Big data infrastructure is the main component for any organization to sort, store, process, and analyze large datasets. The technology involved provides easy and quick access to corporate data for rapid, informed decision-making. Big data offers the potential to improve customer experiences, capture new markets, and drive top-line revenue growth creating a flourishing infrastructure market driven by a rapid increase in consumer and operational data. The market segment is forecasted to reach $4.2 billion by 2026 at a CAGR of 32.3% from 2021 to 2026.
Big data – Synopsis
Big data is a collection of datasets that are too large or complex to analyze and extract information from using traditional data processing methods. Oracle defines big data as data that contains great variety, in ever-increasing volumes, and velocity. Note, however, that there is no fixed threshold size for data to be defined as big data. And, therefore, data is categorized as big data by the following:
Volume – Volume of the data matters as the data could be too challenging or too extensive for an organization to handle and process. Processing high volumes of unstructured data gathered from social media, e-commerce, and the internet of things can generate a wide gamut of often unwieldy information.
Variety – Variety refers to the types of data that need to be processed and grouped, which can be an arduous task to accomplish. While structured data neatly fits in traditional data processing architecture like relational database systems, unstructured and semi-structured data like audio, video and text need additional preprocessing to derive meaningful insights.
Velocity – Any organization that rapidly receives or generates volumes of data and perhaps acts on it has big data. For instance, organizations processing data gathered from social media, e-commerce, and IoT likely fall under this category.
Big data infrastructure
In simple terms, big data infrastructure is the information technology infrastructure that hosts big data. The infrastructure entails
– The tools that collect data from various sources – Software systems and storage hardware that store the data – A network that transfers the data – Applications that host the analytics tools that analyze the data – Backup infrastructure to backup or archive the volumes of data
Big data infrastructure pain points and solutions
Big data is revolutionizing how organizations use the information to make informed decisions and guide broad infrastructure strategies. However, one of the biggest challenges of big data is storage, leading to new performance and scale challenges.
The rapidly increasing volume of data requires a highly flexible and scalable storage system such that the entire system doesn’t need to be brought down to increase storage capacity. To meet scalability requirements, object-oriented file systems can be leveraged as traditional file systems cannot support massive volumes of data.
Big data deals with structured and unstructured data involving social media tracking and transactions which are leveraged for tactical decision-making in real-time. Hence, big data storage cannot be latent to avoid the loss of value or risk of becoming stale. Implementing flash-based storage systems facilitates scale without sacrificing performance.
Another important aspect of storage is the cost involved as storage is one of the most expensive components of big data. However, specific techniques like de-duplication, building custom hardware, and utilizing tape for backup and redundancy can significantly reduce storage costs.
Another broad infrastructure challenge in big data is the accessibility of data from storage devices, as it depends on users who access it from multiple sources. Furthermore, the data storage must support various structured, semi-structured, and unstructured formats. Hence, storage environment requirements exceed traditional models and include handling information from diverse sources vital for operational success.
Recent studies aim to investigate and devise new approaches for taming big data storage and access challenges, allowing faster and more efficient access. One such method is leveraging smart and dynamic infrastructure, based on ideas similar to system virtualization that involves networking and storage that can automatically provision and adjust as necessary. Another approach is to introduce dependable systems whose aim is to create big data systems that are safe, reliable, available, and manageable big data systems, making them robust and cost-effective.
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