--- title: Apache Pig Reviews meta_title: 'Apache Pig Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 21 reviews by the users' company size, role or industry to find out how Apache Pig works for a business like yours. aggregate_rating: rating_value: 3.9 review_count: 21 scale: '5' date_modified: '2026-08-09' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Apache Pig Reviews **Vendor:** The Apache Software Foundation **Category:** [Big Data Analytics Software](https://www.g2.com/categories/big-data-analytics) **Average Rating:** 3.9/5.0 **Total Reviews:** 21 ## About Apache Pig Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. ## Apache Pig Reviews ### 1. Apache Pig makes it easy to create efficient data pipelines **Rating:** 4.5/5.0 stars **Reviewed by:** Prashant V. | Enterprise (> 1000 emp.) **Reviewed Date:** March 20, 2020 **What do you like best about Apache Pig?** Apache Pig and its query language (Pig Latin) allowed us to create data pipelines with ease. 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. **What do you dislike about Apache Pig?** Pig being a greedy language, will not evaluate data until it's actually needed. So errors are not visible unless you actually try to dump/print the data. There is no "debug" functionality to run the code in a dry-run mode. **Recommendations to others considering Apache Pig:** Unless you already have implementations of Pig in the company that you are building on top of, you might be better off with other newer technologies with more **What problems is Apache Pig solving and how is that benefiting you?** I have used Pig for data piplining and aggregation. The flow of the language reflects the flow of the data and so it is intuitive to understand what the data transformation is doing. However it hasn't kept up with the latest advances in technologies. If you were choosing a language, you would be better off with either Hive or Spark. Pig also has a steeper learning curve since it uses a proprietary language (Pig Latin). ### 2. Apache Pig has saved my life from forever coding! **Rating:** 4.5/5.0 stars **Reviewed by:** Prashanth K. | Big Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 18, 2020 **What do you like best about Apache Pig?** What I like best about Apache Pig how efficiently we can write any of our complex map reduce or spark jobs without having much deep knowledge of Java, Python, Groovy. Also, its easy to control the execution of job with the help of pig. **What do you dislike about Apache Pig?** What I dislike about Apache Pig is its error debugging consume most of its development time as it can be some times immature/unstable. Also the support community is very much less when compared to that of hadoop mapreduce or spark issues. **Recommendations to others considering Apache Pig:** For all of you who are facing writing complex mapreduce or spark jobs, you should definitely try pig **What problems is Apache Pig solving and how is that benefiting you?** One of the problem which I was able to solve was, I was able to reduce latency using pig which was earlier observed while running spark jobs mainly due to optimization offered by Apache pig. ### 3. Useful for map reducing huge dataset - application in transportation engineering for probe data **Rating:** 4.5/5.0 stars **Reviewed by:** Subhadipto P. | Engineer II, Enterprise (> 1000 emp.) **Reviewed Date:** July 31, 2018 **What do you like best about Apache Pig?** It can handle some of the simple mathematical operations, along with reducing the data. Aggregating the data is extremely useful. Running DateTime functionalities in apache pig is really a useful feature for faster and quicker results. Pig works on datasets of around 150 to 180 GB per month and reduces them efficiently within say 10 to 12 minutes. I would definitely recommend Apache pig to any basic coding person in the field of transportation engineering to start using Apache pig, especially when you need to handle huge dataset. **What do you dislike about Apache Pig?** It cannot perform sequential operations, like taking consecutive lines and then comparing them. However, the workaround is to rank the segments, merge them and then perform the task. The main drawback still lies in the fact that it cannot be used to perform loops and nested loops across any variable(s). Hive might be a better choice in certain cases for that reason. **Recommendations to others considering Apache Pig:** Reducing huge volume of data **What problems is Apache Pig solving and how is that benefiting you?** Reducing speed data provided by INRIX to a monthly or a yearly aggregate. Pig is also utilized to calculate various existing traffic signals performances which were otherwise calculated using various small scale measures. The main benefit of using pig on such operations is that any city agency can prepare the codes for once and run it throughout the city, or even they can extend it to a statewide level which will make it very efficient, quicker, and more reliable than the traditional way of handling them using SQL database ### 4. Apache Pig **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** January 18, 2019 **What do you like best about Apache Pig?** Less number of instructions does big tasks of collecting, loading, consolidating the data. **What do you dislike about Apache Pig?** Not enough tools to debug Incorrect/misleading exceptions **What problems is Apache Pig solving and how is that benefiting you?** Report generation Data Analysis Alert setup based on threshold Triaging of the production issues. ### 5. Powerful data analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** September 17, 2017 **What do you like best about Apache Pig?** - SQL like syntax - powerful and feature-rich **What do you dislike about Apache Pig?** - Much more difficult to use than Hive - takes a while to get used to and learn as compared with Hive **What problems is Apache Pig solving and how is that benefiting you?** Data insights for programmatic advertising ### 6. Apache Pig **Rating:** 5.0/5.0 stars **Reviewed by:** kevin r. | A/R Analyst - Write-offs, Internet, Enterprise (> 1000 emp.) **Reviewed Date:** March 10, 2017 **What do you like best about Apache Pig?** The ecosystem and the way it works. Being able to implement and integrate what you currently use. **What do you dislike about Apache Pig?** I think getting started is a bit patchy but once you're familiar and used to it, it can be very helpful. **What problems is Apache Pig solving and how is that benefiting you?