Revisiting aggregation techniques for big data
Vassilis J. Tsotras · 2013
In this talk we first present an introduction to AsterixDB [1], a parallel, semistructured platform to ingest, store, index, query, analyze, and publish "big data" (http://asterixdb.ics.uci.edu) and the various challenges we addressed while building it. AsterixDB combines ideas from semistructured data management, parallel database systems, and first-generation data-intensive computing platforms (MapReduce and Hadoop). The full AsterixDB software stack provides support for big data applications from the storage and processing engine (Hyracks [2] available at: http://hyracks.googlecode.com), to the exible query optimization layer (Algebricks), to the interfaces for user-level interaction (AQL, HiveQL, Pregelix, etc.) Hyracks is a partitioned-parallel engine for data intensive computing jobs in the form of DAGs. Algebricks is a model-agnostic, algebraic layer for compiling and optimizing parallel queries to be processed by Hyracks. Queries for AsterixDB can be expressed by either popular higher-level data analysis languages like Pig, Hive or Jaql, or by its native query language (AQL) and data model (ADM) with support for semi-structured information and fuzzy data.