Large datasets, often associated with big data, are a principal feature of modern enterprise retail systems, due to the volume and complexity of the data involved.
Retail systems commonly generate huge amounts of data collected from various sources, such as invoices, card payments, loyalty transactions, or fiscalization.
Furthermore, retail data is generated and updated in real-time, and comes in various formats, both structured (ie, as records) and partially structured (for example, JSON files or logs). Also, data may often involve complex relationships, with a varying degree of granularity, such as the fine-grained data of an individual sales transaction and aggregated data like a daily revenue summary.
Managing large datasets was therefore one of the principal tasks for our implementation of the POINTER Store Analytics software solution. In particular, this involved developing a strategy for database management and maintenance that employs the best practices and specific features of the selected database management system (PostgreSQL), and enables a sustainable integration of the database features into the context of the software application.
Here are some examples of applied specific features:
- The concept of data partitioning allowed for a time-based data table partitioning scheme that followed the common legal requirements for archiving of financial or accounting data that dictate how long records must be retained, and the varying retention periods by document type.
- Adherence to the ACID principles (Atomicity, Consistency, Isolation, Durability) ensured data integrity and consistency in data processing.
- Native support for JSON or XML data types made PostgreSQL an excellent choice for handling semi-structured data within a large dataset, coming from diverse retail applications using these formats for data interchange.
In summary, the specific capabilities provided by PostgreSQL for management and processing of large datasets presented a robust and reliable platform on which to base the implementation of a business-oriented retail-specific software solution.
To learn more about POINTER Store Analytics, visit https://pointer.hr/software/pointer-store-analytics
To learn more about PostgreSQL, visit https://www.postgresql.org
Large datasets, often associated with big data, are a principal feature of modern enterprise retail systems, due to the volume and complexity of the data involved.
Retail systems commonly generate huge amounts of data collected from various sources, such as invoices, card payments, loyalty transactions, or fiscalization.
Furthermore, retail data is generated and updated in real-time, and comes in various formats, both structured (ie, as records) and partially structured (for example, JSON files or logs). Also, data may often involve complex relationships, with a varying degree of granularity, such as the fine-grained data of an individual sales transaction and aggregated data like a daily revenue summary.
Managing large datasets was therefore one of the principal tasks for our implementation of the POINTER Store Analytics software solution. In particular, this involved developing a strategy for database management and maintenance that employs the best practices and specific features of the selected database management system (PostgreSQL), and enables a sustainable integration of the database features into the context of the software application.
Here are some examples of applied specific features:
- The concept of data partitioning allowed for a time-based data table partitioning scheme that followed the common legal requirements for archiving of financial or accounting data that dictate how long records must be retained, and the varying retention periods by document type.
- Adherence to the ACID principles (Atomicity, Consistency, Isolation, Durability) ensured data integrity and consistency in data processing.
- Native support for JSON or XML data types made PostgreSQL an excellent choice for handling semi-structured data within a large dataset, coming from diverse retail applications using these formats for data interchange.
In summary, the specific capabilities provided by PostgreSQL for management and processing of large datasets presented a robust and reliable platform on which to base the implementation of a business-oriented retail-specific software solution.
To learn more about POINTER Store Analytics, visit https://pointer.hr/software/pointer-store-analytics
To learn more about PostgreSQL, visit https://www.postgresql.org
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