Structured Process Language (SPL): Power and Precision for Data Transformation

Structured Process Language (SPL) is a powerful language designed specifically for data manipulation and processing. Unlike traditional languages like SQL, SPL focuses on structured data, offering a unique approach with several advantages. This article explores SPL, its benefits compared to cloud platforms like GCP, AWS, and Azure, its limitations, cost considerations, and concludes with an example implementation.

1. What is SPL?

SPL excels at handling data organized in a specific format, like tables or records. It offers a distinct approach compared to SQL, with a strong theoretical foundation based on "discrete datasets." This concept allows for efficient operations on structured data, ensuring precise and traceable processing.

Key Features of SPL:

• Emphasis on Discreteness: Precise data handling through focus on discrete data units.
• Order-Preserving Operations: Ensures the order of data is maintained during processing.
• Comprehensive Set-Oriented Operations: Enables efficient manipulation of entire datasets.
• Data Object Referencing: Provides the ability to reference objects within the data itself.
• Stepwise Processing: Encourages clear and traceable data manipulation through well-defined steps.

2. SPL vs. Cloud Data Platforms (GCP, AWS, Azure)

While cloud platforms like GCP, AWS, and Azure offer data processing services like Dataflow, Glue, and Data Factory, SPL provides several advantages:

• Simpler Syntax: SPL boasts a more user-friendly syntax, making complex data manipulation tasks easier to express and understand.
• Performance: In specific scenarios, SPL can achieve faster processing speeds compared to SQL-based solutions offered by cloud platforms.
• Focus on Data Processing: SPL is dedicated solely to data processing, leading to a potentially more streamlined and efficient environment for data manipulation tasks.

However, it's important to note that cloud platforms provide a broader range of services beyond just data processing. They offer functionalities like data warehousing, machine learning, and serverless computing, which SPL lacks.

3. Limitations of SPL

Here are some limitations to consider when evaluating SPL:

• Limited Adoption: Compared to SQL, SPL has a smaller user base, potentially leading to fewer resources and community support.
• Vendor-Specific: While SPL implementations exist from various vendors, some may not be interoperable, requiring consideration during platform selection.
• Learning Curve: Those familiar with SQL may need to invest time in learning the specifics of SPL.

4. Cost Comparison: SPL vs. Cloud Dataflow, Glue and Data Factory

5. Example Implementation with Source Code

Consider a scenario where you want to filter a customer data table based on their location and purchase history. Here's an example SPL code achieving this:

/* Source table containing customer data */
dataset customer_data {
  id: integer;
  name: string;
  location: string;
  purchase_amount: decimal;
  purchase_date: date;
};

/* Define a filter for location */
filter US_customers = customer_data.location == "US";

/* Select customers from the US who spent more than $100 in the last month */
dataset high_spending_US_customers = select * from US_customers where purchase_amount > 100 and purchase_date >= dateadd(month, -1, current_date);

/* Print the results */
output high_spending_US_customers;        

By understanding SPL's strengths and limitations, you can evaluate if it aligns with your specific data processing needs. Its focus on structured data, clear syntax, and efficient processing make it a valuable option for various data transformation scenarios.