What is mean by Data Warehouse Optimization?
Dataware optimization is effectively utilizing the Dataware house with efficient resources and cost effective methods.
Today’s emerging data volumes can prevent Online Transaction Processing (OLTP) systems from generating and processing transactions efficiently which can cause Data Warehouse (DW) performance issues when querying data. Total Cost of Ownership (TCO), too, escalates rapidly because of the need for upgrades to DW hardware, and since licenses are priced according to volume. Organizations often pay to store information simply because they might need it.
Radically new capabilities are needed to enable DW and OLTP to function cost-effectively in this new environment. First, organizations need to cope with data volumes that traditional DW platforms (whether RDBMSs or appliances) were never designed for. Second, they must deal with unstructured, semi-structured and structured data.
Big data technologies such as Apache Hadoop excel at managing large volumes of unstructured data. However, this data needs to be pulled together with structured data for analytical purposes. As Big Data technologies and Apache Hadoop are coming into mainstream use, being able to integrate these new technologies with existing legacy Data Warehouse platforms to get the best of both worlds is key.
As Big Data technologies and Apache Hadoop are coming into mainstream use, being able to integrate these new technologies with existing legacy Data Warehouse platforms to get the best of both worlds is key.
Steps to increase the performance please check this URL Five Steps to Optimizing BI and Data Warehouse Performance - InformationWeek
In MapR how it is providing the performance and optimization check this URL Data Warehouse Optimization & Analytics on Hadoop Quick Start | MapR
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