** Being able to utilize data efficiently regarding analytics. ### 7. A very good big data solution for querying **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** May 26, 2016 **What do you like best about Apache Pig?** 1. SQL like syntax. 2. Ease of use. 3. Short learning curve 4. Ease of maintenance 5. Decrease in development time. This is the biggest advantage especially considering vanilla map-reduce jobs' complexity, time-spent and maintenance of the programs. **What do you dislike about Apache Pig?** 1. Slow for larger queries 2. Errors need to be better 3. Support is less 4. Source and Sink need to be present 5. Especially the errors that Pig produces due to UDFS(Python) are not helpful at all. When something goes wrong, it just gives exec error in udf even if problem is related to syntax or type error, let alone a logical one. This is a big one. **Recommendations to others considering Apache Pig:** You have UDFs which you want to parallellize and utilize for large amounts of data, then you are in luck. Use Pig as a base pipeline where it does the hard work and you just apply your UDF in the step that you want. Lazy evaluation: unless you do not produce an output file or does not output any message, it does not get evaluated. This has an advantage in the logical plan, it could optimize the program beginning to end and optimizer could produce an efficient plan to execute. Enjoys everything that Hadoop offers, parallelization, fault-tolerancy with many relational database features. If you want to do apply some statistics to your dataset. Functional programming paradigm fits quite naturally to pipeline processes, so I expect it to be quite successful. **What problems is Apache Pig solving and how is that benefiting you?** Data Analysis for the raw data we have. Initial data exploration has been useful with pig. ### 8. Big data? No problem with the operations! **Rating:** 5.0/5.0 stars **Reviewed by:** Gökhan E. | Technology Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** March 07, 2016 **What do you like best about Apache Pig?** I have used apache pig on my part time job which handles big data and apache pig scripts helped me a lot. I have created custom functions and it makes easier to handle complex and huge tasks and makes easier to maintain after configuration. Also the system optimization of pig script jobs helped me to focuse on semantics and so. The default mode i mean the map reduce mode is very efficient. **What do you dislike about Apache Pig?** Sometimes i feel that our data is not that big in order to be handled with pig script. Its documentation makes me sweat and takes a lot of time to get used to. **What problems is Apache Pig solving and how is that benefiting you?** I was using it in Ad platform and our servers were getting too much requests and datas. While targeting this data pig scripts helped me a lot. ### 9. Analyzing large data can be so easy with this tool! **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Internet | Small-Business (50 or fewer emp.) **Reviewed Date:** March 17, 2016 **What do you like best about Apache Pig?** Pig is a great high level scripting language for operating with big datasets that work under the Apache's open-source project Hadoop. This software allow you to transforming and optimizes the data operations into MapReduce, something that can be challenging with others platforms. I recomment this tool to my clients that need to manage a big list of users that will load a considerable amount of data daily. This can help you to clean, search and declares independent execution plans easily. You can compare this tool with sql programming but the way this tool use UDF help you with ease call the functions directly with Java, Js, Python and of course the big Ruby. **What do you dislike about Apache Pig?** At the beginning was a bit difficult to get used to working under his pig latin language, however there is very good documentation online that allow you to manage your process. Apache Pig it got many competitors so they will need to optimize the system because sometimes the scripts won't get you the ideal results. **Recommendations to others considering Apache Pig:** Learn Pig Latin and be ready to have an easy day of work **What problems is Apache Pig solving and how is that benefiting you?** My clients used normally to big process with data sets that will contain specially json objects they will be available to solve very convoluted data sets. ### 10. Awesome! Love this tool. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** May 13, 2014 **What do you like best about Apache Pig?** Once you get through the ideas and concepts that this software set can do you will love it to. Its extremely procedural which really reminds me of sql programming. It is extremely powerful, can connect to Hbase and of course Hive. **What do you dislike about Apache Pig?** Optimizing. This can be difficult and be considered "artsy". This is because pig creates map reduce jobs that run on hadoop. So if your cluster isn't totally optimized, just optimizing this scripts won't get you the best results. **Recommendations to others considering Apache Pig:** If you are looking for a tool that is more geared towards developers and not novice users than pig is your tool. It is free and lots of documentation online. **What problems is Apache Pig solving and how is that benefiting you?** ELT. We use pig to process our disparate data sets, containing json objects and "normalized" data to create data warehouse structures in hive. We have realized that we are able to solve complex problems (joining for example) without having to write custom map reduce jobs. ## Apache Pig Discussions - [Is apache pig going out of businees?](https://www.g2.com/discussions/26561-is-apache-pig-going-out-of-businees) - 1 upvote - [View Apache Pig pricing details and edition comparison](https://www.g2.com/products/apache-pig/reviews?filters%5Bnps_score%5D%5B%5D=5§ion=pricing&secure%5Bexpires_at%5D=2026-08-12+13%3A07%3A07+-0500&secure%5Bsession_id%5D=082b5935-4ceb-4a86-b4df-1dd79c87a121&secure%5Btoken%5D=a3eeb5b32146dd0a1e62db9e06c2fdb81c13381c90012c8f23cd87ebd23ab0fe&format=llm_user) ## Apache Pig Features **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **Operations** - Data Visualization - Data Workflow - Data Discovery **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management ## Top Apache Pig Alternatives - [Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews) - 4.3/5.0 (415 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (714 reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,145 reviews)