Is Data Virtualization the Secret Behind Operationalizing Data Lakes
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The amount of expanding volume and variety of data originating from various sources are a massive challenge for businesses. In attempts to overcome their big data challenges, organizations are exploring data lakes as repositories where huge volumes and varieties of raw, unstructured data is housed. When an organization’s data is stored in a ‘lake’ for accessibility purposes, it is appealing for business users and architects – if only they know what they are looking for and exactly where to look!

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A Digital Revolution by Logical Data Lakes? 

A data lake is a centralized repository that allows users to store all their data at any scale. However, the data available in a data lake is not necessarily very structured or relational. It is like a storage for all the raw, unstructured data which is being saved for any unforeseen future usage or needs. However, this is where the concept of a logical data lake comes in. With a logical data lake, all the data associated with a data lake is not physically stored in one place – there might be a physical data lake behind the scenes but there is more to the concept. By creating a “virtual” or a “logical” data lake through a layer of abstraction, organizations are connecting all their data sources in a logical fashion i.e. by using a logical data lake through an approach called data virtualization. There is a digital revolution happening in a broad sense, in the data world where there are a variety of things happening ranging from cloud transition, data science, advanced analytics and more. Data has definitely become the backbone of the industry and logical data lakes are a big part of this revolution. 

Deciphering Data Lakes 

Before the discussion moves ahead, it is very important to understand the differences between a physical and a logical data lake. A physical data Lake, as the name suggests, is where data actually resides in a way that it takes up memory and storage. In a physical data lake, data is captured from the field, from various sources such as geospatial data or even social media data and this raw data, unstructured data is stored on premises in a data center or in the public cloud. In the case of logical data lakes, there is more than the physical data lake created by the organization in the form of a data center or the public cloud. There is a logical connection between all the various data silos and the physical data lake. Essentially, when users are utilizing the data for reporting or analytical purposes, they are not only accessing the data from the data center but also from the additional data sources. 

Another differentiating factor with physical data lakes is that the data gets combined in reality which takes up more storage space leading to additional costs to the organizations and the data is constantly replicated as well. But with logical data lakes,the rate of data replication is minimal, if at all it happens and this is a crucial differentiator. 

In a nutshell, physical data lakes are more expensive and less compliant due to all the data replication that’s happening and there is always an added risk of data or information leakage. Whereas, with a logical data lake, those risks are mitigated since the data is not being moved or replicating too much leading to higher level of data security. 

Pain Points with Data Lakes 

Historically, data lakes were created because organizations generated immense amounts of valuable data. This generated data used to be stored in data warehouses when organizations knew where and how this data would be utilized for example for data science and advanced analytics purposes. But there were also huge quantities of valuable data being generated from various different silos and sources and organizations could not predict its future usability. Hence, instead of disposing the data, these were stored in a sort of a dumping ground and this stored data turned into a data lake over time. To operationalize this data to make it useful, organizations must invest in manpower to build resources such as a data science team, an analytics team and that too across different locations and industries. This replication, expense and total cost of ownership is a massive challenge for organizations. Another challenge is just deciphering and deriving the usable data from these data lakes, which is a huge undertaking for data scientists. This is where the concept of logical data lakes becomes crucial. 

Building a Logical Data Lake with Data Virtualization

There are multiple ways to build logical data lakes, but one of the most prominent ways to build a logical data lake is by using the approach called data virtualization. Data virtualization basically creates an abstraction layer like the Denodo Platform on top of the physical data lake and the various other data sources and silos and essentially connects them by creating a logical data lake.To sum it up, data virtualization facilitates and expedites data access and usage in a cost-effective manner and assists organizations in deriving more value from their data lakes and data sources. The data virtualization technology improves an organization’s ability to govern and extract more value from its data lakes by extending them as logical data lakes.

For more information about if data virtualization for operationalizing data lakes is the real deal, join our discussion with Saptarshi Sengupta, Senior Director of Product Marketing at Denodo, on All Things Data

Neha Gurudatt