TrustRadius: an HG Insights company

Apache Pig

Score8.4 out of 10

22 Reviews and Ratings

Get a Demo

What is Apache Pig?

Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.

Categories & Use Cases

Reviews

What users are saying about Apache Pig.
View all reviews

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

Incentivized
Sourov K ChowdhuryView profile
Database Software Engineer in Product Management at Best Web Design Ltd. (11-50 employees employees)

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.

Most Important Features

  • 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.

Return on Investment

  • 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.

Alternatives Considered

Apache Hive, Google BigQuery and Apache Spark

Other Software Used

Jira Software, Databricks Lakehouse Platform (Unified Analytics Platform), Eclipse

Useful ETL scripting tool

Incentivized
Jordan MooreView profile
Software Consultant in Information Technology at Avalon Consulting (51-200 employees employees)

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).

Return on Investment

  • Iterate quickly on ETL pipelines.
  • Scale up parallel processing.
  • Easily templatable scripting language.

Alternatives Considered

Apache Spark, Apache Flink and Apache Hive

A great ETL tool for your big data

Incentivized
Verified User
Program Manager in Information Technology (201-500 employees employees)

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.

Most Important Features

  • 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.

Return on Investment

  • 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).

Alternatives Considered

Apache Hadoop, Azure Data Lake Storage, Amazon EMR (Elastic MapReduce), Presto (formerly Presto DB), Confluent Platform and Alteryx

Other Software Used

Cloudera Data Platform, Alteryx, Apache Flink, Splunk Cloud, Google BigQuery, Databricks Lakehouse Platform (Unified Analytics Platform)

My Apache Pig Review

Incentivized
Kartik ChavanView profile
Data Analyst in Information Technology at The University of Texas at Arlington (1001-5000 employees employees)

Pros

  • Long logics in Java? Apache Pig is a good alternative.
  • Has a lot of great features including table joins on many databases like DBMS, Hive, Spark-SQL etc.
  • Faster & easy development compared to regular map-reduce jobs.

Cons

  • UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors.
  • Being in early stage, it still has a small community for help in related matters.
  • It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement.

Return on Investment

  • Return on Investments are significant considering what it can do with traditional analysis techniques. But, other alternatives like Apache Spark, Hive being more efficient, it is hard to stick to Apache Pig.
  • It can handle large datasets pretty easily compared to SQL. But, again, alternatives are more efficient.
  • While working on unstructured, decentralized dataset, Pig is highly beneficial, as it is not a complete deviation from SQL, but it does not take you in complexity MapReduce as well.

Alternatives Considered

Apache Hive, Apache Spark and Apache Spark MLib

Other Software Used

Apache Hive, Apache Spark, Apache Spark MLib

Apache Pig

Verified User
C-Level Executive in Product Management (51-200 employees employees)

Use Cases and Deployment Scope

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.

Pros

  • Useful for map -reducing huge datasets
  • Easy to learn and deploy
  • Optimization is higher compared to relative products.

Cons

  • 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

Most Important Features

  • Easily process any size of data
  • Understanding schema is also very easy
  • Reduces complexity of implementing Map-Reduce

Return on Investment

  • Inefficient Debugging
  • Writing UDFs is very challenging

Alternatives Considered

Apache Hive