We organised our work in 2018 into three phases. In the initial phase, we carried out a high-level feasibility study of ADWC and AAC, making sure the technology could manage the extreme demands of our IIoT systems and our complex analytics queries. In this phase, we also explored the flexibility of provisioning, as well as the ability of the technology to automate updates, backups, and patches.
The second phase was dedicated to the evaluation of various procedures for migrating the data from our current on-premises architectures to Oracle’s cloud services. In particular, we considered the complexity of the data format, partitioning, indexing, etc. This work made it possible for us to evaluate the initial workload and data-analysis performance on a representative subset of the data, helping us to gain insights into the advanced optimisation features of AAC. We were also able to use Oracle Hybrid Columnar Compression to reduce storage requirements to about a tenth of what they previously were, as well as reducing the requirement for full scans. Thus, the performance for data retrieval and analytics tasks was significantly improved. On top of this, the system offered transparent and automated access to Oracle’s “Exadata SmartScan” and “Exadata Storage Indexes” features. This reduced — or, in some cases, removed entirely — the dependency on indexes.
In the last phase, we also worked with AAC to offer seamless data analytics based on collaborative and interactive dashboards. Our most recent work focuses on elasticity and scalability. In particular, we are working to increase the data volume used to one terabyte and increase the complexity of the workloads and analysis.
This will lead to a comparison between the Autonomous Database’s capacities and other databases platform including the current on-premises setup.