Database management and technologies
A lot of business activities rely on database management and technologies. Yet very few of these businesses fully understand the functionality of database solutions, and even fewer know how to use the information and records contained in their databases to gain competitive advantage. Here we define common database types and forms.
What is database management?
As you will discover, database is a broad term for a tool that handles and gives structure to a dataset. A database is like a bucket, or a container, for information. The data contained in a database is typically held in a table, which has both rows and columns.
A search for the term “databases” on the G2 software review website yielded results of more than 3,700 different applications and tools. This is in part because so many different types of applications and technologies rely on databases to run and operate. Of these thousands of tools with a database component, 746 are classified specifically as database software. In this article, we explain the difference between the database types and review some popular solutions.
There is also a difference between databases and Database Management Systems (DBMS). A DBMS is an interface that enables you to access or use the data in a database. A DBMS makes it much easier for you to retrieve and manage large data sets.
Master data in databases
Master data in a database refers to the core entities that are essential to the operations of a business and are used across multiple applications and systems within an organisation. This data is critical for ensuring that various processes and transactions are conducted consistently and accurately.
Master data plays a vital role in business intelligence, analytics and decision-making processes, enabling enterprise businesses to operate efficiently and respond effectively to market changes.
Master data typically includes the following key components:
- Entities: comprising key data such as customers, products, suppliers, employees, and locations. These entities serve as the primary building blocks for business operations.
- Attributes: of each entity has associated attributes that provide additional details. For example, customer master data might include attributes such as name, address, contact information, and purchase history.
- Consistency: One of the main purposes of master data is to provide a single, authoritative source of truth. By standardising master data across different systems, organisations can ensure that all departments are using the same information, reducing discrepancies and errors.
- Lifecycle Management: to handle the creation, updates, versioning and retirement of records and assets. Managing this lifecycle effectively is crucial for maintaining accurate and relevant data.
Master data from a database is often integrated and synchronised across various systems and applications, such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), and supply chain management systems. This integration helps in maintaining accurate and consistent data across the whole organisation.
Types of databases
There are two main types of databases – relational databases (SQL) and noSQL databases. SQL in relation to databases means "Structured Query Language." It is the standard language used to deliver relational databases. With SQL, you can perform various operations such as querying data, updating records, inserting new data, and deleting data. SQL also allows users to create and manage database structures, enforce data integrity, and control access to data. Its widespread use makes it a fundamental skill for database management and manipulation.
Relational or Structured Query Language (SQL) databases
Relational databases organise and manage data in a traditional table format, where each data entry is structured in rows and columns. Each column contains data of a specific type or field, such as first name, order number, or a product logo image link, while rows establish relationships between these data points. For instance, a row can relate a first name with other records such as surname, user name, email address, and customer ID.
Enterprise businesses use relational databases to manage data from their applications, ensuring they can consistently access records like customer information, product details, social data, and financial data, including purchases, fulfilment, revenue and expenses. These systems can vary from small, desktop applications that create local databases to large enterprise-level data stores.
Nearly all relational databases use some form of SQL as their query language and follow the ACID properties for reliable transactions: atomicity, consistency, isolation and durability.
Relational databases are often preferred for their simplicity compared to NoSQL alternatives such as object-oriented databases, document databases, and graph databases. Examples include MySQL, PostgreSQL and Oracle Database
Non Structure Query (NoSQL) databases
Within the NoSQL camp, there are several different types of databases. NoSQL databases form a broad category that encompasses various database types designed for flexibility and scalability. They typically have unstructured or semi-structured data and adopt a non-relational approach to data storage, offering flexibility and scalability for handling large volumes of structured, semi-structured and unstructured data. Some of the main types aon NoSQL databases include:
Document databases, which organise related data in a document format, allow for document creation, query-based retrieval, updates, edits and deletions within those documents. Due to their unstructured nature, document stores are easily scalable by using clusters. These databases support various document model types while maintaining relevant data together in a semi-structured schema, which also allows for the storage of metadata within the documents.
Businesses looking to implement a schema-less database may consider using a document database. Document databases can store information in multiple formats, including YAML, JSON, BSON, and XML. With their diverse array of data models and approaches, NoSQL databases efficiently handle big data applications and real-time services, enabling businesses to scale and adapt to evolving needs
RDF databases which are a graph-based database that is designed to store what are known as triples. These triples represent data points in a subject-predicate-object format. By utilising this triple structure, these databases offer a unique approach to data retrieval and presentation. One of the advantages of RDF databases is their ability to systematically organise information into these triples, which can also be visualised in graph format for users.
XML databases which store data in XML-formatted documents. Users can retrieve, update, edit, delete, or return this data to the database through queries. The Extensible Markup Language (XML) is user-friendly and can be easily understood by both humans and machines, often making it simpler to work with than relational databases, which may require advanced knowledge for interpretation.
