DB2 management, tutorials, scripts, coding, programming and tips for database administrators
Today most of the tasks done by traditional DBAs are performed by artificial intelligence systems or the database itself. With so few important tasks left to perform, are DBAs really needed anymore?
Big data as an application (or as a service) is being supplanted by artificial intelligence (AI) and machine learning. Few new requirements for a big data solution have arisen in the past few years. All the low-hanging fruit (fraud detection, customer preferences, just-in-time re-stocking and delivery, etc.) have already been big data-ized. Is this the end of big data?
IBM has enhanced its Operations Analytics software on the IBM Z platform by leveraging IBM Watson Machine Learning (WML) for z/OS. WML is implemented as a service that interfaces with several varieties of data, creates and trains machine learning models, scores them and compares the models with live metrics. The operations analytics software then classifies and analyzes the results to detect and predict problems. Read on to learn more!
Your production database environment stores personally identifiable information in an encrypted form, limits access to these fields and masks them when displayed. However, some types of data encryption create potential performance problems as well as security issues. Your data may be at risk unless you mask it effectively.
IBM’s machine learning is being used to improve the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your business is prepared to consume these ML and AI conclusions. Learn more…
A look at ten top databases fighting for mind-share in 2019.
The IT enterprise has grown, databases are bigger, companies have more product lines, more services, more customers and more transactions. Business analysts used to be satisfied with subsets of only certain data elements for their queries, and now they want it all. Now they want more....
As big data solutions grow in size and complexity, concerns can rise about future performance. One way to assess the potential effects is to measure your big data application’s health.
Until recently, business analytics against big data and the enterprise data warehouse had to come from sophisticated software packages. This was because many statistical functions such as medians and quartiles were not available in basic SQL, forcing the packages to retrieve large result sets and perform aggregations and statistics locally. Today, many database management systems have incorporated these functions into SQL, including IBM's flagship product, Db2.
A new separately-priced software offering from IBM on z/OS systems uses machine learning and artificial intelligence to assist the Db2 optimizer in choosing high-performance access paths for SQL statements.
Db2 Version 12 includes many new and improved features specifically aimed at improving application performance. As today's development teams are driven to implement applications at a faster pace, the DBMS must support the ability to retrieve data quickly, while at the same time reducing overall resource usage. Here is a detailed look at some modern transactional data processing issues and how Db2 meets these challenges.
IBM’s Db2 Version 12 for z/OS was designed to synergize with new IBM z14 hardware, which includes several new and updated options for hardware-assisted data encryption and compression. These features can be used by the database management system to store and retrieve encrypted and compressed data transparently without application knowledge or intervention. Read on to learn more.
IBM now provides an option to configure its Db2 version 12 for z/OS and complementary IBM Db2 Analytics Accelerator (IDAA) to permit concurrent transactional processing of operational data with analytics processing of data in the appliance. This new feature, zero-latency HTAP (hybrid transactional analytical processing) provides a patented replication process that propagates native Db2 table changes to the IDAA data store. This then allows BI queries to act on up-to-date data.