Here are some of the difficulties of Implementing Data Warehouses:
- Implementing a data warehouse is generally a massive effort that must be planned and executed according to established methods.
- Construction, administration, and quality control are the significant operational issues which arises with data warehousing.
- Some of the important and challenging consideration while implementing data warehouse are: the design, construction and implementation of the warehouse.
- The building of an enterprise-wide warehouse in a large organization is a major undertaking.
- Manual Data Processing can risk the correctness of the data being entered.
- An intensive enterprise is the administration of a data warehouse, which is proportional to the complexity and size of the warehouse.
- The complex nature of the administration should be understood by an organization that attempts to administer a data warehouse.
- There must be a flexibility to accept and integrate analytics to streamline the business intelligence process.
- To handle the evolutions, acquisition component and the warehouse’s schema should be updated.
- A significant issue in data warehousing is the quality control of data. The major concerns are: quality and consistency of data.
- Consistency remain significant issues for the database administrator.
- One of the major challenge that has given differences in naming, domain definitions, identification numbers is Melding data from heterogeneous and disparate sources.
- The data warehouse administrator must consider the possible interactions with elements of warehouse, every time when a source database changes.
- There should be accuracy of data. The efficiency and working of a warehouse is only a good as the data that support its operation.
- Usage projections should be estimated conservatively prior to construction of the data warehouse and should be revised continually to reflect current requirements.
- To accommodate addition and attrition of data sources, the warehouse should be designed. This also avoids a major redesign.
- Sources and source data will be evolve, and the warehouse must accommodate such changes.
- Another continual challenge is fitting of the available source data into the data model of the warehouse. This is because requirements and capabilities of the warehouse will change over time as there will be a continual rapid change in technology.
- A far broader skills will be required by administration of data warehouse for traditional database administration.
- Managing the data warehouse in large organization, design of the management function and selection of the management team for a database warehouse are some of the major tasks.
Some best practices for implementing a Data Warehouse:
- The data warehouse must be built incrementally.
- User expectations about he completed projects should be managed.
- It is important to be politically aware.
- There should be a build in adaptability.
- Developing a business/supplier relationship is the best practice.
- Data Mining
- Data Warehousing
- Data Abstraction and Data Independence
- DBMS | Data Replication
- Data Mining | Sources of Data that can be mined
- Data Mining | KDD process
- Data Warehouse Modeling | Snowflake Schema
- DBMS | Row oriented vs. column oriented data stores
- Star Schema in Data Warehouse modeling
- Dimensional Data Modeling
- Characteristics and Functions of Data warehouse
- Data Warehouse Architecture
- Difference between Structured, Semi-structured and Unstructured data
- DBMS | Data Marts
- DBMS | Data Management issues in Mobile database
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