Semantic and Presentation Layer of Big Data
In this Post, I introduced sematic layers and presentation or consumption layer with some programs as SQL Server Analysis Services (SSAS), Qlik Sense and Microsoft PowerBI business intelligence tools. Big data layers that are sources, data warehouse, semantic layer and, presentation layers display in this post. Some terms as Data Lake and warehouse component of big data are introduced [1].
Introduction
“Big data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.” [2].
In this description, Big data is huge volume data in storages beyond of traditional data . This concept and data processing are commonly use in many different business area in today. Some of these are e-commerce, manufacturing, IOT, cloud systems.
Big data is stream from data sources to presentation layers. Data sources is the first step creation of big data which are huge volume data. When data reach to huge volume, some traditional solution is not run. Thus, some tools developed for big data solution (SSAS, Hadoop, Azure). In this data, there is some different data sources. Some of these are, IOT sources, e- commerce, network log, etc. Variant and heterogenous of data will be complex, huge volume and low velocity. Second step is Data warehouse. Data warehouses use same data sources. Data warehouse is replicated. This step created for set-up the semantic layer and processing of presentation layers. [3]
https://prologika.com/prologika-newsletter-fall-2013/
- Semantic Layers
Semantic layer is the place where created relationship multi tables and preparing model for processing in presentation layers. This layer display the arranged data that acquired from data warehouse because of ETL (Extract, Transform, and Load). [4]. SQL Server Analysis Services (SSAS) tabular and multidimensional (OLAP) programs encounter of requirement for semantic layers. Data warehouses are replicated. It is most major in order to prevent missing data and, process in semantics layers [5]. This step is necessary in order to in process business intelligence programs. If there is direct connection storage and presentation layers, BI tools will be complex and low speed run because of in memory using. Model management will be harder due to direct to source using.
https://www.sqlshack.com/implementing-an-ssas-tabular-model-for-data-analytics/
- Presentation and Consumption Layers
Consumption or presentation layer generate insight and roadmap for business area from result data. It makes sense of arrange data. Microsoft PowerBI, Qlik Sense and Tableau is commonly use in business intelligence tools. In this Big data Layer, BI tools make sure process within Presentation Layer or Consumption Layer. The acquired data are arranged and prepared with model in semantic layer or analytics and reporting layer. [6].
The outcome of the analysis is consumed by various users within the organization and by entities external to the organization, such as customers, vendors, partners, and suppliers. [7].
- Conclusion
In this post, I tried to introduce semantic layers and presentation layer and importance of them. Complexity of source in big data concept, heterogenous data and variety types, replicated of data warehouse and importance, requirement of created semantic model for visualization and data business insight are written in post.
References
[1] Chessa, A., Fenu, G., Motta, E., Reforgiato Recupero, D., Osborne, F., Salatino, A., & Secchi, L. Enriching Data Lakes with Knowledge Graphs.
[2] TechAmerica Foundation’s Federal Big Data Commission, 2012
[3] Manikandan, G., & Abirami, S. (2017). Big data layers and analytics: a survey. In Computer Communication, Networking and Internet Security (pp. 383–393). Springer, Singapore.
[4] https://docs.microsoft.com/en-us/azure/architecture/guide/architecture-styles/big-data
[5] Satapathy, S. C., Bhateja, V., Raju, K. S., & Janakiramaiah, B. (2016). Computer communication, networking and internet security. Proceedings of IC3T, 5, 20160.(Pg-389)
[7] Understanding the architectural layers of a big data solution — IBM Developer
Author:
Mustafa Kaynak