4 Strategies to a Fast-track Data Lake Implementation within Enterprises

Developing comprehensive data lake architecture is one of the best ways to enhance operational efficiencies while having a unified source of data truth. Firms can analyze customer information in real-time, extract meaningful trends & insights, and remain compliant to industry norms/regulations in a highly robust manner.

As it relates to data lake implementation, preserving data integrity in its native form is critical. Additionally, it is important to handle data sources correctly while ensuring that sensitive information is protected when accessed or stored. There are several bottlenecks that enterprises must address, which is why following the four strategies below is critical to implementing a robust data lake successfully.  

Outlining business requirements early

While data lakes are designed to hold large structured, semi-structured, and unstructured data quantities, it is best to create a data lake that fits  your business requirements. Enterprises need to analyze their data analytics needs and design their data lakes leveraging department-level requirements within the organization. Third-party requirements may also need to be considered, especially when dealing with large data quantities and multi-format data transfers.   

Managing the input flow of information

Enterprises need to manage input sources entering the data lake holistically. While there may be diverse data sources feeding into a Hadoop data lake, each source must follow a specified pathway and protocol. This is where an experienced technological partner, such as Impetus Technologies, can help enterprises design a highly efficient data lake. By managing incoming files correctly, and defining their relationship within and between datasets, enterprises can preserve data fidelity at scale within the data lake successfully.

Testing is key to successful adoption

While enterprises may want to expedite their data lake adoption, it is best to develop a test model (or proof of concept) for your data lake prior to engaging in a large-scale implementation. The test data lake should capture the essential requirements necessary, while also being agile enough to run test applications. Firms can then run custom quality tests to validate the data stored within the data lake and extract insights to test the sample set. Any errors, technology gaps, or code mistakes can be highlighted quickly during this stage.

Monitoring data lakes regularly

By testing the implementation of the data lake regularly, enterprises can reduce the risk of bugs or errors present within the architecture. This fast-tracks the data lake adoption, as there are minimal hassles while scaling it to the enterprise-level. Firms should also monitor the performance of the data lake frequently to ensure that it keeps up with the evolving needs of the enterprise.