Stein and Morrison 2014 (†866)Stein, Brian, and Alan Morrison. "The Enterprise Data Lake: Better Integration and Deeper Analytics," Technology Forecast: Rethinking Integration 1 (2014).
- data lake (p. 2): To solve the challenge the hospital faced with data storage, integration, and accessibility, the hospital created a data lake based on a Hadoop architecture, which enables distributed big data processing by using broadly accepted open software standards and massively parallel commodity hardware. ¶ Hadoop allows the hospital’s disparate records to be stored in their native formats for later parsing, rather than forcing all-or-nothing integration up front as in a data warehousing scenario. Preserving the native format also helps maintain data provenance and fidelity, so different analyses can be performed using different contexts. The data lake has made possible several data analysis projects, including the ability to predict the likelihood of readmissions and take preventive measures to reduce the number of readmissions.1 (†2603)
- data lake (p. 5): Many people have heard of data lakes, but like the term big data, definitions vary. Four criteria are central to a good definition: · Size and low cost: Data lakes are big. They can be an order of magnitude less expensive on a per-terabyte basis to set up and maintain than data warehouses. With Hadoop, petabyte-scale data volumes are neither expensive nor complicated to build and maintain. Some vendors that advocate the use of Hadoop claim that the cost per terabyte for data warehousing can be as much as $250,000, versus $2,500 per terabyte (or even less than $1,000 per terabyte) for a Hadoop cluster. Other vendors advocating traditional data warehousing and storage infrastructure dispute these claims and make a distinction between the cost of storing terabytes and the cost of writing or written terabytes. · Fidelity: Hadoop data lakes preserve data in its original form and capture changes to data and contextual semantics throughout the data lifecycle. This approach is especially useful for compliance and internal audit. If the data has undergone transformations, aggregations, and updates, most organizations typically struggle to piece data together when the need arises and have little hope of determining clear provenance. · Ease of accessibility: Accessibility is easy in the data lake, which is one benefit of preserving the data in its original form. Whether structured, unstructured, or semi-structured, data is loaded and stored as is to be transformed later. Customer, supplier, and operations data are consolidated with little or no effort from data owners, which eliminates internal political or technical barriers to increased data sharing. Neither detailed business requirements nor painstaking data modeling are prerequisites. · Late binding: Hadoop lends itself to flexible, task-oriented structuring and does not require up-front data models. (†2604)