Industrial data architecture is a set of technologies, processes, and standards that organize and manage all types of variables generated in manufacturing environments. Through a well-designed architecture, it is possible to transform raw data from equipment and sensors into actionable information.
This process adds value to operations by optimizing processes and improving product quality. For an architecture to be robust, scalable, and tailored to the specific needs of each type of manufacturing, the following points must be considered:
- Data sources;
- Volume and velocity (big data);
- Data quality;
- Scalability and flexibility;
- Data security and governance;
- System integration;
- Storage;
- Analytics and visualization tools;
- Cost and maintenance.
Therefore, there is a strategic logic that can be followed for this implementation to occur in a cyclical and gradual manner:
- Planning and discovery: defining the project foundation;
- Data collection and integration: building pipelines to bring data from their sources to a centralized location;
- Processing and storage: structuring data to make them useful;
- Analysis and consumption: extracting value from the data;
- Governance and evolution: ensuring the longevity and security of the architecture.
For example, in a medium-sized animal processing facility, where 5 to 10 tons are produced per day, each stage of the process generates a large volume of data. In this context, a well-structured data architecture is essential for ensuring operational efficiency, profitability, food safety, and compliance with regulatory requirements.
Given the data complexity at each stage, this example highlights the need for a robust data architecture. However, the real challenge is managing the high volume and velocity of these data streams. To address this challenge, Edge Computing and Cloud Computing applications can serve as complementary solutions.
Edge and Cloud: Architecture Strategies for Industry
For an industrial data architecture to be truly effective and fulfill its role of transforming raw data into value, it is essential that it be supported by technologies that enable efficient processing of this information.
Edge Computing and Cloud Computing are two data processing approaches that work in a complementary way to optimize operations. The choice between one or the other (or a combination of both) depends directly on the type of application, the required response time, and the volume of data.
By bringing data processing closer to the source where it is generated, Edge Computing applications, instead of relying on a central server, enable real-time responses. For this reason, this technology is used for predictive maintenance, quality control on production lines, and automation.
In the context of meat post-processing, for example, maintaining ambient temperature within a defined standard ensures food quality. With real-time monitoring, any variation is quickly detected, allowing immediate action before the deviation causes damage to the product.
Cloud Computing applications, in turn, can be used in scenarios that involve complex analytics, long-term data storage, and activities that do not require real-time responses. Among its most common uses are historical data analysis, focusing on process optimization, scalability, and flexibility in resource allocation.
In the case of temperature monitoring during meat post-processing, the historical data helps identify trends that may compromise quality. With this information, it is possible to make assertive decisions, such as disqualifying an entire batch, ensuring food safety and compliance with regulatory requirements.
The combination of these applications forms a hybrid cloud architecture, which refers to an integrated environment that combines on-premises infrastructure, private clouds, public clouds, and Edge Computing solutions. This association creates a unified and adaptive IT infrastructure.
Designing Data: Data Lake and Data Warehouse for Industry
Once processing and infrastructure are in place, the next crucial step in unlocking the value of data is its storage and strategic analysis. For this, Data Lake and Data Warehouse approaches are fundamental.
The physical architecture of a Data Lake combines scalable storage with distributed processing engines, making it ideal for storing raw and unstructured data from multiple sources, as well as incorporating components such as ingestion, cataloging, governance, and processing.
Ingestion is managed through pipelines that support both continuous streams and batch loads, providing flexibility for capturing both real-time and historical data. A cataloging system organizes metadata, facilitating data discovery, traceability, and the enforcement of governance policies.
Processing is performed by engines compatible with multiple analytical paradigms, such as SQL queries, statistical analysis, machine learning, and distributed processing. In the meat processing industry, this includes:
- Machine sensor data: high-frequency data streams such as machine temperature and pressure readings;
- Rejected batch data: detailed information about production batches rejected by quality control;
- Packaging and shipping data: raw data including material type, weight, sealing status, and storage temperature.
In contrast, a Data Warehouse is designed for structured, cleansed data, with infrastructure optimized for reliable and fast analytics. Data undergoes ETL (Extract, Transform, Load) processes, being cleaned and standardized before storage in relational schemas.
Ingestion is driven by business rules, with an emphasis on consistency. The catalog is integrated with the data model, and processing is focused on structured analytics, aggregations, and the generation of key performance indicators, enabling fast and reliable queries for exploratory analysis:
- Production history: processed information such as deboned material weight, number of packages produced per hour, and quantity of rejected batches;
- Efficiency analysis: calculations of the efficiency of each production line;
- Quality control: standardized quality control records, such as package weight and final product pH.
The combined use of these types of repositories forms an effective data management system, providing manufacturing industries with the ability to define both real-time and long-term data-driven strategies, as well as optimize the efficiency and quality of their processes and products.
Discover Data Circle Space, a hub for those exploring innovative solutions at the intersection of data, programming, and industrial processes.
Learn more about ST-One.