Paper Info
Title
ETLMR: a highly scalable dimensional ETL framework based on mapreduce
Abstract
Extract-Transform-Load (ETL) flows periodically populate data warehouses (DWs) with data from different source systems. An increasing challenge for ETL flows is processing huge volumes of data quickly. MapReduce is establishing itself as the de-facto standard for large-scale data-intensive processing. However, MapReduce lacks support for high-level ETL specific constructs, resulting in low ETL programmer productivity. This paper presents a scalable dimensional ETL framework, ETLMR, based on MapReduce. ETLMR has built-in native support for operations on DW-specific constructs such as star schemas, snowflake schemas and slowly changing dimensions (SCDs). This enables ETL developers to construct scalable MapReduce-based ETL flows with very few code lines. To achieve good performance and load balancing, a number of dimension and fact processing schemes are presented, including techniques for efficiently processing different types of dimensions. The paper describes the integration of ETLMR with aMapReduce framework and evaluates its performance on large realistic data sets. The experimental results show that ETLMR achieves very good scalability and compares favourably with other MapReduce data warehousing tools.
Year
DOI
Venue
2013
10.1007/978-3-642-37574-3_1
T. Large-Scale Data- and Knowledge-Centered Systems
Keywords
DocType
Volume
fact processing scheme,data warehouse,low etl programmer productivity,large realistic data set,scalable mapreduce-based etl,etl developer,high-level etl specific construct,scalable dimensional etl framework,mapreduce data warehousing tool,etl flow
Journal
8
Citations 
PageRank 
References 
23
1.61
19
Authors
3
Name
Order
Citations
PageRank
Xiufeng Liu110814.69
Christian Thomsen29512.10
Torben Bach Pedersen32102181.24