Apache Pig

Apache Pig

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Score 8.0 out of 100
Apache Pig

Overview

Recent Reviews

Apache Pig

7 out of 10
April 07, 2022
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig …
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Apache Pig - lot to improve

7 out of 10
April 28, 2021
Apache Pig and its query language (Pig Latin) allowed us to create data pipelines with ease and heavily used by our teams. The language …
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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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Product Details

What is Apache Pig?

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

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Reviews and Ratings

 (24)

Ratings

Reviews

(1-9 of 9)
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Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Sourov K Chowdhury | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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!
April 07, 2022

Apache Pig

Score 7 out of 10
Vetted Review
Verified User
Review Source
Debugging the code for errors and functionalities is very time consuming leading to waste of development hours and low quality code. Since it is in early stage community support is also very less as compared to other products
Score 7 out of 10
Vetted Review
Verified User
Review Source
Write complex map reduce jobs without having much deep knowledge of Java, Python, Scala. Advanced features such as secondary sorting, optimization algorithms, predicate push-down techniques are very useful. With Apache Pig it's easy to aggregate data at scale compared to other tools. It automates important Map Reduce tasks into SQL kind queries.


Jordan Moore | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Subhadipto Poddar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
It is well suited when you are aggregating data but really difficult if you want to aggregate based upon line by line. Apache Pig can be picked up in a few days with a few demonstrations. Codes can be written quickly, however, it becomes difficult to take up complicated tasks using it.
Kartik Chavan | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
It is one great option in terms of database pipelining. It is highly effective for unstructured datasets to work with. Also, Apache Pig being a procedural language, unlike SQL, it is also easy to learn compared to other alternatives. But other alternatives like Apache Spark would be my recommendation due to the high availability of advanced libraries, which will reduce our extra efforts of writing from scratch.
Score 7 out of 10
Vetted Review
Verified User
Review Source
Apache Pig is well suited as part of an ongoing data pipeline where there is already a team of engineers in place that are familiar with the technology since at this point I would consider it relatively depreciated since there are more suitable technologies that have more robust and flexible APIs with the added benefit of being easier to learn and apply. For ad-hoc needs, I would recommend Hive or Spark-SQL if a SQL-esque language makes sense otherwise to make use of Spark + a Notebook technology such as Apache Zeppelin. For production data pipelines I would recommend Apache Spark over Apache Pig for its performance, ease of use, and its libraries.