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 …
Apache Pig is called Pig Latin—that it provides a high-level scripting language to perform data analysis, code generation, and …
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig …
Apache Pig and its query language (Pig Latin) allowed us to create data pipelines with ease and heavily used by our teams. The language …
Pig is used by data engineers as a stopgap between setting up a Spark environment and having more declarative flexibility than HiveQL …
Apache Pig is being used as a map-reduce platform. It is used to handle transportation problems and use large volume of data. It can …
As a requirement of a distributed processing system, we are using Apache Pig within our Information Technology department. I use it to an …
Apache Pig is one of the distributed processing technologies we are using within the engineering department as a whole and we are …
Yes, it is used by our data science and data engineering orgs. It is being used to build big data workflows (pipelines) for ETL and …
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Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
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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.
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!
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
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.
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.
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.
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.
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.
- Custom load, store, filter functionalities are needed and writing Java map reduce code is not an option due susceptible to bugs.
- Chain multiple MR jobs into one pig job.
- Chain multiple MR jobs into one pig job.