Big data architecture manages capture, processing, and analysis of data that is too complex for traditional data handling systems.
What constitutes big data varies from organization to organization. By and large, it depends on the data literacy of users and the power of their tools. For some companies, hundreds of gigabytes of data signal their entrance into the big data realm. Meanwhile, for large enterprises it could be hundreds of terabytes. Equally, as technology advances, so does the definition of big data. Not only the tools become more sophisticated, but a concept of the big data too. However, big data is increasingly defined by the value of the intelligence it holds. Therefore, businesses no longer measure big data only by its volume, but also by the richness of the insights it provides.
As such, the expectations attached to advanced analytics have shifted dramatically. Although big data storage facilities have fallen in price, the volumes of data continue to grow. Moreover, multiple sources deliver this data at varying velocities and scales, from granular data arriving at a rapid pace from smartphones to huge chunks dumped at incremental periods from historic systems. In response, companies often have to implement new software or machine learning systems to manage the incredible diversity of the data they receive. Today, these are the obstacles the big data architecture of today aims to overcome.
Why implement a big data architecture?
Dated IT infrastructure can no longer meet the demands of data handling. The volume, velocity, and variety of big data mean that organizations can no longer rely on old fashioned data management systems. Therefore, businesses need to implement modern big data architectures. Five key imperatives drive this shift:
- The democratization of data which demands the data to be accessible, secure, reliable, and properly managed.
- Increased connectivity, to ensure both internal and external communications are seamless.
- The move towards greater data literacy and self-service systems.
- The necessity for predictive and prescriptive analytics over historical reporting.
- Future-proofing IT capabilities.
Why cloud-based infrastructures are the key
There are numerous reasons why a move towards modern big data architecture is a key concern for enterprises of today. These initiatives are often complicated by the limitations of existing systems and incompatible formats. As a result, cloud-based data lakes are rapidly replacing data warehouses as the key component of modern big data architecture. Unlike a traditional data warehouse, cloud-based data lakes can manage all types of data, including unstructured information. Cloud storage systems can store data in its raw form, without the need for definition. In contrast to data warehouses, a cloud-based storage system will categorize data during analysis, as opposed to capture.
Furthermore, a cloud-based data lake is highly scalable. This means that enterprises can adapt to the size of their architecture according to their specific needs. In addition, cloud-based storage systems can store data from a great variety of sources, including existing databases, mobile devices, social media activity, and much more. Although the data warehouse certainly is not obsolete yet, it is quickly being superseded by cloud-based solutions as the centerpiece of a modern big data architecture.
Resilient big data architecture
Extracting insights from complex data sets requires a high-functioning and resilient big data management solution. Therefore, IT managers and C-levels should bear in mind the imperatives mentioned above. When delivering a big data management plan, corporations should determine whether or not they have the necessary internal capabilities to handle big data. From there, they can define whether or not they require external consultants or software to meet their business goals.