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  1. Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP (DOLAP '11)
  2. Easy and effective parallel programmable ETL
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A rule-based tool for gradual granular data aggregation
Easy and effective parallel programmable ETL
Column-oriented query processing for row stores
Analytics over large-scale multidimensional data: the big data revolution!
Enforcing strictness in integration of dimensions: beyond instance matching
A model-driven framework for ETL process development
Data visualization on web-based OLAP
Building cubes with MapReduce
Building a display of missing information in a data sieve
Interactive exploration and visualization of OLAP cubes
Incremental integration of data warehouses: the hetero-homogeneous approach
Optimization of operator partitions in stream data warehouse
Analyzing continuous fields with OLAP cubes
Towards a more straightforward and more expressive metamodel for SDW modeling
Cascading top-k keyword search over relational databases

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Easy and effective parallel programmable ETL

Content Provider ACM Digital Library
Author Thomsen, Christian Pedersen, Torben Bach
Abstract Extract-Transform-Load (ETL) programs are used to load data into data warehouses (DWs). An ETL program must extract data from sources, apply different transformations to it, and use the DW to look up/insert the data. It is both time consuming to develop and to run an ETL program. It is, however, typically the case that the ETL program can exploit both task parallelism and data parallelism to run faster. This, on the other hand, makes the development time longer as it is complex to create a parallel ETL program. To remedy this situation, we propose efficient ways to parallelize typical ETL tasks and we implement these new constructs in an ETL framework. The constructs are easy to apply and do only require few modifications to an ETL program to parallelize it. They support both task and data parallelism and give the programmer different possibilities to choose from. An experimental evaluation shows that by using a little more CPU time, the (wall-clock) time to run an ETL program can be greatly reduced.
Starting Page 37
Ending Page 44
Page Count 8
File Format PDF
ISBN 9781450309639
DOI 10.1145/2064676.2064684
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-10-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Extract-transform-load (etl) Parallelism Python
Content Type Text
Resource Type Article
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