DB2 management, tutorials, scripts, coding, programming and tips for database administrators
For most companies, IT-related hardware and software maintenance is costly, time-consuming and requires hiring and retaining a support staff of operating system and database management system specialists. Delegating these responsibilities to an outside firm allows a new application to be developed and implemented more quickly. However, there are issues with delegating database support to an outside service. In this article, we will concentrate on how these environments scale with application growth, especially if the data and services are stored in the cloud.
Vendors now offer Database as a Service (DBaaS) as part of a bundled solution of managed IT services. Delegating database management services to an external provider may have many benefits, but delegating database administration services has many hidden dangers. In this article, we focus on how delegating data modeling to an outside service can cause problems when making application updates, changing business rules or doing performance tuning.
The Database Administrator (DBA) is usually a technical professional who supports one or more hardware and software platforms that provide application solutions. However, technical details such as SQL tuning, hardware and software upgrades, and database designs tend to be tactical in nature. It is essential that the DBA also maintain a strategic outlook to get ahead of potential problems. Lockwood Lyon addresses two of these strategies: knowing application breaking points and preparing for future enhancements to big data applications.
Big data applications were once limited to hybrid hardware/software platforms. Now, recent advances are allowing applications like these to be integrated with and federated into operational systems. In particular, IBM's DB2 for z/OS Version 12 delivers new features and functions that allow the DBAs to design, define and implement very large databases and business intelligence query platforms that fulfill some big data expectations.
Your big data application needs regular extracts from your production systems. While many best practices exist for big data extract, transform and load (ETL) processes, we sometimes forget that these data-intensive procedures can affect the operational environment’s performance. Here are some suggestions to mitigate such performance risks.
Big data applications start big and keep growing. As the masses of big data being analyzed grow both in size and complexity, the hardware and software communities have responded with huge storage offerings and massively parallel storage and retrieval mechanisms. The logical next step is for the IT enterprise to take advantage of these technological innovations for things other than classical big data processing.
While the performance of a mission-critical application sometimes takes center stage, IT staff are always aware that the performance of the overall system affects all applications. Database management systems, operating system software, physical data storage and retrieval, and backup and recovery operations all take part in providing a solid infrastructure for applications. For many customer facing applications, such as on-line transaction processing, the database management system is the key component.
Assume you’ve been given a directive by IT management to "tune" things. What strategies are available and how do you judge which ones are best? Considering the current economic climate, can any be implemented on a shoestring budget? Can any of these tasks be assigned to resources with basic DBA skills and still be productively accomplished?
As budgets tighten, how can database administrators keep up with the rapidly-changing new technology environment? The solution: automation. In this article we discuss various ways to automate common processes and relieve the infrastructure staff from repetitive tasks, so that then can concentrate on more urgent priorities.
Early data warehouse implementations began as collections of financial and customer data that accumulated over time. Modern warehouses have evolved into complex and elegant enterprise analytics platforms, hosting a broad collection of multiple data types, queried by advanced business intelligence software. As the warehouse environment becomes more valuable, capacity planning becomes critical. In this article we present several strategies for managing data warehouse capacity planning and performance tuning.
Lockwood Lyon presents simple SQL statements that the database administrator (DBA) can execute against the DB2 catalog to determine if your DB2 subsystem suffers from common maladies, and what the DBA can do to fix or mitigate potential problems.
Despite the sophistication of the latest DB2 software versions and the power of current IBM z/server technology, it is still possible for performance and data availability to deteriorate due to a variety of things, including increased dataset extents, loss of clustering, index page splits, and other factors. This article presents simple SQL statements that the database administrator (DBA) can execute against the DB2 catalog to determine if one or more application databases suffer from common maladies, and what the DBA can do to fix or mitigate potential problems.