Apache Pig

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.

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.

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.

Apache Pig and its query language (Pig Latin) allowed us to create data pipelines with ease and heavily used by our teams. The language is designed to reflect the way data pipelines are designed, so it discards extraneous data, supports user defined functions (UDFs) , and offers a lot of control over the data flow.

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.

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 handle data streaming from multiple sources and join them. This can be used to extract key findings, aggregate results and finally process output which is used for different types of visualizations.

As a requirement of a distributed processing system, we are using Apache Pig within our Information Technology department. I use it to an extent of generating reports with advanced statistical methods, both for internal use as well as external purposes. But our Data Science team and Data Engineering team use it to build pipelines in Big Data environment, to conduct further advanced analysis including for machine learning purposes.

Apache Pig is one of the distributed processing technologies we are using within the engineering department as a whole and we are currently using it mainly to generate aggregate statistics from logs, run additional refinement and filtering on certain logs, and to generate reports for both internal use and customer deliveries.

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 analytics. It provides easy and better alternatives to writing Java map-reduce code.