What is Big Data? Big Data seems to be a big deal, will systems using traditional database be dead? Should I choose Big Data or traditional relational database? Big Data has certainly raised a lot of interests in recent days. Big Data brings unlimited business opportunities to enterprises, including more effective in-depth insights, allowing owners to better understand customer behavior, predict market activities more accurately, and improve overall performance.
People and companies produce more and more data over the years. The IDC report shows that in 2010 alone, 1.2 ZB (equivalent to 1.2 trillion GB) of new data was produced worldwide. By 2025, this number will climb to 175 ZB (equivalent to 175 trillion GB), or even more! So as the business uses this booming resource for predictive analysis and data exploration, the Big Data market will continue to grow.
So, what are the key differences between Big Data and the traditional data? What potential impact does Big Data have on current data storage devices, processing procedures, and analysis techniques?
Traditional data is a structured data which has been widely used and maintained by all types of businesses for the past 30 to 40 years. In traditional database system, such as relational databases and data warehouses, a centralized database architecture is used to store and maintain the data in a fixed format or fields in a file. Structured Query Language (SQL) is used to manage and access the data.
Today, traditional data still accounts for most of the data volume. Companies use traditional data to track sales, manage customer relationships, or work processes. Traditional data is usually easy to use and can be managed using traditional data processing software.
We can consider Big Data an upper version of traditional data. Big Data deal with large or complex data sets which is difficult to manage in traditional data processing software. Big Data used to be defined by the “3Vs”, but now there are five main characteristics, which are often referred to as the “5Vs”:
Volume: The name Big Data itself is related to a size which is enormous. To determine the value of data, size of data plays a very crucial role. Whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. “Volume” is one characteristic which needs to be considered while dealing with Big Data solutions.
Velocity: The term “velocity” refers to the speed of generation of data. How fast the data flows in from sources like business processes, application logs, networks, social media sites, sensors, Mobile devices, etc. and how fast data can be processed to meet the demands. Big Data is generated at a rapid speed and often requires immediate processing.
Variety: Big Data datasets usually contain structured, semi-structured, and unstructured data. Spreadsheets and databases used to be the only sources of data considered by most of the applications in the old days. Nowadays data in the form of emails, photos, videos, smart devices, PDFs, audio, etc. are also being considered in the analysis applications.
Variability: This refers to inconsistencies and uncertainty in data, that is data which is available can sometimes get messy and quality and accuracy are difficult to control.
Value: Data itself is of no use or importance, it needs to be converted into something valuable to extract Information. So, you can say that “Value” is the most important V of all the 5V’s.
The differences include, but not limited to:
Data Size: Traditional data usually uses measurement units such as GB or TB. Therefore, this amount of usage can be centrally stored in a central device, and sometimes only one server is needed. Big Data usually uses measurement units such as PB, ZB, or EB. The ever-increasing massive datasets of Big Data can be said to be the main driving force behind the scenes, leading to modern, large-capacity, cloud-based storage solutions.
How the data is organized: Traditional data are generally structured data. The fields of traditional datasets are arranged in the form of records, files, tables, etc., so that they can be used to find out the relationship between data and manipulate the data content accordingly. Traditional databases such as SQL, Oracle DB, and MySQL all use pre-configured and fixed models. Big Data is a dynamic model. Big Data storage devices are both primitive and unstructured. When acquiring Big Data, these original data will be applied in a dynamic mode. Considering the way that modern non-relational or NoSQL databases such as Cassandra and MongoDB store data in the form of files, they are all suitable for unstructured data.
Infrastructure required to manage data: Traditional data is usually managed in a centralized structure. For a smaller amount of structured data sets, this is more cost-effective and safer. A centralized system consists of more than one client node (such as a computer or mobile device) connected to a central node (such as a server). The central server controls the network and monitors security. Due to the large scale and complexity, it is impossible to manage Big Data from the center. It requires a decentralized infrastructure. The distributed system connects multiple servers or computers through the network, and the operation is the same as other peer nodes. The infrastructure can be scaled horizontally, and it can continue to operate normally even if individual nodes fail. Distributed systems can use off-the-shelf hardware to reduce costs.
Source: Traditional data usually comes from ERP transaction data, CRM transaction data, financial data, online transactions, and other enterprise-level data. Big Data captures a wide range of corporate and non-corporate data, including emails, social media, device and sensor data, and audiovisual data. The types of these data sources are changeable, and they evolve and grow every day. Unstructured data sources include text, images, pictures, and audio files. The tables of traditional databases simply cannot use these data. Because there are more and more unstructured data and more and more diverse data sources, Big Data analysis is indispensable if you want to make good use of these data.
Way of analyzing data: Traditional data analysis methods are slow and gradual: data analysis is usually performed after events occurred and data are generated. Traditional data analysis is accurate and can help companies understand the impact of known strategies or changes within a specific period with limited variables. Big Data analysis is instant and fast. Big Data generates data every second, so it can be analyzed now that the data is collected. Big Data is more about speed, so the analysis is NOT accurate. Big Data analysis allows companies to have a more dynamic and comprehensive grasp of supply, demand and strategies.
Big Data and traditional data have different purposes but are related. Although Big Data seems to have more potential benefits, it is not applicable to all situations and is not necessary.
Characteristics of Big Data:
Characteristics of traditional materials:
The rise of Big Data does not mean that traditional data will be eliminated. Choose between two solely based on the needs of a company. As more and more companies produce large amounts of unstructured datasets, what we need are tools that function appropriately. To prepare for the future of Big Data, it is necessary to update the strategy at any time, and it is important to understand how to switch between the two models.
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