data · open source
BI Dashboards & Analytics with Databricks
BI Dashboards & Analytics built on Databricks, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 7
Why Databricks for this
The AI layer is genuinely useful here: a director can ask the follow-up question rather than filing a request and waiting three days for an analyst.
Databricks is strongest at one platform covering data engineering, analytics and machine learning. For bi dashboards & analytics that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: heavier than most mid-market workloads need. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
We hand over with runbooks, tests and a team that knows how it works, not a dependency.
The honest assessment
- What it is
- Unified analytics and ML platform on the lakehouse model.
- Strongest at
- one platform covering data engineering, analytics and machine learning
- Trade-off
- heavier than most mid-market workloads need
- Category
- data
We are not a reseller for Databricks and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Metric definitions agreed and documented once
- A semantic layer so numbers cannot diverge by report
- Dashboards designed for decisions, not for decoration
- Scheduled distribution to the people who need it
- Natural-language follow-up questions over the same data
- Usage tracking so unused dashboards get retired
Questions
Which BI tool do you use?
Power BI, Metabase, Superset or a custom build, chosen on your licensing, team skills and how much customisation you need. We are not tied to one vendor.
Why do our reports disagree?
Almost always because the same metric is defined differently in different places. A semantic layer with one agreed definition fixes it structurally rather than report by report.
Can non-technical staff ask their own questions?
Yes, natural-language querying over the governed semantic layer, so answers stay consistent with the dashboards.
Alternatives for bi dashboards & analytics
Same capability, different stack. Each page states its own trade-off.
Building with Databricks?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878