive’s as well as Pig’s components are seen to have the same number of users in various projects.
Read More: Hive Interview Questions and Pig Interview Questions
Read: Top 45 Pig Interview Questions and Answers
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