- Usage Scenarios
- Decision Criteria
- Vendor Analysis
- Key Takeaways
- About William McKnight
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
This report is the third in a series of enterprise roadmaps addressing cloud analytic databases. The last two reports focused on comparing vendors on key decision criteria that were targeted primarily at cloud integration. The vectors represented how well the products provided the features of the cloud that corporate customers have come to expect. In 2017 we chose products with cloud analytic databases that exclusively deploy in the cloud, or had undergone major renovation for cloud deployments. In 2019, we updated that report with the same vendors. This report is an update to the 2019 Enterprise Roadmap: Cloud Analytic Databases. However, this time around we have new vendors and a new name.
We’ve reviewed and adjusted our inclusion criteria. We’re now targeting the technologies that tackle the objectives of an analytics program, as opposed to the means by which they are achieving these objectives. These days many believe the best vessel to be a data lake/cloud storage (not necessarily in data page/relational-like format). And many are finding ways to join the relational database with the lake as a “lakehouse,” treating the data lake as external tables. We have included here viable solutions that don’t have a traditional data warehouse.