Pig vs Hive
Ali Ahmed Shaikh
Data Engineer | Expert in ETL Solutions & Database Optimization | Driving Business Growth Through Data Excellence
Pig and Hive are the two key components of the Hadoop ecosystem. What does pig hadoop or hive hadoop solve? Pig hadoop and Hive hadoop have a similar goal- they are?tools that ease the complexity of writing complex java MapReduce programs. However, when to use Pig Latin and when to use HiveQL is the question most of the have?developers have. Apache HIVE and Apache PIG components of the Hadoop ecosystem are briefed. If we take a look at the diagrammatic representation of the Hadoop?ecosystem, HIVE and PIG components cover the same verticals.
Is the battle HIVE vs PIG real? Does the pair have the same advantages and disadvantages while processing enormous amounts of data? The answer is NO, there is no HIVE?vs PIG in the real world, it’s just the initial ambiguity on deciding the tool which suits the need. Hive Query language (HiveQL) suits the specific demands of?analytics meanwhile PIG supports huge data operation. PIG was developed as an abstraction to avoid the complicated syntax of Java programming for MapReduce. On the?other hand HIVE, QL is based around SQL, which makes it easier to learn for those who know SQL. AVRO is supported by PIG making serialization faster. When it really?boils down to taking a decision between Pig and Hive, the suitability of each component for the given business logic must be considered and then the decision must be?taken.
Pig vs. Hive- Performance Benchmarking
Apache Pig is usually more efficient than Apache Hive as it has many high-quality codes. When implementing joins, Hive creates so many objects making the join?operation slow. Here are the results of the Pig vs. Hive Performance Benchmarking Survey conducted by IBM –
The results of the Hive vs. Pig Benchmarking Survey revealed Pig consistently outperformed Hive for most of the operations except for grouping of data.