Databases in Production It: Which Database Is the Right One?
Databases play an important role in modern production IT. They are central storage locations for a constantly growing amount of data from various sources. This data helps to monitor, control and optimize production processes. In this blog, we deep dive into the different types of databases in manufacturing IT, highlight their advantages and disadvantages, and give you the critical criteria for choosing the right solution.
Databases: Library 4.0 in modern production
In today's manufacturing landscape, data sources are more diverse than ever. They provide you with valuable information about your production processes. Employees in production use them to evaluate the behavior of processes, control machines and ensure product quality. That is why databases are like libraries in modern production, because they are a central medium for storing and archiving production data and making it readily available.
Data storage: One of the main tasks of databases in production is reliable data storage. This includes archiving data over long periods of time, temporarily storing current production data, and securing data through backups. Long-term archiving allows companies to analyze historical production data and gain valuable insights for future improvements. At the same time, temporary storage ensures that current production data from applications such as MES, ERP or monitoring software can be retrieved and stored quickly and efficiently. Backups also protect against data loss and ensure production continuity.
Data consistency and centralization: A major advantage of databases is their ability to ensure that data is consistent and that information is centralized. By combining data from various sources, all relevant information is up-to-date and available to you from a central location. This also makes subsequent processing and analysis easier.
Data provision: Databases are not only used to store data, but also to provide data for various purposes. For example, they provide the data required for calculating key performance indicators and presenting them in charts. At the same time, they provide large amounts of raw data that can be used for detailed analyses and optimization
Types of databases and their uses
Different types of databases are used depending on the application. Various technical architectures have been developed based on the requirements for data types, data volumes and the frequency of read and write accesses. The main types of databases include:
- Relational databases that are based on a table-structured model that focuses on the relationships between data. They are widely used and use the SQL database language for queries and changes.
- Object-oriented databases store data in the form of objects, similar to object-oriented programming. They support object classes and the inheritance of class concepts and methods. As a result, they are ideal for applications that need to manage complex data structures and relationships.
- Hierarchical databases organize data in a tree-like structure, with each element having a specific relationship with other elements. This makes them suitable for applications where data needs to be stored in a natural hierarchy.
- Network-like databases are similar to hierarchical databases, but offer even more flexibility in the relationships between data. They are particularly useful in applications that require complex networks of data relationships.
Relational databases are field proven
Relational databases are widely used as database management systems in production IT. The focus is on the relationships between data, which are defined by primary and foreign keys in various tables. SQL standardizes and simplifies their use and administration.
- Stability and structure: Table-based structure enables clear organization and easy management of data.
- High data integrity: Storing data in defined schemes ensures consistency and reliability.
- Easy maintenance: The clear structure makes it easier to maintain and update existing data sets.
- SQL based: Because SQL is widely used, it is easier to find qualified professionals.
- Low flexibility: Fixed structure limits adaptation to new or changing requirements.
- Limited scalability: Less suitable for applications that require rapid growth or storage of large amounts of unstructured data.
- Limited horizontal scalability: Usually operated on a single server and limited by hardware capacity.
A practical example from manufacturing IT is using relational databases to manage production jobs. In such environments, tables can be set up for customer orders, machine parameters, job schedules, and inventory controls. These tables are linked together by relationships that provide a comprehensive view of the production process. If, for example, a production job is changed, this change can be automatically passed on to the appropriate machine parameters and job schedule to ensure that everything stays in sync.
Another application is quality control. Relational databases store both product data and process data in this scenario. By integrating these tables, quality managers can quickly analyze which machines or production processes are causing problems so that they can implement appropriate measures effectively.
The most well-known relational databases include Oracle DB, Microsoft SQL Server (MS SQL), PostgreSQL, and MySQL. Oracle DB is often used in large, complex production environments that require high availability and robust security features. MS SQL Server is particularly popular in companies that have a Microsoft-centric IT infrastructure. PostgreSQL and MySQL are popular open-source alternatives that have proven to be reliable and flexible in many production applications.
