Db2 administrators and data scientists can now also more easily explore new datasets in Db2 with a new tool called IBM Augmented Data Explorer. The tool combines natural language query capabilities and faceted search. As a result, developers can explore datasets using a search engine-like experience that combines written queries with natural language queries and auto-completion. Developers can now accelerate the exploration of new datasets without needing to know SQL while still using its power. Additionally, developers and data engineers can use the sophisticated capabilities of IBM Data Virtualization to focus on their work without having to worry about ETL, even if the data is spread across different hybrid data stores.
They can also use IBM Watson Studio to create cognitive applications in Db2 and train models whether the data is on-premises (Db2) or in the cloud (Db2 on Cloud) or both for a truly hybrid, Multi-cloud support.
Developers can get started quickly with code samples baked into Jupyter Notebooks. Going forward, data scientists will be able to use Db2 Graph for complex modeling and visualization, which will be released later this year.
Additionally, AI application free database software developers will no longer need to worry about tuning database performance. The next release of Db2, due out this spring, will feature a machine learning-based optimizer in addition to a proven cost optimizer for best performance. Db2 is also able to automatically manage resources and schedule their workload execution using Adaptive Workload Management.
In short, IBM Db2 is a database built for and powered by AI, making 2019 an exciting year for its users.
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