I can’t believe that it’s been so long since I blogged about the Best Practice papers! Kate Kurtz sent me four new ones today and I found that my last entry about this topic was posted April 2009: “Learn & Benefit from Others “. Here are the four newest ones:
Written by Matthias Nicola and Susanne Englert
Update Published January 2011
This paper provides principles and guidelines for using DB2® pureXML® to solve business problems effectively and to achieve high performance when managing XML data in enterprise applications. The examples illustrating the best practices are based on a real-world financial application scenario and demonstrate how to implement the guidelines. The examples can be easily adapted to other types of XML applications. The paper covers the following areas:
- Storage options for XML data to improve performance and storage efficiency
- Techniques for adding XML data into a DB2 database
- Techniques for querying and updating XML documents efficiently
- Techniques for using indexes over XML data with queries effectively
- Techniques for efficiently maintaining and monitoring an XML database
- Techniques for developing efficient pureXML applications
Written by Maksym Petrenko, Mike Winer, and Joyce Coleman.
Published February 2011
Summary: Data in a data warehouse can be classified according to its temperature. The temperature of data is based on how often it is accessed, how volatile it is, and how important the performance of the queries that access the data is. Hot data is frequently accessed and updated, and users expect optimal performance when accessing this data. Cold data is rarely accessed and updated, and the performance of the queries that access this data is not essential. Using faster, more expensive storage devices for hot data and slower, less expensive storage devices for cold data optimizes the performance of the queries that matter most while helping to reduce overall cost.
Learn about a strategy for managing a multi-temperature data warehouse by storing data on different types of storage devices based on the temperature of the data.
This article provides guidelines and recommendations for each of the following tasks:
- Identifying and characterizing data into temperature tiers
- Designing the data base in an IBM® Smart Analytics System environment to accommodate multiple data temperatures
- Moving data from one temperature tier to another
- Using DB2® workload manager (WLM) to allocate more resources to requests for hot data than to requests for cold data
- Planning a backup and recovery strategy when a data warehouse includes multiple data temperature tiers
The content of this article applies to data warehouses based on version 9.7 or later of DB2 for Linux®, UNIX®, and Windows®. All examples in the article refer to IBM Smart Analytics System and InfoSphere™ Balanced Warehouse® environments.
Written by Walid Rjaibi and Mark Wilding
Published February 2011
Summary: Public cloud computing is an emerging computing technology that uses the Internet and central remote servers to host data and applications. It allows consumers and businesses to use applications without installing them locally and access information from any computer with Internet access. Cloud computing allows for much more efficient computing by using centralized storage, memory, and processing. The benefits of cloud computing are clear, and so is the need to develop appropriate security for cloud implementations.
This article is important for all DBAs who are setting up or managing databases in a public cloud environment. The details and best practices in this article will help DBAs protect themselves and their companies from security leaks and exposures by applying a standard, high-grade security policy to all databases that are hosted in a public cloud.
The information in this article is organized into three main sections:
- The IBM data server security blueprint: The blueprint first positions data server security within the bigger enterprise security picture. This section also describes steps to develop and roll out a security plan.
- Threats and countermeasures: This section describes the most common threats that affect a data server, whether it is deployed in a traditional environment or a cloud computing environment. The section then recommends a set of countermeasures.
- Additional cloud-specific security challenges: This section examines the additional challenges posed to data security in a cloud environment, in particular the need for privileged user monitoring and data segregation.
Written by: Silvio Luiz Correia Ferrari, Marco Antonio Norbiato, and Joyce Coleman.
Published March 2011
Summary: The IBM Smart Analytics System family of offerings evolved from the InfoSphere Balanced Warehouse family of offerings. Both are based on the same storage and database design principles.
Learn about some of the frequently asked questions about system maintenance in IBM® Smart Analytics System environments and InfoSphere™ Balanced Warehouse environments.
The frequently asked questions are grouped into the following two categories:
- System administration
- Ongoing maintenance and upgrades
The questions and answers discussed here apply to several generations of IBM Smart Analytics System configuration. The article uses the term IBM Smart Analytics System except when referring to specific InfoSphere Balanced Warehouse configurations. Most content, however, applies to IBM Smart Analytics System configurations and InfoSphere Balanced Warehouse configurations. Unless otherwise indicated, all content applies to V9.5 and V9.7 levels of the InfoSphere Warehouse and DB2® software.
You can find all the best practice articles on the following website:
I hope you find this series of articles useful. I’ll keep the website bookmarked for updates and will blog as new articles are published.