Apache Pig is one of the members of the Hadoop ecosystem when we are working with big data to perform extensive data analysis with different tools Hadoop provides. Apache pig is a tool to create map-reduce programs used in Hadoop. Hadoop has different tools for different use cases. When you have big data to analyze and you don’t have very good programming knowledge. But, if you have good SQL knowledge then Apache pig is the right choice for you. To implement in Apache pig you need to have knowledge of a scripting language called pig Latin. It has syntax similar to SQL query language. It internally executes Hadoop map-reduce jobs when we call pig Latin script. So, it will be helpful to run Hadoop map-reduce jobs without knowledge of programming language to write map-reduce jobs.
Prerequisite
As we know, if we want to learn something new either any programming language or anything apart from the programming domain. Same way, to learn Apache pig there should be some pre-requisite has to be there. As we know Apache pig is part of the Hadoop eco-system, and we know Hadoop has java as a pre-requisite. But, the Apache pig is using pig Latin which is a SQL-like scripting language. So, one pre-requisite to work with the pig is SQL query language. Java won’t be a prerequisite for learning purposes. As the pig takes care of map-reduce job execution in the background there is no necessity to learn the java language. So, you can say Java is not a prerequisite to learning Apache pig.
Use cases of Apache pig
- Data Analysis:- We can perform different types of data analysis on data using Apache pig. We will see the different types of data analysis we can do it.
- Pipeline creation:- When there need to perform a complex data analysis there used to be step-by-step analysis that needs to be performed on data. So, we need to create a data pipeline where data comes from multiple sources. We do an analysis of data and perform data transfer from source to destination.
- Log Analysis:- There used to be a huge amount of log gets generated in huge organizations. So, to perform an analysis of data we can use Apache pig to get some insights out of logs in the server which can be used to take some important decisions regarding servers.
- Health Data Analysis:- Apache Pig can be used to analyze health data and machine learning operations on health data can be performed using neural networks.
- Stock Data Analysis:- Stock data are very huge and arithmetically vast in amount. There are different types of content like open, high, low, average, volume, and much more. So, we can analyze different types of operations on stock data.
- Report Generation:- To generate different reports internally for an organization or client in a product sold, Apache Pig can be used as a prerequisite tool to provide data to generate any report useful to take the meaningful decision at the management level or come up with some detailed insights out of the huge amount of raw data available.
We have seen different scenarios where we can use Apache pig. Now, let’s see where it is useful and where it’s not. What are the advantages and disadvantages?
Pros and cons
Let’s start with benefits first:
Advantages
- Easy to learn:- You can learn easily by learning simple SQL queries. You don’t need to learn a very tough and fancy programming language to get expertise in Apache pig.
- Dataflow:- This language is also called the dataflow language. So, we can define the data flow for our task.
- Less Development Time:- As we don’t need to write programs in any programming language. It’s very quick to start and write some script to solve issues.
- Procedural language:- You can write user-defined functions in Apache pig to perform certain functionality.
- Lazy Evaluation:- Apache Pig executes the statement when it reaches the till point. So, it executes in a lazy evaluation fashion.
- Easy to control execution:- We can easily control what needs to be executed and in which fashion.
- Usage of Hadoop Features:- We know that the Apache pig is part of the Hadoop ecosystem. So, we can use different Hadoop features like hdfs to perform file read-write or any other analysis which helps us to perform better data analysis.
- UDFs:– Apache Pig has the support of user-defined functions. So, if we want to perform any complex analysis we can maintain a single job in a separate user-defined function. It benefits us in better bifurcating out tasks.
- Base Pipeline:– We use Apache pig as a base to perform different data analyses using user-defined functions. So, we can use different user-denied functions at different stages of the pipeline and Apache pig will be used as a base pipeline.
- Unstructured Dataset:- Apache pig is best suited for the data in an unstructured format. It performs conversion from unstructured data to structured data.
We all know everything has always counterparts also. So, Let’s see now some negative sides of Apache Pig.
Disadvantages
- Support:- When it comes to getting some solution or resolving some issue, there are limitations in solutions over google or Stack Overflow. It is because of the limited user base of Apache pig. Apache Hadoop has a limited user base and above it, Apache Pig has even less user base to provide support.
- Not Mature:- Even though it’s widely used for now, still it’s in the development phase. So, it’s not that mature for users of Apache Pig.
- Absence of IDE:- Still, we are in search of a good IDE for Apache pig that provides good suggestions for syntax and functionality.
- Error Handling:- When we face any errors in Apache pig it’s tough to understand the issue and find solutions. The reason behind it is Apache pig doesn’t provide a proper error message by which we can understand what is the issue going on. So, here Apache pig needs to improve error messages to guide through solutions in a better way.
- Delay in Execution:- As we have seen its lazy loading, also come up with one disadvantage which is it takes more time to execute which leads to a delay in execution.
- Implicit data schema:- Implicit schema means it decides the type of fields dynamically without specifying them manually. It makes some timing issues when it comes to content that can conform to different data types.
Difference between Apache pig and Map Reduce
| Feature | Apache Pig | MapReduce |
|---|---|---|
| Level of Abstraction | High | Low |
| Programming Language | Pig Latin (SQL-like) | Java or other programming languages |
| Ease of Use | Simple, less code | Complex, more code |
| Scalability | Yes | Yes |
| Fault Tolerance | Yes | Yes |
| Performance | Optimized for complex data processing | Raw processing power |
| Data Processing Flexibility | Opinionated approach, easier to get started | More control over the data processing pipeline |
| Data Input and Output | Built-in support for reading and writing various data formats | Customizable with third-party libraries or custom code |
Conclusion
As we have gone through different use cases of where Apache pig best suits real scenarios. Later we also verified with different advantages and disadvantages of Apache pig. So, you can decide when to use Apache pig and when not. When we are working with big data, multiple different scenarios are possible. For different scenarios, there used to be different tools available inside big data as Hadoop has a large variety of tools in the ecosystem. So, it is always required to know which tool in our toolbox needs to be used and when. It helps us to work efficiently and on time.
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What is Apache Pig
Apache Pig is a tool used for analyzing large datasets. To implement in Apache pig you need to have knowledge of a scripting language called pig Latin. It has syntax similar to SQL query language.
