Apache Pig Professional, Scientific, and Technical Services Reviews & Insights

Score8.4 out of 10

22 Reviews and Ratings

Community insights

TrustRadius Insights for Apache Pig are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Business Problems Solved

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.

Apache Pig Reviews

3 Reviews

Professional, Scientific, and Technical ServicesInformation Technology & Services3

A great ETL tool for your big data

Rating: 9 out of 10

Incentivized

May 23, 2022

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.

Verified User

Program Manager in Information Technology (Information Technology & Services company, 201-500 employees)

Vetted Review

1 year of experience

"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."

Rating: 8 out of 10

Incentivized

April 9, 2022

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

Apache Pig is a lightweight framework that is simple to learn and put into production. It converts MapReduce tasks into SQL-like queries. It also reduces the data and performs some simple mathematical functions. Combining data is incredibly beneficial. With Apache Pig's Data Time functions, we can get quicker results. It works on 150-180 GB monthly datasets and reduces them in a few minutes. However, it cannot perform sequential operations, such as comparing consecutive lines. And another flaw of this method is that it doesn't allow loops and nested loops to span more than one variable at a time. Then again, I'd say go for it!

Sourov K Chowdhury

Database Software Engineer in Product Management at Best Web Design Ltd. (Information Technology & Services, 11-50 employees)

Vetted Review

1 year of experience

View profile

Useful ETL scripting tool

Rating: 8 out of 10

Incentivized

March 20, 2020

Use Cases and Deployment Scope

Pig is used by data engineers as a stopgap between setting up a Spark environment and having more declarative flexibility than HiveQL while moving away from MapReduce. It solves the problem of needing to iteratively transform and migrate data between supported Hadoop environments while being able to debug the process at each step.

Pros

Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."

Cons

General syntax for the FOREACH ... GENERATE feature is confusing for nested actions.
The docs are hard to navigate, but it is made up for by reasonable examples.
A version less than 1.0 doesn't instill confidence in the product that has been around for over half a decade (as of writing).

Likelihood to Recommend

If someone wants to process data and doesn't have access to platforms such as Spark or Flink, and wants to do so in a minimal, portable fashion that requires simply requires learning a new scripting language, then Pig is great. It also supports running the same code against a cluster as a single developer machine for testing.

Pig is more suited for batch ETL workloads, not ML or Streaming big data use-cases.

Jordan Moore

Software Consultant in Information Technology at Avalon Consulting (Information Technology and Services, 51-200 employees)

Vetted Review

2 years of experience

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"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."

Rating: 8 out of 10

Incentivized

April 9, 2022

Use Cases and Deployment Scope

Pros

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.

Cons

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.

Likelihood to Recommend

Sourov K Chowdhury

Database Software Engineer in Product Management at Best Web Design Ltd. (Information Technology & Services, 11-50 employees)

Vetted Review

1 year of experience

View profile