MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
MapReduce and parallel DBMSs: friends or foes?
Communications of the ACM - Amir Pnueli: Ahead of His Time
pygrametl: a powerful programming framework for extract-transform-load programmers
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
ETLMR: a highly scalable dimensional ETL framework based on mapreduce
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Hi-index | 0.00 |
This paper demonstrates ETLMR, a novel dimensional Extract--Transform--Load (ETL) programming framework that uses Map-Reduce to achieve scalability. ETLMR has built-in native support of data warehouse (DW) specific constructs such as star schemas, snowflake schemas, and slowly changing dimensions (SCDs). This makes it possible to build MapReduce-based dimensional ETL flows very easily. The ETL process can be configured with only few lines of code. We will demonstrate the concrete steps in using ETLMR to load data into a (partly snowflaked) DW schema. This includes configuration of data sources and targets, dimension processing schemes, fact processing, and deployment. In addition, we also present the scalability on large data sets.