Big data architecture is a system for managing huge volumes of data, which may be too complicated for traditional database management systems (DBMSs) to handle. It dictates how data must be processed, stored, accessed, and consumed.
Big data architecture directs how enterprises use big data analytics. As such, it serves as a blueprint for big data networks and solutions. It shows how solutions would work, what components must be put in place, how information processes would proceed, and what security measures must be taken.
Think of big data architecture as a water dam built to control water flow, making it easier for everyone to receive the right amount of water supply.
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Who Needs Big Data Architecture?
Organizations that fit the following descriptions need big data architecture:
- Their business deals with extracting information from a vast amount of network or data logs.
- Their operations regularly involve processing data that’s more than 100GB in size and takes running tasks for more than 8 hours.
- They want to incorporate third-party products into their networks to improve current operations, requiring them to invest in a big data project.
- They have tons of unstructured data on hand that they want to structure for better data analysis.
What Are the Benefits of Big Data Architecture?
The volume of data that organizations have to deal with continually grows by the day. In an era where data is highly essential for organizations, access is not enough. They need to make sense of available data and apply findings to their operations. That is where big data architecture comes in. It helps in:
- Cost reduction: Using big data technologies, such as cloud-based analytics, can help companies reduce the cost of operating, storing, and managing massive amounts of data in on-premises servers.
- Real-time decision making: For processes that require timely decisions, big data architectures with a streaming component can be useful.
- Business prediction: An organization’s success is often dictated by how forward-looking it is. Those who can prove that they are industry leaders can achieve the most success. That is possible if they can effectively predict future consumer needs and forecast trends based on big data analytics.
What Are the Challenges That Come with Big Data Architecture Use?
While an effective big data architecture offers tons of benefits, implementing it comes with challenges as well. These include:
- Poor data quality: One of the main challenges in using big data architecture is dealing with a diverse set of data sources that do not conform to the same quality. Part of the work that organizations must do is ensure that they do not have missing or duplicate data that can significantly impact data analytics.
- Scalability: While big data architectures help companies deal with big data, they can be hard to upscale or downscale if they’re not properly designed. It is crucial to factor in scaling up or down when designing the architecture. Often, organizations lack budget, which can have a significant effect on overall performance.
- Security: Big data is an essential asset since it can provide deep insights for an organization. However, protecting that data should also be included in the planning stage. Nowadays, hackers can steal or fabricate data that can impact analysis.
Despite the cost and inherent complexity of working with big data architecture, a business needs to have it. Success boils down to how carefully it is planned out and managed by stakeholders.