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 Taking an Information-Centric Approach to Managing Database Applications

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Number of posts : 1850
Age : 55
Registration date : 2008-03-08

PostSubject: Taking an Information-Centric Approach to Managing Database Applications   Sun 13 Apr - 19:48

Database growth pits two competing IT priorities against each other, the database administrator who needs multiple copies of the production system for testing and quality assurance (QA) and the storage administrator, who is working to manage his storage environment against growth, availability and consumption priorities.

Database applications, unlike email and file servers, require multiple copies of production to support test and development efforts such as creating versions for patch, test, QA, training and possibly a stand-by copy for disaster recovery purposes. On average, for every production application, IT makes eight copies for production support. As the production database grows, so do all the copies, consuming large quantities of storage. When an application or database needs to be upgraded, additional copies are required to reduce risk associated with the upgrade process. Many times, the need for storage by the database administrators (DBAs) exceeds the allocated storage and storage consumption forecasts. CIOs and IT directors struggle to reduce costs of infrastructure while keeping mission-critical database applications online, operational and current.

When DBAs or application development teams submit requests for additional storage, storage administrators and storage budget owners need to comply while keeping within budgets. If the database grows at a faster rate than originally planned, storage administrators may not be able to deliver the required storage to properly complete the tests. The storage problem is multiplied by the number of production applications a data center manages. On average, IT data centers manage at least six mission-critical applications. Multiply the number of applications by the number of copies (6 apps x 8 copies = 42 total) to meet the storage requirement, plus the servers required to support each copy, and the power to support the entire infrastructure, it is no surprise that more than 70 percent of IT budgets are allocated to the database applications even though only 20 percent of the production data is database data. Analysts estimate that email and unstructured content represents approximately 80 percent of production data.

DBAs are trained to understand the database and application development tools and become proficient in skills such as structured query language (SQL), application development tools, performance-tuning queries, database layout and database-specific backup, restore and recovery tasks. Storage administrators are trained to understand storage platforms, the differences between network-attached and storage area network-attached storage, fiber channel versus serial ATA drives, RAID groups and LUNs. There is very little overlap in content between the two disciplines. Resources proficient at both databases and storage skills are uncommon and are highly sought after. To address these issues, storage providers are improving resources and documentation on best practices for managing databases, and database independent software vendors (ISVs) are improving storage-aware features, reducing storage management overhead. The gap is getting smaller, but it still has a long way to go.

Storage Administrator Challenges with DBAs

Databases consume large volumes of storage.
Storage utilization rates are poor.
Mismatch between storage requirements and database requirements introduce unnecessary costs.
No common language between the storage administrator and the DBA makes it difficult to properly communicate with each other.

Taking an information-centric approach as part of a comprehensive information lifecycle management strategy to managing database applications can improve the communication between the DBA and storage groups, resulting in significantly lower costs and improved operational efficiencies. Specific information-centric strategies that can directly improve communications and lower cost are database classification for production, test copies and tiered storage.

Classify Information by Business Need

The information-centric approach begins with IT and DBAs both collaborating with the business users to classify business information based on the value to the business and any data retention policies that need to be adhered to. As the value of the information becomes less critical or ages beyond a data retention period, the data can be moved to an infrastructure with different service levels or deleted/purged. Without applying an information-centric strategy to the production data, all information is classified into a single category.

Define Test and Development Requirements

For test and development processes, application developers and DBAs can extend the data classification to define better requirements for what data sets are required for the specific test and development processes. It is not always required to have a full copy of production to perform tests and support new feature development. In this case, classifying test and development data by the test and development requirements can reduce the volumes of data used in the test and development processes.

For example, take a 1TB production database with seven copies, totaling in 8TB of storage capacity. Without using an information-centric approach, all data would be classified into a single category and treated the same from the storage administrator’s perspective. After collaboration between the business users, application developers DBAs, and storage administrators, approximately 600GB of production data was classified as aged or archive- eligible data. If this data is relocated from the production environment to an online available, active archive, the production can be reduced by 60 percent. Further evaluation of the test and development strategy identified that only two copies of production need to be full copies – standby and a reporting instance. All of the other copies could be reduced in size by only copying subsets of data. Reducing the data volumes on these copies further reduced the total storage requirement by 72 percent.
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