Data Lakes, repositories or archives for large quantities of structured and/or unstructured data, are fast approaching as one of the most efficient, dependable, and easy ways of storing data outside of silos. This specific system is structured to accept data from different origination points making it ideal for larger businesses. While data lakes are known for their enormous capacity, they also have the ability to preserve original data accuracy as well as historical lineage. While data lakes offer a solution to the challenges of integration, this form of storage also happens to be cost-effective and flexible to work around different business structures and needs.
Above all else, Data Lakes offer the ease of accessibility and create the appropriate space and platform for analysis, such as:
- Providing a collection point for real-time analytics
- Offering a staging area for a warehouse
- Creating a space for scientific discovery and ideation
There are two basic, popular structures, the Hadoop Distributed File System and the NoSQL (Not only SQL). These are specific data lake architectures designed to structure scalable data. These processing models allow for faster-growing data management, ideal for business models structured around massive data consumption.
What are the Positives in Using a Data Lake?
For many businesses, the new challenge is dealing with high quantities of data that need to be easily accessible and integrated. Previous broad-based data integration models followed a single floor plan, therefore restricting the multifaceted uses of data that many businesses desired. Data Lakes step outside of this archaic monolithic approach allowing for data growth and variety to expand as well as the tearing down of data silos.
Data Lakes have many positive attributes, two of those being flexibility in design and accessibility. While older data repositories offer remote assistance in set-up and technical support, Data Lakes provide unique localized teams of business analysts and scientists to work directly with business to customize their system.
Mistakes to Avoid
Before implementing a Data Lake system, it is imperative for all involved parties to develop a strategic plan. A concrete plan should take into consideration the appropriate technologies and methods for the specific issue or problem that the business is troubleshooting. Consequences of jumping into implementation include the creation of more silos, empty sandboxes, or simply losing track of the data inventory.
A Growing Data Lake
All Data Lakes begin with the raw product and, as the business continues to utilize this style of a repository, new data flows in. Shared semantics will begin to mature within the user interaction, as well as feedback, aiding in the refining of the data lake. In short, the growth of a data lake inspires discovery.
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