Popular Data Warehouse Technologies
Interestingly, data warehousing requires simple technology features than the previous data-driven systems. For instance, DWH does not require online updating and demands minimal locking needs. This section delineates the technological requirements and features for the DWH.
Data Warehouse Tutorial Video
Manage large amounts of Data
As discussed earlier, this era of big data finds data warehouse as the ultimate solution to handle and manage large volumes- terabytes and petabytes of data. Data warehousing technology provides the ability to deal with outsized and huge volumes in rapid and simple manner. This data management can be performed in several ways depending upon the technological requirements of DWH. Some of the ways can be through flexibility of addresses where the data is stored, indexing, data extensions, efficient management of data overflow and alike. For a technology to be fault-tolerant, it should maintain efficiency along with large data storage. Thus, the requirements for both volume and efficiency must be met by the technology- data warehousing is one such example.
Management of Multiple Media Files
Together with managing huge data with efficiency and cost-effectiveness, DWH supports management of multiple storage media files. While it is inadequate to manage a developed data warehouse on Direct Access Storage Device (DASD) alone, the technology provides a storage hierarchy with respect to the access speed ad storage cost.
One of the most important features of the DWH is the flexible and unpredictable data access. The Data Architects put in lot of efforts to make the data flexible by spreading it across various storage media and performing partitioning. This, in turn, encompasses quick and efficient indexing of data. Secondary indexing, temporary indexing, sparse indexing are few techniques supported by the DWH.
Similarly, data monitoring is one significant technology benefit data warehousing provides. The DWH monitoring identifies what data has and has not been used until now. It plays a major role:
- when the DB requires a reorganization of data
- when an index gets poorly structured
- when there is excess data overflow
- if there is available remaining space.
Interface to Many Other Technologies
The data passing in a data warehouse is flexible and done through a wide variety of technologies. From the operational environment to data marts to DSS applications to data mining warehouses to alternate storage, the data is passed to each of these technology interface from the warehouse.
Another important technological feature supported by warehousing is the rich and robust language specification. Programmers must be able to use simple and easy-to-understand languages inside the data warehouse. The language interface should support:
- access of a dataset one at a time
- access a data record at a time
- use of one or more indexes satisfying a query
- SQL interface
- insertion, updating and deletion of data
Data warehousing supports many different languages to handle queries , access data and to optimize graphic nature of the interface.
Effective and Efficient Data Loading
Data warehousing is capable of efficient data loading, especially for large data warehouses. The two major methods of data loading includes one record at a time through language interface or by means of a utility. The latter loading method is relatively faster. Similarly, data warehousing also supports efficient loading of indexes at the time of data loading.
Since there is a huge volume of data to be loaded, it is divided into various job streams, and each stream is independently executed. This reduces the elapsed time during loading.
There is yet another significant must-have technology that the DWH follows – ETL. The data is staged before it is passed on to ETL layer. Remember, data staging is performed when you have large volumes of data and the processing complexity if high.
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Besides successful creation and loading of indexes, DWH also requires efficient access of indexes. It can be achieved by using bit maps, storing all or parts of an index in main memory, consisting of multileveled index, creating a range of indexes and others.
Needless to say, working with such massive quantities of data urges the need of data compaction. The data occupies lesser space and allows efficient access of data. The compaction is preferable as the data once inserted is seldom changed/updated in a warehouse.
DWH is intelligent enough to allow processing of a service request by simply seeing the index instead of moving to the primary data source. It looks for data in the indexes if such a request is formulated.
Let’s compare it with other technology as well. Check out Data Warehouse vs Database Blog to do so.
DWH enables faster restoring of a table from non-DASD storage. The ability of restoring from secondary storage is highly preferable as it gives you better and bigger savings. It should be able to restore both full and partial databases.
Many other technological advancements provided by DWH include Referential Integrity, Page/Row level locking, high-speed buffering, data VIEWs, Transaction Integrity.
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