Columnar databases which organise data by columns instead of rows allowing for variable columns. Examples of columnar databases include Apache Cassandra and HBase. This storage format enhances speed and efficiency for immediate analytical queries. Primarily utilised in data warehouses, columnar databases manage and process large volumes of data from various sources, serving as a foundation for business intelligence tools.
They facilitate document creation, retrieval through queries, data updating and editing, and information deletion. Due to their unique storage format, columnar databases help reduce resource consumption when querying large data sets. As a result, businesses looking to implement a database for data warehousing and big data processing often choose columnar databases.
Key value databases which store data as associative arrays, where each value is linked to a unique key that identifies it. As a type of NoSQL database, key value stores are among the least structured.
A key value database can be compared to a simple filing cabinet where each drawer is labelled with a unique identifier (the key). Within each drawer, there is a single file or document (the value) that contains related information.
Imagine if each drawer represents a customer ID. Inside that drawer, you might find a file with that customer’s details, such as their name, address and order history. Just like you can quickly find a drawer based on its label to access the corresponding information, a key value database allows you to retrieve data by using a unique key, making it a straightforward and efficient way to store and access individual records.
Time series databases are designed for handling time-stamped data, often used for monitoring and analytics. These databases enable businesses to store data that is associated with specific time stamps. Companies may choose this type of database when they need to track data in real time or when they operate applications that generate continuous data streams. Examples of applications that produce time series data include network and application performance monitoring (APM) tools, sensor data from IoT devices, financial market data, and various security applications, among others.
Time series databases are specially optimised for storing this type of data, allowing for easy retrieval and analysis. This data format is commonly used in predictive analytics and machine learning algorithms, helping users analyse historical data to foresee future trends. Additionally, some big data processing and distribution software may offer time series storage capabilities. Examples include InfluxDB and TimescaleDB.
Graph Databases focus on relationships between data points, storing data as nodes and edges. These databases link specific data points, referred to as nodes, and establish relationships, known as edges, creating graphs that users can query. Nodes might represent entities like customers, companies or any data that a business decides to track. Edges illustrate the connections between these nodes, providing clarity on the relationships for users.
Graph databases are particularly useful for businesses that need to extract data without the hassle of organising it into distinct relationships. Large enterprises may deploy complex queries to retrieve detailed and specific insights about customer data, user interactions, or product tracking, among other applications. Database administrators can handle high data volumes while still being able to create effective models.
Graph databases are often chosen for their flexibility and capacity to visualise connections, making them suitable for various data needs. Examples include Neo4j and Amazon Neptune.
Object-oriented databases store data in the form of objects, similar to object-oriented programming. Programmers can create, modify, and store datasets as objects within these databases. To retrieve the information stored in object-oriented databases, a specific query language is required.
These databases are frequently used by engineering firms and scientific laboratories, as they can manage more complex data structures compared to relational databases, which depend on a tabular format. Some users may opt for an object-relational database, which combines features of both object-oriented and relational databases. Examples include db4o and ObjectDB.
How does Pimcore handle data sets?
Pimcore is a powerful platform that handles data sets effectively by integrating various data management functionalities, including Product Information Management (PIM), Digital Asset Management (DAM), Customer Data Management (CDM), and Content Management System (CMS). Pimcore allows users to create custom data models using a flexible data structure. You can define entities and attributes according to specific business needs, enabling you to manage complex data sets.
Pimcore also supports two main types of data: Objects (for structured data such as products) and Documents (for unstructured data like web pages). This separation helps maintain a clear repository of data. Pimcore allows users to create relationships between different data entities, enhancing data cohesion. For example, products can be linked to categories, assets, and other related data sets.
At Stimulus, we help Australian businesses get their data in order with Pimcore. As Silver Partners, we have helped enterprise companies to coordinate their online offerings and use data records to promote their products online. Check out our work or contact us for more information about how Pimcore can offer you greater cohesion and system integration.
Related questions
How do modern businesses use databases for knowledge management?
Dealing with data is a big issue for many modern businesses. The most effective modern businesses leverage the information held in their databases for knowledge management to enhance information sharing, collaboration, and overall efficiency.
Modern businesses effectively use databases as a crucial component of their knowledge management systems, enhancing collaboration, innovation, and operational efficiency. Businesses use databases to create centralised repositories where employees can store and access vital information, documents, and resources. This helps reduce silos and ensures that knowledge is easily retrievable.
How can I create cohesive records in databases?
Cohesive records and the creation of reliable data rely on good governance practices. Effective Master Data Management (MDM) practices include establishing governance procedures to define how master data is created, updated, and maintained. This ensures data quality, integrity, and compliance with local, national or international regulatory requirements.
Making sure that each column in your database has a consistent data type, like integers, strings or dates can reduce errors when querying the data. You should also use clear and consistent naming conventions for tables, columns and other database objects to enhance readability and maintainability within your records.