What users are saying about
19 Ratings
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Based on 19 reviews and ratings
127 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>Score 8.7 out of 100
Based on 127 reviews and ratings
Likelihood to Recommend
Apache Pig
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
Data Analyst
The University of Texas at ArlingtonElectrical/Electronic Manufacturing, 1001-5000 employees
Apache Spark
The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Owner, previous CEO
Econometric StudiosFinancial Services, 11-50 employees
Pros
Apache Pig
- 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."
Software Consultant
Avalon ConsultingInformation Technology and Services, 51-200 employees
Apache Spark
- Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
- Faster in execution times compare to Hadoop and PIG Latin
- Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
- Interoperability between SQL and Scala / Python style of munging data
Software Engineer
LinkedInInternet, 5001-10,000 employees
Cons
Apache Pig
- May not fit every need and a SQL-like abstraction may be more effective for some tasks (look at Spark-SQL, Hive, or even an actual DBMS)
- All Pig jobs are written in a Domain Specific Language so not a lot of transferable knowledge
- Writing your own User Defined Functions (UDFS) is a nice feature but can be painful to implement in practice

Verified User
Engineer in Engineering
Computer Software Company, 51-200 employeesApache Spark
- Memory management. Very weak on that.
- PySpark not as robust as scala with spark.
- spark master HA is needed. Not as HA as it should be.
- Locality should not be a necessity, but does help improvement. But would prefer no locality
Data Czar
Envisagenics, Inc.Marketing and Advertising, 51-200 employees
Usability
Apache Pig
Apache Pig 10.0
Based on 1 answer
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Research Assistant
Iowa State UniversityHigher Education, 5001-10,000 employees
Apache Spark
Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Domain Consultant
InfosysInformation Technology & Services, 10,001+ employees
Support Rating
Apache Pig
Apache Pig 6.0
Based on 2 answers
The documentation is adequate. I'm not sure how large of an external community there is for support.
Software Consultant
Avalon ConsultingInformation Technology and Services, 51-200 employees
Apache Spark
Apache Spark 8.2
Based on 6 answers
1. It integrates very well with scala or python.2. It's very easy to understand SQL interoperability.3. Apache is way faster than the other competitive technologies.4. The support from the Apache community is very huge for Spark.5. Execution times are faster as compared to others.6. There are a large number of forums available for Apache Spark.7. The code availability for Apache Spark is simpler and easy to gain access to.8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Technical Manager
Rishabh Software Private LimitedInformation Technology & Services, 501-1000 employees
Alternatives Considered
Apache Pig
- Provided better ways for optimized hadoop jobs than Hive but not anymore.- Spark DSL is much more advanced and compute times are significantly less.

Verified User
Team Lead in Engineering
Retail Company, 10,001+ employeesApache Spark
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Verified User
Engineer in Engineering
Computer Software Company, 51-200 employeesReturn on Investment
Apache Pig
- 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.
Data Analyst
The University of Texas at ArlingtonElectrical/Electronic Manufacturing, 1001-5000 employees
Apache Spark
- It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
- Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
- It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Consultor Tecnico - Java Developer and Php Developer.
Consultec-TIComputer Software, 51-200 employees
Pricing Details
Apache Pig
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Apache Spark
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No