Optimizing your data requires a reliable way for you to maintain, organize, update, and access volumes of stored information through a database.
However, not all databases are made equal.
Data volume, schema, and type can vary, determining whether you need to use a traditional or a Big Data database.
For instance, if you’re dealing with massive volumes of information, you would need big data analytics tools and databases to handle your data properly and extract your desired insights.
This guide looks into the five general key differences between big data and traditional databases. But first…
What is a traditional database and a Big Data database?
A data structure is a storage format to manage data efficiently. This is essentially what a traditional database is — a data structure that lets you store and work with your information.
Traditional databases allow you to fetch or request data, usually through Structured Query Language (SQL).
Today, most traditional database designs have shifted to a relational model with 60.5% of databases in SQL-based Relational Database Management Systems (RDBMS) that allow for end-to-end data optimization process.
On the other hand, from a 30,000 feet perspective, a big data database is where you store big data.
Big data database features include handling data requirements that traditional RDBMS can’t manage in speed, variability, and volume.
Most big data systems are designed as NoSQL databases, which means they store and retrieve data without requiring a fixed schema, making them more scalable and flexible and offering increased performance.
Due to big data databases’ capabilities, they can be a more cost-efficient option since they can easily meet the increasing demands of other big data applications and tools you might use.
Big Data database versus traditional database
While big data and traditional databases have many differences, we’ll focus on five general characteristics and factors and how they differ in each of these aspects.
Traditional databases are designed based on a fixed schema, which is static in nature. This means they can only work with limited structured data types, usually those that fit seamlessly into tables or relational databases.
This can be limiting since most data that you will work with is unstructured.
A wide variety of unstructured data, such as images, videos, geolocation data, documents, web content housed in your Content Management Software (CMS), and other types, need more advanced ways for storing and processing the information properly.
Most traditional databases cannot these handle alone (especially high volume unstructured data).
In contrast, big data databases work using a dynamic schema, including both structured and unstructured data. The schema is only applied when the data (which is stored in raw form) is accessed.
In big data analytics, data sets coming from various sources are attached. Functions are then conducted, including information cleansing, storing, indexing, distributing, searching, visualizing, accessing, analyzing, and transforming.
2. Data architecture and volume
Traditional databases work better when the data volume, which is the amount of data the database system stores and processes, is low (ideally with the maximum capacity in gigabytes).
Data size bigger than gigabytes such as terabytes and petabytes could lead to the database system failing to provide results efficiently or even accurately.
On the other hand, big data databases are designed to handle massive data volumes — from customer engagement to shopping behavior information.
For instance, Hadoop, while not technically a database but an open-source software framework that allows storing data and running applications on commodity hardware clusters used in big data, offers large storage capacity for any type of data.
It also offers massive processing power and can manage essentially limitless simultaneous tasks, allowing for more seamless data handling.
The architecture for traditional and big data database systems also varies.
Most traditional databases have Atomicity, Consistency, Isolation, and Durability (ACID), responsible for ensuring and maintaining data integrity. These also ensure they are maintained accurately during transactions that occur within the database system.
A big data database system, such as the Hadoop example, consists of a few core components, including a distributed file system for processing large-size data. It also includes a Hadoop yarn used for computing and managing multiple computer clusters.
3. Data variety and throughput
Generally, the data variety is the means and process through which data is processed within the database system. It can be semi-structured, structured, and unstructured.
Database systems designed for big data can store and process all types of data, such as information from modern customer service software, regardless of the processing method it went through.
However, a traditional database can only manage limited types of unstructured data.
A traditional and big data database can also vary in throughput or the total data volume processed within a specific period to ensure maximum output.
Traditional database systems usually can’t reach a high throughput rate due to their data variety and volume processing limitations, whereas big data databases can quickly achieve this.
4. Scalability of analytics infrastructure
If your data workloads are predictable and constant, the better option would be a traditional database.
This could also work great for low dataset volumes for small companies who want to use a database system that can be used with marketing analytics software and related tools for digital marketing.
However, to address increasing data demands as your information and company grow, leveraging a big data database with a scalable infrastructure is the better choice.
Some big databases include features that spin virtual servers up or down in minutes, which can better accommodate irregular workloads, allowing for flexible scalability.
5. Data analysis speed
Big data databases and software frameworks designed to handle big data can process large distributed information that addresses each file within the database, which can take time.
If your task doesn’t require fast performance, a big database or software framework that can manage big data is ideal.
Tasks such as scanning historical data, running end-of-day (EoD) reports for daily transaction reviews, including purchasing articles online for your content marketing efforts, and other related jobs, are better off with big data databases.
However, if you rely on time-sensitive analysis, a traditional database is a better option. This can be one of the brilliant analytics tips for tracking social media posts where real-time analysis is crucial.
Traditional databases are well equipped to analyze smaller data sets in near-real or real-time, which is ideal if you prefer faster data processing and analyses.
60.48% use an SQL database while 39.52% use a NoSQL database, but in the end, the right database for you should be based on your needs.
Both big data and traditional database systems have their pros and cons, depending on your requirements, so weigh carefully and choose the best fitting one for you.
While this post is by no means a comprehensive guide, it does give you a top-level view of the general differences between big data and traditional databases.