Differences Between Pig vs Spark
Pig vs Spark is the comparison between the technology frameworks that are used for high volume data processing for analytics purposes. Pig is an open-source tool that works on the Hadoop framework using pig scripting which subsequently converts to map-reduce jobs implicitly for big data processing. Whereas Spark is an open-source framework that uses resilient distributed datasets(RDD) and Spark SQL for processing the big data. Spark framework is more efficient and scalable as compared to the Pig framework. Pig Latin scripts can be used as SQL like functionalities whereas Spark supports built-in functionalities and APIs such as PySpark for data processing.
Head to Head Comparison Between Pig and Spark (Infographics)
Below is the top 10 Comparison Between Pig and Spark:
Key Differences Between Pig and Spark
Below are the lists of points, describe the key Differences Between Pig and Spark
- The Apache Pig is general purpose programming and clustering framework for large-scale data processing that is compatible with Hadoop whereas Apache Pig is scripting environment for running Pig Scripts for complex and large-scale data sets manipulation.
- Apache Pig is a high-level data flow scripting language that supports standalone scripts and provides an interactive shell which executes on Hadoop whereas Spark is a high-level cluster computing framework that can be easily integrated with Hadoop framework.
- The data manipulation operations are carried out by running Pig Scripts. In Spark, the SQL queries are run by using Spark SQL module.
- Apache Pig provides extensibility, ease of programming and optimization features and Apache Spark provides high performance and runs 100 times faster to run workloads.
- In terms of Pig architecture, the scripting can be parallelized and enables to handle large datasets whereas Spark provides batch and streaming data operations.
- In Pig, there will be built-in functions to carry out some default operations and functionalities. In Spark, SQL, streaming and complex analytics can be combined that powers a stack of libraries for SQL, core, MLib, and Streaming modules are available for different complex applications.
- Apache Pig provides Tez mode to focus more on performance and optimization flow whereas Apache Spark provides high performance in streaming and batch data processing jobs.
- Apache Pig provides Tez mode to focus more on performance and optimization flow whereas Apache Spark provides high performance in streaming and batch data processing jobs. The Tez mode can be enabled explicitly using configuration.
- Apache Pig is being used by most of the existing tech organizations to perform data manipulations, whereas Spark is recently evolving which is analytics engine for large scale.
- Apache Pig uses lazy execution technique and the pig Latin commands can be easily transformed or converted into Spark actions whereas Apache Spark has an in-built DAG scheduler, a query optimizer and a physical execution engine for fast processing of large datasets.
- Apache Pig is similar to that of Data Flow execution model in Data Stage job tools like ETL (Extract, Transform and Load), whereas Apache Spark runs everywhere and works with Hadoop and is able to access multiple data sources diversely.
Pig and Spark Comparison Table
Below are the lists of points, describe the comparisons Between Pig and Spark.
|
Basis of Comparison |
PIG | SPARK |
| Availability | Open Source Framework by Apache Open Source Projects | Open source clustering framework provided by Apache Open Source projects |
| Implementation | Provided by Hortonworks and Cloudera providers etc., | A framework used for a distributed environment. |
| Performance | Provides good performance for distributed pipelines | Spark is preferred over Pig for great performance. |
| Scalability | Limitations in scalability | Faster runtimes are expected for Spark framework. |
| Pricing | Open Source and depends on the scripts efficiency | Open Source and depends on the efficiency of algorithms implemented. |
| Speed | Faster but slower compared to Spark but productive for smaller scripts | Many times Faster than Pig and provides greater runtime capacity. |
| Query Speed | Multi Query execution capacity. | Spark SQL query performance is very high with SQL Tuning. |
| Data Integration | Fast and Flexible with different tools. | Can load data and manipulate from different external applications. |
| Data Format | All data formats are supported for data operations. | Supports complex data formats such as JSON, NoSQL, parquets etc. |
| Ease of Use | Easier to frame pig scripts like SQL queries. | Handles complex operations using frameworks in-built features. |
Conclusion
The final statement to conclude the comparison between Pig and Spark is that Spark wins in terms of ease of operations, maintenance and productivity whereas Pig lacks in terms of performance scalability and the features, integration with third-party tools and products in the case of a large volume of data sets. As both Pig and Spark projects belong to Apache Software Foundation, both Pig and Spark are open source and can be used and integrated with Hadoop environment and can be deployed for data applications based on the amount and volumes of data to be operated upon.
In most of the cases, Spark has been the best choice to consider for the large-scale business requirements by most of the clients or customers in order to handle the large-scale and sensitive data of any financial institutions or public information with more data integrity and security.
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Apart from the existing benefits Spark has its own advantages being open source project and has been evolving recently more sophistically with great clustering operational features that replace existing systems to reduce cost incurring processes and reduces the complexities and run time.
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