In this extremely competitive market, the key to success for an enterprise lies in the collection of data across all sources possible followed by rapid and efficient extraction of useful data from the same. This however can be very challenging considering the sheer volume of data that needs to be dealt with while trying to address the issues arising in the proper integration of both human and technical resources. Another major challenge is the effective storage and management of the ever-expanding data pool in a way that is consistent and offers data governance for analytics. The main dilemma for an organization however is to choose an appropriate architecture for enterprise data management. A Data Fabric can be more practical for real-time operations while a Data Lake provides a larger pool of intel required for the said operations themselves. In this article, we will explore both.
What is Data Fabric?
A data fabric can be considered as a data architecture that is layered on top of various data sources an enterprise has access to. The implementation is convenient because a data fabric is indifferent to any specific mode of data storage allowing for a large-scale implementation without the need for homogenizing the data pools. What a data fabric platform then does is provide integrated data which only contains relevant and specific information, to the specific user, on-demand, in a very short span of time. This data provides insight and leads to the creation of more polished business strategies and more personalized ones.
This quick access to relevant, filtered information sourced from various non-homogeneous data pools would otherwise be impossible mostly because of two reasons:
- The data pool is ever-expanding
- The traditional methods of data integration consume a substantial amount of time and resources making quick access to data impossible.
These concerns are addressed in more detail in the following section.
How is it beneficial?
To understand the benefits of a data fabric architecture we must understand the underlying principles of its operation which mirrors the AI Ladder approach. The process of condensing useful data is achieved by the following steps:
- Collect – Data is collected across all available data sources.
- Organize – Collected data is then screened for quality and cataloged to facilitate quick and accurate integration. This valuable data is then governed to limit access to selected users.
- Analyze – The first step is to create an AI Model composed of algorithms that are responsible to impart specific characteristics to the nature of the Data Mesh. This is first tested on a low volume workload to identify errors or unforeseen results – following a successful test run, modifications, and upgrades to the feature set are made and further trials are conducted on heavier workloads that simulate real-life scenarios.
- Infuse – Infusion of created AI models with the data pool leading to the creation of a data mesh.
The aim is to execute each of the above steps with consistency, efficiency, and proper governance of data.
Non-homogeneous data accumulates in a pool when data is stored across multiple systems, each with a separate way of organizing and integrating data. This creates multiple layers of data over time that cannot be integrated in a cohesive manner thwarting chances of quick access to useful information. Attempting to integrate all this data requires time and IT development resources which can be bypassed with the implementation of a well-designed Data Mesh that can cut down inherent boundaries between man and machine with a swift yet accurate data collection and integration.
Okay, so what does a Data Lake do then?
As the name implies, a data lake is a storehouse of data just like a lake is for water. Specifically, it acts as a large, centralized data pool responsible for the collection and storage of large data from multiple sources in its raw format. This data can be classified into structured, semi-structured, and unstructured data. All of these are structured and tagged for quick and efficient retrieval upon user request.
Lakes can be created by storing data across multiple ranges of hardware and is therefore cheap to operate with a seemingly endless storage capacity. This is why Data Lakes are quite enticing to enterprises that have to deal with a constant influx of big data as it facilitates data storage
as-is and in all formats.
The complete data set can be either stored on a server or in the cloud – the latter is cheaper, requires less time to set up, and can be scaled up as per demand. Convenience has made cloud computing services gain significant traction recently with multiple players in the game.
Data Lakes lack inherent analytical features and rely on other software tools for the same. Basically, it means that while Data Lakes are great for storing large data as-is from multiple sources, it does not combine relevant data together for analytical purposes.
Conclusion
Therefore, Data Fabric and Data Mesh have their own strengths and one isn’t really a complete substitute for the other. An enterprise often has to collect large amounts of data across multiple sources with an aim to formulate more inclusive business strategies which appeal to a wider clientele. At the same time, all that data must be combed in an efficient yet faster way to extract useful information which actually contributes to the formulation of effective business models. This is where one compliments the other and adopting both would be the best-case scenario.