@article{4093,
  author = {Jan Schneider, Christoph Gröger and Arnold Lutsch},
  title = {Evolving Analytical Data Platforms from Data Warehouses to Address the Lake House Attributes},
  journal = {Journal of Intelligent Computing},
  year = {2024},
  volume = {15},
  number = {3},
  doi = {https://doi.org/10.6025/jic/2024/15/3/81-88},
  url = {https://www.dline.info/jic/fulltext/v15n3/jicv15n3_1.pdf},
  abstract = {The ongoing rise in the availability of data and the increasing sophistication of data-driven analysis methods have motivated companies to gather and examine vast quantities of data for competitive benefits. Various tools for managing analytical data have been created to support these activities. Traditionally, data warehouses have been the go-to option for business analysts for reporting and OLAP. At the same time, data lakes have introduced a new approach supporting complex analytics. Given the distinct features and intended audiences of these two primary data platforms, companies often find it necessary to use both, leading to complicated, prone to errors, and costly systems. To tackle these challenges, there has been a recent push to merge the capabilities of data warehouses and data lakes into what is known as lake houses, aiming to offer a unified platform for various analytics needs. This work offers a summary of the development of analytical data platforms from data warehouses to data lakes to lake houses and discusses the unique attributes of lake houses. It also explores what functionalities data lakes lack, hindering their transition to lake houses.},
}