Several characteristics are used to distinguish between big data and traditional data. These include: The size of the data How the data is organized The architecture required to manage the data The sources from which the data derives The methods used to analyze the data Size Traditional data sets tend to be measured in gigabytes and terabytes. As a result, their size can allow for centralized storage, even on one server. Big data is distinguished not only by its size but also by its volume. Big data is usually measured in petabytes, zettabytes, or exabytes. The increasingly large size of big data sets is one of the main drivers behind the demand for more modern, high-capacity, cloud-based data storage solutions. Organization Traditional data is normally structured data that’s organized in records, files, and tables. Fields in traditional data sets are relational, so it’s possible to work out their relationship and manipulate the data accordingly. Traditional databases, such as SQL, Oracle DB, and MySQL, use a fixed schema that is static and preconfigured. Big data uses a dynamic schema. In storage, big data is raw and unstructured. When big data is accessed, the dynamic schema is applied to the raw data. Modern non-relational or NoSQL databases like Cassandra and MongoDB are ideal for unstructured data, given the way they store data in files. Architecture Traditional data is typically managed using a centralized architecture, which can be more cost-effective and secure for smaller, structured data sets. In general, a centralized system consists of one or more client nodes (e.g., computers or mobile devices) connected to a central node (e.g., a server). The central server controls the network and monitors its security. Because of its scale and complexity, it isn’t possible to manage big data centrally. It requires a distributed architecture. Distributed systems link multiple servers or computers over a network, operating as co-equal nodes. The architecture can scale horizontally (scale “out”) and will continue functioning even if an individual node fails. Distributed systems can leverage commodity hardware to reduce costs. Sources Traditional data typically derives from enterprise resource planning (ERP), customer relationship management (CRM), online transactions, and other enterprise-level data. Big data derives from a broader range of enterprise and non-enterprise-level data, which can include information scraped from social media, device and sensor data, and audiovisual data. These source types are dynamic, evolving, and growing every day. Unstructured data sources can also include text, video, image, and audio files. Leveraging this type of data isn’t possible using the columns and rows of traditional databases. Because an increasingly significant amount of data is unstructured and comes from multiple sources, big data analysis methods are required to extract value from it. Analysis Traditional data analysis occurs incrementally: An event occurs, data is generated, and the analysis of this data takes place after the event. Traditional data analysis can help businesses understand the impacts of given strategies or changes on a limited range of metrics over a specific period. Big data analysis can occur in real time. Because big data generates on a second-by-second basis, analysis can occur as data is being collected. Big data analysis offers businesses a more dynamic and holistic understanding of their needs and strategies. For example, suppose a business has invested in a training program for its staff and wants to measure its impact. Under a traditional model of data analysis, the business might set out to determine the impact of the training program on a particular area of its operations, such as sales. The business notes the sales volume before and after the training and excludes any extraneous factors. It can, in theory, see how much sales have increased as a result of the training. Under a big data model of analysis, the business can set aside questions regarding how the training program has impacted any particular aspect of its operations. Instead, by analyzing a mass of data collected in real time across the whole business, it can identify the specific areas that have been impacted, such as sales, customer service, public relations, and more.