Data Lakes and Data Virtualization: Which Approach To Take
Data lakes and data virtualization are both approaches to data management. However, each has its own advantages and disadvantages. Let’s take a look at both to see which could be a better approach to take.
In its simplest definition, a data lake is a storage repository that holds a large amount of raw data in its native form. A data lake can be used to investigate, explore, archive, and refine data. Data lakes are becoming much more useful as there are now so many data sources that companies can use to make better business decisions such as social networks, online news, review websites, and weblogs. All these data sources result in rapidly increasing data volumes that can be analyzed.
Data lakes work similar to a body of water where water flows in, fill a reservoir, and then flows out. The incoming flow represents the multiple raw data archives including emails, spreadsheets, and social media content. The reservoir of water represents a dataset where analytics can be run. The outflow of water is the analyzed data.
The benefits of a data lake are endless. Data lakes allow a place of storage for unlimited amounts of long-term data in any format. They can also store data that may or may not be used later. With data lakes, all types of data, whether structured or unstructured, can be integrated. Data lakes allow for data exploration and discovery.
The disadvantages of a data lake are that they are not good for quick and easy analyzing. It is not ideal for returning queries back very fast. Also, data may not be clean and it may be hard to find the quality or the lineage of the data.
Data virtualization is more of an agile data integration approach that many uses to gain more insight from their data. With data virtualization, you can respond faster to the changing analytics or BI needs and save over data replication and consolidation. Data virtualization provides instant access to all the data you need, when you need it, the way you want it. With this approach, it is easy to gain more business insights by leveraging all your data.
However, data virtualization should not be used at all times. Depending on which situation you are approaching, sometimes consolidating data in a warehouse would work much more effectively.
Here at SPIN we can assess your requirements and guide you as to which approach suits you better and help with implementation. Contact us!
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