Non-relational databases (NoSQL) are more versatile
Non-relational databases, also known as NoSQL databases, differ from the traditional relational model by deliberately avoiding joins to provide better horizontal scalability. They are often referred to as structured data storage and are particularly suitable for applications that need to manage large amounts of data and different types of data.
A practical example is the use of graph databases such as Neo4J in maintenance planning and optimization. These databases are suitable for managing complex relationship structures, such as those that occur when tracking machine components and their maintenance history. Graph databases make it possible to analyze relationships between various machine components, maintenance events and malfunctions in order to develop preventive maintenance strategies and minimize downtime as a result.
- Big data management: Optimized for various types of data and structures, ideal for unstructured or semi-structured data.
- Flexibility: High adaptability to different data requirements without rigid schemes.
- Scalability: Supports horizontal scaling, ideal for big data applications and cloud-based environments.
- Distributed data storage: Efficient processing of large volumes of data by distribution across multiple servers.
- Difficult data relationship maintenance: Data relationships are more complex to handle compared to relational databases, which can affect consistency.
- Consistency issues: Challenges in ensuring data integrity, particularly with strict consistency requirements.
- Limited transaction support: No full transaction features, which makes them less suitable for applications that require ACID compliance.
Types of NoSQL Databases
These save simple key-value pairs and are therefore ideal for applications that require quick read and write access to high volumes of data. Examples include DynamoDB and Redis. In production IT, key-value databases can be used to manage user profiles and session data to ensure fast access times.
Data is organized in column families instead of rows, which enables efficient storage and retrieval of large amounts of data. Examples include Scylla, HBase, and Cassandra. In production environments, they are excellent for storing telemetry data from machines, because they can process large amounts of sensor information quickly and efficiently.
Stores data in files that can be in JSON, BSON or XML format. These are particularly suitable for applications that require flexible and dynamic data schemes. Examples include Couchbase and MongoDB. In production IT scenarios, they are often used to manage product catalogs and customer information, which can have different and unpredictable structures.
Specializes in indexing and searching large amounts of text and are suitable for applications that require quick full-text search and data analysis. Examples include Solr, Splunk, and Elasticsearch. In production IT, these types of search engines are used to analyze log data and monitor system performance in order to be able to react quickly to potential problems.
Store data in the form of graphs, which makes them particularly useful for applications that need to map complex relationship structures. Examples include Datastrax and Neo4J. In production environments, they can be used to analyze supply chain relationships and optimize production processes. They help to visualize and analyze the connections between different production steps and suppliers in order to understand and improve processes.
Integrated databases are for large software solutions
Integrated databases are an essential part of modern software solutions in production IT. In contrast to standalone database solutions, which can be flexibly configured, they are permanently connected to specific applications such as manufacturing execution systems (MES), line controllers (LC), and monitoring systems (MDA/PDA). These systems are often used in production environments where production efficiency and quality need to be improved, processes monitored and optimized, and comprehensive reports and analyses compiled.
A special feature of integrated databases is that they often have predefined data structures. These data structures are specifically tailored to the requirements of the respective application and support the functions and processes within the system. For example, the data structures of an MES are often specifically designed to manage production jobs, machine data, material flows, and quality control data in order to optimally support processes.
Finding and maintaining the right database with Kontron AIS
Today, databases are indispensable if production data is to be efficiently managed and analyzed. They store, organize, and supply information that you need to optimize your production processes. Finding the right database for your application isn't easy. Because we have more than 30 years of experience, we can help you find the right database solution to match your requirements, and we can also take care of implementation and maintenance. That is how we can help you to make your production more effective. Our FabEagle® system solutions for production monitoring and control also offer you a quick and reliable start to digitalizing your production processes.
Looking for the right database solution for your production digitization? I would be happy to help you!
