data · open source
Predictive Analytics & Forecasting with Databricks
Predictive Analytics & Forecasting built on Databricks, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why Databricks for this
Explainability is not a compliance checkbox here, a planner who cannot see why the forecast moved will override it, and then the model may as well not exist.
Databricks is strongest at one platform covering data engineering, analytics and machine learning. For predictive analytics & forecasting 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.
Six weeks to something running in production, not six quarters to a strategy document.
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
- Data audit before any modelling, with gaps reported
- Baseline model so improvement is measurable
- Error bars and confidence intervals on every forecast
- Feature importance you can explain to the business
- Backtesting against held-out historical periods
- Monitoring for drift once live
Questions
How much history do you need?
Generally two to three seasonal cycles for demand work, less for churn or risk scoring. The data audit in week one tells us what is realistically achievable with what you have.
How accurate will the forecast be?
We report error against a naive baseline on held-out periods. If the model does not beat the baseline meaningfully, we say so rather than shipping it.
Can the business understand the output?
Yes, feature importance and driver explanations are part of the deliverable. A forecast planners cannot interrogate is a forecast they will override.
Alternatives for predictive analytics & forecasting
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