The evolution of data architecture for analytics started with data warehouses. These data warehouses were really good at creating insights from historic data. Although it offers some basis for predictive modeling, they are not agile enough or responsive for the large volume and variety of data that organizations are facing today and that has become a challenge for them.
The next progression in analytics-based architectures is data lakes. The reason is that it quickly accounted for the diverse schemas and data types with which organizations are dealing at a large scale. But the way it accounted for diversity has lots of hidden things that can be explored. Data lakes are fundamentally enterprise file systems and thus turn into ungoverned data swamps that need extensive engineering for the companies in order to connect and query data. This results in a lot of time spent on wrangling data that is still unconnected and because of that the productivity suffers and many insights are missed.
Well, there are data lake houses that combine a few of the best properties of both data warehouses and data lakes, we still think it is too early to pass judgments on their utility and hence they suffer. Since they are indistinguishable from relational systems, they develop an inability to deal with the data diversity problems of enterprises. We can say that relational data models are not very good when it comes to handling data diversity
Data analytics has evolved from data warehouses to data lakes and now to data fabrics. Data fabrics implemented properly represent the last evolution of analytics architecture. It reduces the efforts of data engineers, data scientists and data modelers which they spent on preparing data as compared to the previous approaches that are based on physical consolidation of data.
With a combination of semantic data models like knowledge graphs and data visualization, a data lake approach allows data to remain in its native place and provides uniform access to that data for query across clouds, and organizations.
This approach streamlines the complexity of data pipelines, reduces DataOps costs and reduces time to analytic insight.
Comprehensive data fabrics provide enhanced analytics to the organization and knowledge graphs play an important role in that. Their graph underpinnings are critical in order to represent complex relationships between data sets that are most diverse to improve insights. They align data within a universal graph to provide organizations with rationalized access to the structured, semi-structured and unstructured data.
Knowledge graphs can identify relationships between individual and collective attributes. They also can make intelligent inferences about semantic facts to build additional knowledge on any specific domain. These capabilities when come together firms can know much more about their data’s significance to business outcomes.
Expressive Semantic Models
Semantic data models are detailed with real-world information about concepts, and problems which business users understand. Their ability to identify relationships between data to create intelligent inference are the backbone of semantic knowledge graphs. They simplify the kind of schema concerns that monopolize the time of data modelers.
They automatically expand to accommodate new types of data requirements, on the other hand, relational data models generally require modelers to create new schemas and then migrate data accordingly. This advantage addresses data wrangling concerns and also enhances the real-world knowledge.
Virtualization technologies are now entering the heart of data fabrics. They offer representations of data that are uniformly accessed irrespective of the location of data in the data fabric, data can remain in the source systems yet it is still accessed in a single place. This also reduces the need for replicating data.
The data fabric approach is based on the meaning and not the location of data and diminishes the data silo culture. It promotes data sharing, data reuse, and insights by connecting data through enterprise, in cloud and on-premise environments.
The Epitome of Analytics Architecture
An analytics architecture is based on where data resides in the storage layer. Data fabrics are the backbone of the modern state architecture that too when implemented efficiently with data visualization, knowledge graphs and data models. These capabilities together eliminate a lot of time, costs and work spent on data pipelines and DataOps.
It does this by bringing together data at the computation layer instead of consolidating it physically at the storage layer and thus it results in better and greater speed to insights
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