Product Updates & Newsletters
Every edition covers what we shipped, what we learned, and what is coming next.
A low-volume feed for data architects, platform owners, consultants, and the business-leaning members of data product teams. Written by the team building NextTables, sent only when there's something worth saying.
You can expect:
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New features, improvements and bugfixes
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Design patterns from real setups: validation logic, row-level access, embedding choices
- Builder playbooks for scoping, modeling, and rolling out data apps that stay maintainable
- Cross-platform notes on what works across SAP Business Data Cloud, Databricks, and more.
- What the team is working on, and where the product is headed next
Releases, use cases, and best practices
What's new
Every release worth your attention: new features, new connectors, and the small tweaks that change how the grid feels. Items that are still coming are clearly marked Coming Soon.
Patterns from real setups
How real teams design their data apps in production: validation logic, row-level access, embedding choices, cross-platform reference data. Written up so you can borrow what works.
Builder playbooks
Practical guidance for designing, modeling, and rolling out data apps on enterprise data platforms. How to scope a first app, where to put validation, how to keep things maintainable as the number of apps grows.
Frequently asked questions
We aim for one edition a month, more when we ship a major release, less when there's nothing meaningful to share.
Members of the NextTables product team. Each edition is written by people closer to what's shipping than to marketing fluff. We aim for substance: design patterns, real setups, what we've learned.
If you're not yet a NextTables customer, our team may occasionally reach out separately, about a relevant case study, an event, or a conversation we think you'd find useful. You can unsubscribe from any contact at any time.
Absolutely. The patterns and best practices apply whether you're evaluating NextTables, scoping a first project, or just thinking about how data maintenance should work on enterprise data platforms like SAP Business Data Cloud (Datasphere), Databricks and others.
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