Effective data management has become crucial for organizational competitiveness and innovation. The adoption of sophisticated strategies for data management, such as layered data architecture, known as "medallion" architecture, and Robotic Process Automation (RPA), allows not only the efficient storage and processing of large volumes of data, but also the transformation of this data into strategic intelligence to support business decisions.

Layered Data Architecture: The Medallion Model

The tiered data architecture is structured into three main levels: Bronze, Silver and Gold. This model provides a solid foundation for data processing, ensuring an efficient and scalable approach throughout the data lifecycle.

Bronze Tier: Gross and Consolidated Storage

The Bronze tier acts as the foundation of the Data Lake, where raw data from various sources is stored without transformation. It uses a dedicated PostgreSQL database (for example) to guarantee the integrity of the original data, preserving it exactly as it was collected. The emphasis at this stage is centralization and data integrity, providing a reliable basis for subsequent processing.

Silver Layer: Transformation and Standardization

In the Silver tier, data stored in the Bronze tier is processed and transformed. This stage includes data standardization, type adjustment, and other transformations necessary to ensure data quality and uniformity. For example, the PySpark library is used to perform cleaning operations, removing special characters and type corrections, preparing the data for more advanced analysis.

Gold Tier: Business Processing and Analysis Readiness

At the Gold tier, data is refined and prepared for analytical use. Specific corrections and enhancements are applied according to business needs, resulting in a data set ready for generating strategic insights. ID mapping operations and other customizations are performed using, for example, Spark with Python, ensuring that the data is aligned with the defined nomenclatures and requirements.

Robotic Process Automation (RPA): Optimizing Data Flow

Robotic Process Automation (RPA) is incorporated to improve efficiency and accuracy in data processing. RPA automates repetitive tasks and data collection and movement processes between layers of the medallion architecture, including automated data extraction, transformation, and loading (ETL). This reduces the need for manual intervention and speeds up data flow.

Integration with Layered Architecture

RPA integrates cohesively with the layered data architecture. Automated scripts, integrated with Apache Airflow, manage the sequential execution of tasks and the movement of data between the Bronze, Silver and Gold tiers. Automation ensures that the data pipeline runs efficiently, with the creation of Directed Acyclic Graphs (DAGs) in Airflow that define task dependencies and execution flows.

Comparison Metrics: RPA vs. Real-Time Processing

Choosing between different data processing methods, such as RPA and real-time processing (streaming), is a critical decision that directly impacts the efficiency and effectiveness of data projects. Comparison between RPA and real-time processing can be made based on several metrics:

Latency

Latency measures the time required for the system to process data after an event has entered. In RPA systems, latency can be lower for repetitive, scheduled tasks, while real-time processing is ideal for data that requires an immediate response.

Transfer Fee

Transfer rate refers to the amount of datathose processed per unit of time. RPA is efficient for processing large volumes of data in batches, while real-time processing is more suitable for scenarios that demand high speed of continuous processing.

Hardware Requirements

Using RPA can require fewer hardware resources compared to real-time processing, which often requires robust infrastructure to handle continuous streams of data.

Transforming Data into Strategic Intelligence

The combination of medallion architecture with RPA allows the transformation of raw data into strategic intelligence in an efficient and scalable way. Integration between the data storage and processing layers, combined with process automation, facilitates the generation of valuable insights that support informed decisions and drive innovation. The dashboards and reports developed from data processed in the Gold tier exemplify how these technologies promote operational excellence and deliver real value to organizations.