Information is king in business. However, storing and using that information effectively can be a challenge. This is where two powerful tools come into play: relational databases and big data processing. Understanding how big data is processed using relational databases is crucial for organisations aiming to leverage their data for strategic advantage.

This post will dive into both concepts, explaining what they are, how they differ, and when to use each type of technology. Whether you're a data enthusiast or just starting out, understanding these fundamental technologies will equip you to handle your data needs more effectively.

What are Relational Databases?

Relational databases are database management systems that organise data into structured tables. These tables are defined by rows, each representing a unique record, and columns, which represent the attributes of those records. The power of relational databases lies in their ability to efficiently manage data through established relationships between these tables, which can be queried and manipulated using a specialised language, typically SQL (Structured Query Language).

This system of organisation ensures that data can be retrieved, inserted, updated, and deleted in a controlled and systematic manner, promoting data integrity and reducing redundancy through normalisation—a process that organises data to minimise duplication. Relational databases are widely used in various applications, from financial records to customer data, because of their robustness, reliability, and strong consistency models. They remain a fundamental tool for data management in both small and large enterprise environments.

A basic diagram that visually illustrates the concept of a relational database, showing several tables and the relationships between them through connecting lines

What is Big Data Processing?

Big data processing refers to the methods and technologies used to handle vast volumes of data that traditional data processing software can't manage effectively. This involves analysing, systematically extracting information, and dealing with data sets so large and complex that they require advanced and unique data storage, management, analysis, and visualisation technologies.

Essentially, while big data can be processed using relational databases to some extent, the scalability and flexibility needed often surpass what traditional databases can handle. Instead, technologies like Apache Hadoop and Apache Spark are employed. These platforms distribute data processing tasks across many systems for more efficient handling of large data volumes, providing fault tolerance and rapid processing capabilities. This ability to process big data enables organisations to make more informed decisions and uncover hidden patterns, correlations, and insights that drive innovation and operational efficiencies.

A basic diagram that visually illustrates the concept of big data processing, depicting a distributed computing system with multiple nodes and data flows, representing parallel processing across different machines.

Comparing Operations and Applications

The main difference between how big data is processed using relational databases and big data technologies lies in their scalability and data structure flexibility. Relational databases are highly structured and they operate on a structured query language (SQL). As a result, they are highly effective for managing structured data with complex relationships. They excel in environments where transactional integrity, such as banking and inventory systems, is critical. However, their scalability can be limited when faced with very large or rapidly growing data sets.

On the other hand, big data technologies are designed to handle unstructured data, such as social media feeds, video, or sensor data, making them more adaptable to diverse data types. These technologies use distributed computing to process data across multiple machines, which significantly enhances their ability to scale and manage unstructured or semi-structured data such as logs, social media feeds, and sensor data. As a result, applications of big data technologies are often found in contexts requiring real-time analytics and decision-making, such as traffic monitoring and predictive maintenance systems. 

Let’s take a look at how these technologies might work in a business environment. Specifically, we are going to look at which technology is best for communicators, sellers and analysts.

For Communicators

Communicators often deal with engaging customers through various channels and managing large volumes of customer interaction data. For such roles, big data technologies are generally more suitable due to their ability to handle unstructured data like customer feedback from social media, emails, and customer service interactions. Tools like Hadoop or real-time data processing frameworks such as Apache Kafka can enable communicators to quickly analyse and respond to customer sentiments and trends.

For Sellers

Sellers require access to detailed customer data, sales performance metrics, and market trends to effectively drive sales strategies and enhance customer relationships. 

Here, a hybrid approach may be beneficial:

• Relational databases are ideal for managing structured data such as sales transactions, customer profiles, and inventory data where transactional integrity and complex queries are critical.
• Big data technologies can be applied to analyse broader trends from unstructured data sources like market sentiment analysis on social media or consumer behaviour patterns from online interactions.

For Analysts

Analysts need to derive insights from vast and varied data sets to inform strategic decisions. Big data technologies are particularly advantageous for analysts because they can handle the volume, velocity, and variety of data:

• Tools such as Apache Spark allow analysts to perform complex data processing and machine learning on large datasets more efficiently than traditional relational databases.
• For structured data analysis involving complex queries over smaller datasets, SQL-based relational databases may still be preferable.

Relational Databases and Big Data Processing: What Changes Can We Expect to See?

So what changes can we expect to see over the coming years?

Put simply, the future of relational databases and big data processing is steering towards a more integrated and intelligent approach, with advancements in AI and machine learning leading the charge. Relational databases are expected to evolve with capabilities that enhance performance and scalability, integrating more seamlessly with big data platforms to manage vast and varied datasets more effectively.

Big data processing, in turn, is likely to see greater adoption of real-time analytics and automated decision-making processes, facilitated by AI-driven tools. These advancements will enable organisations to react instantly to market changes and consumer behaviours, transforming data into actionable insights at unprecedented speeds.

As relational databases and big data processing continue to evolve, the role of data visualisation, particularly through services like those offered by Bestiario, will become increasingly crucial. As these databases adapt to handle more complex and larger datasets, and as big data environments incorporate real-time analytics, the need for effective visualisation tools will become paramount.

Essentially, data visualisation acts as the bridge between the raw, complex data stored in relational databases and big data systems, and the actionable insights businesses need. By integrating these visualisation tools directly with databases and big data platforms, organisations can pull live data, apply analytical models, and instantly generate visual reports that make trends, patterns, and anomalies easily understandable. This integration facilitates quicker, more informed decision-making across all levels of an organisation, enhancing strategic planning and operational efficiency.