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Apache Pig has proven to be an invaluable tool for data engineers working with large datasets in the Apache Hadoop ecosystem. Users have …
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What is Apache Pig?
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
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What is Apache Pig?
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Apache Pig has proven to be an invaluable tool for data engineers working with large datasets in the Apache Hadoop ecosystem. Users have found it to be an excellent high-level scripting language that simplifies the process of working with big data. With Apache Pig, data engineers can easily build pipelines for advanced analysis and machine learning purposes, allowing them to transform and optimize data operations into MapReduce.
One of the key advantages of Apache Pig is its ability to write complex map-reduce or Spark jobs without requiring deep knowledge of Java, Python, or Groovy. This feature has been highly appreciated by users who value the efficiency and simplicity it brings to their work. Additionally, Apache Pig's query language, Pig Latin, provides users with a straightforward way to build data pipelines, eliminating redundant data and supporting user-defined functions UDFs.
The software also gives users control over task execution, which is crucial in maintaining control in a distributed processing system. This control allows users to efficiently handle transportation problems and manage large volumes of data including data streaming from multiple sources and performing joins. Users have utilized Apache Pig to explore and process large datasets in big data analytics projects, performing various operations within a single Java Virtual Machine.
Another key use case for Apache Pig is the generation of aggregate statistics, running refinement and filtering on logs, as well as generating reports for both internal use and customer deliveries. Data science and data engineering teams also utilize Apache Pig for building big data workflows pipelines for ETL and analytics. The software simplifies the creation of these pipelines by providing native language support with Pig Latin, combining features from various database systems like Hive, DBMS, and Spark-SQL.
Overall, Apache Pig offers a versatile solution for handling big data tasks in a simple yet efficient manner. Its user-friendly query language and extensive capabilities make it a valuable tool for data engineers working in the Apache Hadoop ecosystem.
Users have provided several recommendations for using Pig as a tool for writing quick big data applications.
One recommendation is that Pig is a good starting point for developing ad-hoc analytics applications, especially for those with basic programming experience in Java.
Another recommendation is to use Pig as a base pipeline for parallelizing and utilizing User-Defined Functions (UDFs) on large datasets. The lazy evaluation feature of Pig allows for efficient program optimization.
Users also appreciate Pig's integration with Hadoop, which provides parallelization, fault-tolerance, and relational database features. This makes Pig suitable for applying statistics to datasets, and its functional programming paradigm aligns well with pipeline processes.
Additionally, users suggest considering Spark or Hive as alternative tools for developing pipelines. While Pig may be more suitable for developers with programming experience, it is free and has extensive online documentation available for learning purposes.
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(1-4 of 4)Companies can't remove reviews or game the system. Here's why
May 23, 2022
A great ETL tool for your big data
We are working on a large data analytics project where we have to work on big data, large datasets, and databases. We have used Apache Pig as it helps to explore and process large datasets. It helps in performing several operations such as local execution environments in a single Java Virtual Machine. Apache Pig is somehow easy to learn and use and the data structures are nested and richer. We have used largely whenever we used the analytical insights for our sampling data.
- It provides great support to large datasets and ad-hoc reporting.
- It has almost all the set of operators to perform actions such as Join, Sort, Merge, etc.
- Anybody can use Apache Pig with some initial training and it is very much familiar with SQL.
- It can handle almost all structured, and unstructured data.
- Apache Pig is built using the data flows, users can easily see all the processes and information.
- One of the most important limitations of Apache Pig is it does not support OLTP (Online Transaction Processing) as it only supports OLAP (Online Analytical Processing).
- Apache Pig has very high latency as compared to Map Reduce.
- Apache Pig is designed for ETL and thus not perfectly suited for real-time analysis.
- The training materials are hard to learn and need improvements.
- Apache Pig helps us in processing our large datasets for data analytics.
- Apache Pig helps us process Map Reduce in a single script file.
- Apache Pig has good training materials for users, although required some improvements.
- It helps us in providing local and remote interoperability.
- Apache Pig is best known for its fast execution of data processing (+ROI).
- Scaled up large parallel processing on data.
- It helps in saving our time in data processing (+ROI).
- Large community base for quick resolutions (+ROI).
- Compatibility with other 3rd parties applications and tools (-ROI).
- Apache Hadoop, Azure Data Lake Storage, Amazon EMR (Elastic MapReduce), Presto (formerly Presto DB), Confluent Platform and Alteryx
April 09, 2022
"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."
Apache Pig is called Pig Latin—that it provides a high-level scripting language to perform data analysis, code generation, and manipulation. It is an excellent high-level scripting language for working with large data sets. That work under Apache's open-source project Hadoop. Because of this, we can transform and optimize the data operations into MapReduce, which can be difficult on other platforms. We quickly and easily built data pipelines using its query language. It eliminates redundant data, supports user-defined functions (UDFs), and controls data flow well. Its efficiency in writing complex map-reduce or Spark jobs without deep knowledge of Java, Python, or Groovy is what I like best about Apache Pig. Furthermore, with the assistance of a pig, it is simple to maintain control over the execution of a task.
- Its performance, ease of use, and simplicity in learning and deployment.
- Using this tool, we can quickly analyze large amounts of data.
- It's adequate for map-reducing large datasets and fully abstracted MapReduce.
- Pig's error debugging consumes most of its development time because it can be unstable and immature.
- It is significantly more challenging to learn and master than Hive. It's a little slower than Spark.
- Apache Pig makes it simple to handle any amount of data.
- Apache Pig is easy to use and has many options.
- Apache Pig simplifies the Map-reduce process.
- Apache Pig's scripting language is template-friendly.
- A lightweight framework, Apache Pig, is easy to learn and deploy.
- It converts MapReduce tasks into SQL-like queries, useful for data analysis.
- It reduces the amount of data and performs a few simple mathematical operations on the data.
- Combining data is a huge advantage.
It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.
April 07, 2022
Apache Pig
Score 7 out of 10
Vetted Review
Verified User
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig latin which helps to manage to code execution easily. It brings the important features of most of the database systems like Hive, DBMS, Spark-SQL.
- Useful for map -reducing huge datasets
- Easy to learn and deploy
- Optimization is higher compared to relative products.
- Pace of introducing new features is very slow.
- Community is also relatively small because it is still in early stage.
- Debug functionality is not there, also it is compile time
- Easily process any size of data
- Understanding schema is also very easy
- Reduces complexity of implementing Map-Reduce
- Inefficient Debugging
- Writing UDFs is very challenging
It can accommodate Map Reduce in a single script and a single fine. IT has very much documentation present for easy learning. SQL like queries makes it easy to understand
April 28, 2021
Apache Pig - lot to improve
Apache Pig and its query language (Pig Latin) allowed us to create data
pipelines with ease and heavily used by our teams. The language is designed to reflect the way data
pipelines are designed, so it discards extraneous data, supports user
defined functions (UDFs) , and offers a lot of control over the data
flow.
- Data pipeline and aggregation
- Log parsing and reporting
- Combine Map Reduce jobs
- Pig lacks in supporting the advanced features that Apache Spark provides
- Well outdated
- Debugging in Pig is a complex part
- Handling unstructured dataset
- To perform the tasks of collecting, loading, consolidating the data
- Apache Pig is a 1st pass compiler, which is at its best using DAG.
- Doesn't support all kinds of SQL-like abstraction
- It's DML based scripting requires lot of training
- Error handling is not helpful in debugging production issues
Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.