@phdthesis{Sabra2024,
  type      = {Bachelor Thesis},
  author    = {Fatima Sabra},
  title     = {In-Memory-Databases: An In-Depth Dive Into SAP HANA},
  doi       = {10.57683/EPUB-2691},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-26914},
  pages     = {26},
  year      = {2024},
  abstract  = {In the contemporary era, many organizations and companies are confronted with a signif-icant surge in data volumes. This has led to the challenge of capturing, storing, managing, and analyzing terabytes of data, which are stored in diverse formats and originate from numerous internal and external sources. Furthermore, the emergence of novel applica-tions, such as trading, and artificial intelligence, has made the processing of vast amounts of data in real time an absolute necessity. These requirements exceed the processing ca-pacity of traditional on-disk database management systems, which are ill-equipped to manage this data and to provide real-time results. Therefore, data management requires new solutions to cope with the challenges of data volumes and processing data in real time. An in-memory database system (IMDB- or IMD system) is a database management system that is emerging as a solution to these challenges, with the support of other tech-nologies. IMDBs are capable of processing massive data distinctly faster than traditional database management systems. This work examines the approach of IMDBs, with a par-ticular focus on SAP HANA, and compares it with other IMDBs.},
  language  = {en}
}