Data Analysis on Streams
Analyzing real-time data poses special kinds of challenges, such as dealing with large event rates, aggregating activities for millions of objects in parallel, and processing queries with subsecond latency. In addition, the set of available tools and approaches to deal with streaming data is current...
| Otros Autores: | |
|---|---|
| Formato: | |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc
2014.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629579006719 |
| Sumario: | Analyzing real-time data poses special kinds of challenges, such as dealing with large event rates, aggregating activities for millions of objects in parallel, and processing queries with subsecond latency. In addition, the set of available tools and approaches to deal with streaming data is currently highly fragmented. In this webcast, Mikio Braun will discuss building reliable and efficient solutions for real-time data analysis, including approaches that rely on scaling--both batch-oriented (such as MapReduce), and stream-oriented (such as Apache Storm and Apache Spark). He will also focus on use of approximative algorithms (used heavily in streamdrill) for counting, trending, and outlier detection. |
|---|---|
| Notas: | Title from title screen (viewed Aug. 13, 2014). |
| Descripción Física: | 1 online resource (1 video file, approximately 47 min.) |
