The Four Prominent Layers of Big Data Architecture

Big data architecture is the design that serves as the foundation for big data systems. For business users, the big data architecture provides a framework for addressing needs and scope. It’s the design that supports big data systems. It’s meant to be structured in a way that allows for the most efficient data acquisition, processing, and analysis. This big data architecture mainly comprises four basic layers that enable a safe data flow. So, let’s take a closer look at what they are and what they signify.

Four Prominent Big Data Architecture Layers

Many industries are facing challenges in storing and analyzing large amounts of data. Understanding these data architecture layers and leveraging big data properly can help drive innovation and success. The layers are the method of grouping together components that execute certain tasks. These layers become increasingly important when quantities exceed an individual’s capacity and some sort of automation is in demand. In most cases, a big data solution consists of four prominent big data architecture layers as indicated below. Also, please note that these layers are purely conceptual and serve as a technique of organizing the architecture’s components.

Big Data Sources Layer

This layer is capable of batch and real-time processing of large amounts of data from different sources. These sources include data warehouses, RDMS, SaaS apps, and Internet of Things sensors. It contains all of the data sources needed to provide the critical insight needed to handle the business challenge. The information is either organized or disorganized. It is also available in a variety of formats and origins. One of the first steps in building a data strategy is to assess what you now have and compare it to what you’ll need to answer the critical questions you’re seeking answers to. You may already have all you need, or you may require fresh sources.

Data Storage Layer

The data storage layer is the second layer of the big data architecture. The ability to store, handle, and analyze vast unstructured data leads to the forecast of a revolutionary data-driven society. Businesses can now store and analyze more data for less money while also improving their analytical capabilities. This layer collects, translates, and stores data from multiple sources in a format that data analysis tools can understand. Once your Big Data has been collected from your sources, this is where it will be stored. However, governance policies and compliance requirements, establish the optimum storage format for distinct types of data.

Big Data Analysis Layer

This is the layer where data is mined for business insights. To draw insights from the data, it pulls data from the data storage layer or directly from the data source. The analytical layer’s design needs significant consideration and preparation. Proper decisions should be there for managing the tasks involved in building an appropriate analysis, deriving insights from the data, identifying the necessary entities, and locating the data sources that can supply the data for these entities. Data scientists and other technical users can create analytical models that help firms not only understand their past operations but also predict what will happen in the future. It also helps to make decisions about how to transform the business in a better way. Furthermore, the analytics layer’s BI and data visualization components make data easier to interpret and manage.

Data Consumption Layer

This layer takes the information from the analysis layer and passes it on to the appropriate output layer. The findings of the analysis are useful for a wide range of internal and external users. The insights can detect fraud by monitoring transactions in real-time and comparing them with the previous or existing data. A consumer can be warned of a probable fraud while the fraudulent transaction is taking place, allowing prompt remedial action. However, seeing the results of the analysis layer might be difficult. Also, looking at what rivals in comparable markets are doing might be useful at times. 

A recommendation engine can use the results of the analysis to match clients with the goods they enjoy. The recommendation engine examines the available data and generates customized, real-time recommendations. Internal users can also utilize the consumption layer to comprehend, discover, and explore federated data both inside and outside the company. Furthermore, internal users benefit from the ability to create reports and dashboards for business users, which allows them to make informed decisions and develop relevant strategies. Real-time business alerts may be created from data, and operational key performance indicators can be monitored to improve operational effectiveness.

Build Strong Big Data Architecture With Ksolves

As big data, insights, and Artificial Intelligence become more integrated into most businesses’ day-to-day operations, big data architecture must evolve in a new direction. Traditional database systems would struggle to query data lakes that could contain hundreds of terabytes of data. Leaders in data and technology who embrace this new strategy will better position their organizations to be innovative, resilient, and competitive in the future. Ksolves, the top big data development company, touches practically all data operations, including gathering, processing, storage, analytics, and exposure, to improve your big data architecture. We truly understand that different organizations will have different needs. So, we use customized big data architecture design configurations to best match our clients’ needs. Also, with many requiring a complete re-architecting of their existing data platforms and big data infrastructure, we are best at that as well. 

Our big data developers help you to swiftly test and adopt the best big data architecture, allowing you to implement change quickly. Every big data project we work on is focused on producing high-quality results. We specialize in advanced analytics solutions for organizations servicing various sectors as the best big data consulting company. Apart from successful deployment, our big data services include continuous audits and analysis to guarantee that your solution is working properly and delivering the desired business benefits to your business. Also, at Ksolves, our data management experts can advise you on the finest Big Data adoption strategies and methodologies. Thus, to improve security, encourage consumers to exchange data, generate accurate predictions, and increase customer happiness, hire our Big Data development services.