The Albany Research Center, now part of National Energy Technology Laboratory (NETL), is a U.S. Department of Energy laboratory staffed by Federal employees and contractors located in Albany, Oregon. Founded in 1943, the laboratory initially specialized in life cycle research starting with the formulation, characterization, and/or melting of most metals, alloys, and ceramics; casting and fabrication, prototype development; and the recycle and remediation of waste streams associated with these processes. Researchers at the laboratory routinely solved industrial processing problems by investigating melting, casting, fabrication, physical and chemical analysis and wear, corrosion and performance testing of materials through the use of equipment and analytical techniques. Since joining NETL, the laboratory has switched its research focus mainly to materials and processes for fossil energy production and conversion. The facility rests on 44 acres (18 ha) and occupies 38 buildings.S.
The ultimate measure of performance for any data structure is the speed in which data can be retrieved. In columnstore indexes, the time required to return data will be a function of two operations:
Analytic data, by its nature, can be large and challenging to maintain and can grow quickly over time. Similarly, its use increases with time as analysts and data scientists find more ways to crunch it. There is a great convenience to having analytic data in close proximity to its underlying transactional sources. Utility is also gained by choosing a location for analytic data that can withstand the test of time, thus avoiding the need for costly migrations if the data is unable to scale appropriately.
Any analytic data store requires the ability to perform data loads quickly and efficiently. Bulk loading is a reduced logging process that allows data to be inserted directly into a columnstore index. This not only bypasses the delta store, but results in a transaction size that reflects the compression of the target data, greatly reducing the amount of data written to the transaction log when this process is utilized.
Each compressed segment within a columnstore index not only stores analytic data, but through metadata can describe its contents with more precision than rowstore tables can.
Depending on its usage, a columnstore index may require no maintenance at all, infrequent maintenance, or regular maintenance to ensure optimal storage, resource consumption, and performance.
