Overall Satisfaction with Apache Pig
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.
- 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.
- 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.
- 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.
- 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.
It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.
Do you think Apache Pig delivers good value for the price?
Are you happy with Apache Pig's feature set?
Did Apache Pig live up to sales and marketing promises?
Did implementation of Apache Pig go as expected?
Would you buy Apache Pig again?
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!