For enterprise managing database is a challenging task and is used mostly for mission critical activities. These activities include data analysis, supporting enterprise data warehouses which are massive in size, supporting complex queries and are expected to deliver responses to those queries within seconds of time. The database administrators are required to constantly change data storage, allocate parameters and use techniques to boost parameters. This process also involves applying software up gradation and fix patches which are time consuming.
Organizations are constantly working to develop features and functions that are designed to lessen the burden of these activities. Most of the modern RDBMSs have developed features and functions which run faster, require less tuning and optimize the database internals. Another aspect is meeting the SLA for database that requires a set of skills and any mistakes can cause poor performance and unscheduled downtime. Many such approaches to avoid downtime include inmemeory columnar structure have helped but not entirely addressed the problem.
Most common approaches to Automating Database Management
One common approach is optimizing database internals so that it simply run faster. This is what major RDBMS vendors do to solve their problems. This includes RDBMS to have software that analyses its own structure and provides regarding tuning. Even RDBMSs that are called “autonomic” usually require DBA to take tuning action with human involvement for judgement.
Many leading technology firms have been using machine learning (ML) tools over the past few years to improve areas such as image recognition and programmatic advertising, product and story recommendations and now IT and database operations.
Supervised machine learning includes incorporating AI and automation into business process for competitive advantage while boosting efficiency .This is done by eliminating manual process. Using intelligent technologies and digital process to improve IT operations are now most sought after by leading tech firms. For better decision making most of the firms are augmenting intelligence and in doing this they are automating routine and learning for better profitability.
Oracle has recently announced that Oracle Autonomous Database now includes Oracle APEX 19.2. Oracle APEX which is a low-code development platform for rapidly building opportunistic and productivity applications.
Oracle APEX includes all of the capabilities and infrastructure required to build apps, such as native database integration, session state management, responsive design, accessibility, security, and more so that developers can concentrate on solving business problems instead of hand-crafting large amounts of code. This enhances developer’s productivity and increase out of box capabilities.
Oracle APEX 19.2 features includes:
- Extended declarative capabilities with Shared LOVs;
- Brand new Team Development to make collaboration simpler and easier; and
- Ability to drag and drop a spread sheet, JSON or XML file into an existing table.
- Developers can build even better applications faster as it is fully managed cloud service and low-code development platform. This enables developers across a wide skill set to concentrate on solving business problems and quickly delivering enterprise-scale solutions.
The new feature in Oracle APEX 19.2 is Faceted Search that allow end-users to narrow down large datasets to a more manageable size by simply selecting different criteria. Many commercial web sites include faceted search interfaces; however, they are generally very difficult to build, are inflexible, and even harder to maintain.
Oracle APEX 19.2 enables the easy creation of faceted search reports on any dataset, which can be maintained declaratively.