May 9, 2007
The Next Generation of Enterprise Data Warehousing
By Jürgen Haupt, SAP AG, Regional Implementation Group (RIG), EMEA and Dr. Michael Hahne, SAND Technology
Recent technological developments have made it possible to overcome – or in the cases where the data explosion has not yet “hit”, to proactively avoid – the challenges described above. Specifically, SAND Technology, an SAP® Software Partner, has successfully integrated SAND/DNA 2.3 with SAP NetWeaver BI as a nearline storage component. The SAP Certified Integration of SAND/DNA enables customers using SAP NetWeaver BI to implement an efficient nearline repository that can keep massive volumes of less frequently used data (as held in DataStore Objects in the Data Acquisition layer and the Data Propagation layer of the EDW) in a tiny footprint, in a format that permits rapid, easy access. Based on practical experience in the production environments of several customers using SAND/DNA with SAP NetWeaver BI (both with SAP Business Information Warehouse Release 3.1/3.5 and with SAP NetWeaver BI 7.0), it is realistic to expect between 85% and 95% compression, depending on InfoObject type, data volume and data diversity.
March 1, 2007
Near-Line Storage Keeps Static Data Available – SAP NetWeaver Magazine
By Michael Hahne
Near-line components allow the data warehouse to scale cost-effectively to hold many TB — even petabytes (PB) — of accessible data. However, near-line data storage doesn’t necessarily replace archival storage. For one thing, it may not qualify as a compliance-certified “point of record” creation for original data (see “Meeting Compliance Regulations” below). In some cases, the data stored in near-line storage is not in its original form, but is a representation or transformation of the original data. You still need archival, certified storage to keep the originals and to guarantee the retention of old data that you’re unlikely to ever require for reporting or analysis again. In these cases, it’s appropriate — and may be less expensive — to use archival storage instead of near-line storage.
February 7, 2007
Data warehouse for AOL Germany built on SAND
By Hannah Smalltree
Every night, the different source systems push data to a staging area. An extract, transform and load (ETL) tool with embedded data quality checks (from Lexington, Mass.-based Ab Initio Software Corp.) then delivers cleansed data to the SAND Analytic Server. From there, Türling’s team uses a layer of front-end analysis tools, such as Business Objects and SPSS, to create rich customer profiles and support marketing campaigns. Creating enriched customer profiles with accurate, up-to-date and complete data has enabled sophisticated segmentation and propensity modeling, Türling said.Read More…
January 30, 2007
Techworld.com – Ninety percent data compression at VW – and it’s not de-dupe
The ability of SAND/DNA to compress selected data to an extremely high degree (approximately 90 percent on average) while making it available for use in reporting or as the basis for new DataStore objects or InfoCubes was the key factor in Volkswagen Financial Services’ decision. The low total cost of ownership, due to the need for far less administrative support as compared with standard archiving solutions, was also very appealing.
January 30, 2007
Volkswagen to Implement SAND/DNA Storage for SAP – TMCNet
By David Sims
The first customer worldwide to use the newest version of the SAP NetWeaver product, Volkswagen Financial Services, chose SAND/DNA for what SAND officials describe as “maximum nearline functionality enabling the company to store and effectively manage its rapidly growing volumes of data.”
January 8, 2007
NCR Exits the Data Warehouse Market: See You Later, Teradater? | ZDNet.com
By Joshua Greenbaum
Finally, there’s the third way, data archiving. One of the smarter ways to save a bundle on the Teradata model is to off-load your non-essential data warehouse data (i.e. most of it, in most cases) into an archival system that, if well-designed, can be orders of magnitude cheaper and easier to manage as well. One good example of this is SAND Technology’s data-archiving solution. Again, the watchwords are faster, better, cheaper. And, in this case, SAND offers a reasonable solution to the sanitary landfill problem: dump the data in a cheap and highly acessible archive, and at a minimum stop paying the big bucks to store information you may never need to user.
December 13, 2006
SAP INFO: From 650 to 25 GB in One Go: RI Solution moves SAP data to nearline store:
By Michael Hahne
The IT service provider RI Solution GmbH has achieved a compression rate of 96 percent for its data in SAP NetWeaver Business Intelligence. Thanks to nearline storage, the company is saving on archiving costs and at the same time has lower administration expense. In addition, users can access InfoCubes and PSA objects without any loss in performance.
November 21, 2006
Enterprise Systems | Nearline Solutions: Reducing Data Storage Infrastructure Costs
By Jerry Shattner
In an age when the data volumes handled by organizations are growing exponentially, it is becoming impossibly expensive to maintain all enterprise data in a traditional “online” data warehouse while providing adequate performance and service levels to users. Implementing a nearline component is an ideal way to relieve the main warehouse of the burden of data that is rarely changed, while still keeping it readily available for access when required. A nearline solution will ultimately enable you to greatly reduce expenditures on storage infrastructure, not simply by offering high levels of data compression, but also by reducing the amounts of data that need to be replicated or moved across the network in the course of normal warehouse operations.
September 29, 2006
Driving Business Value with ILM-Enabled Database Archives – DM Direct
By Jerry Shattner
Use of column-based data compression technology allows for storage of relational data in what is essentially a pre-indexed format, alleviating the requirement for storing or building indexes at restore time. This design significantly reduces the overall storage needed for the database. Column-based storage also significantly improves data compression: being made up of a single data type, each column of data can be compressed much more efficiently than rows of data, which by definition include many different data types. This technology can also further reducing the data footprint by selecting the best optimized compression strategy for each data type.
August 23, 2006
Jerry Shattner, EVP, SAND Technology – B-eye Network
Jerry Shattner discusses how SAND/DNA enables organizations to store multiple terabytes of data and access it quickly without impacting current infrastructure.