Extract-Transform-Load (ETL) handles large amount of data and manages workload through dataflows. ETL dataflows are widely regarded as complex and expensive operations in terms of time and system resources. In order to minimize the time and the resou
rces required by ETL dataflows, this paper presents a framework to optimize dataflows using shared cache and parallelization techniques. The framework classifies the components in an ETL dataflow into different categories based on their data operation properties. The framework then partitions the dataflow based on the classification at different granularities. Furthermore, the framework applies optimization techniques such as cache re-using, pipelining and multi-threading to the already-partitioned dataflows. The proposed techniques reduce system memory footprint and the frequency of copying data between different components, and also take full advantage of the computing power of multi-core processors. The experimental results show that the proposed optimization framework is 4.7 times faster than the ordinary ETL dataflows (without using the proposed optimization techniques), and outperforms the similar tool (Kettle).
In data warehousing, Extract-Transform-Load (ETL) extracts the data from data sources into a central data warehouse regularly for the support of business decision-makings. The data from transaction processing systems are featured with the high freque
nt changes of insertion, update, and deletion. It is challenging for ETL to propagate the changes to the data warehouse, and maintain the change history. Moreover, ETL jobs typically run in a sequential order when processing the data with dependencies, which is not optimal, eg, when processing early-arriving data. In this paper, we propose a two-level data staging ETL for handling transaction data. The proposed method detects the changes of the data from transactional processing systems, identifies the corresponding operation codes for the changes, and uses two staging databases to facilitate the data processing in an ETL process. The proposed ETL provides the one-stop method for fast-changing, slowly-changing and early-arriving data processing.
