Two Vendors Make a Stack. Three Parties Make It Work.

A delivery model with three named seats: Qlik provides data movement, quality, governance and analytics; a cloud data platform such as Databricks, Snowflake, AWS or Azure provides storage, compute and scale; and Artha is the implementation partner accountable for the combination working in your environment. The point is that the third seat is explicit and contractual, not assumed.

Vendor services teams are good at their own product and stop at its edge, which is rational: their mandate ends where the logo does. Programmes fail at the joins between products and inside your estate's specifics, source systems, compliance posture and operating model. An implementation partner whose only product is the outcome owns exactly those joins. We also bring both vendors in where their depth is needed, rather than instead of it.

The workload decides, not the brochure: existing cloud standards, data gravity, latency, residency, the skills you already pay for and the economics of the query patterns you actually run. Artha is not paid on either licence line, so the recommendation is free to follow the evidence, and hybrid answers, such as Snowflake for governed serving with Databricks for ML, are legitimate outcomes rather than failures to decide.

That is the most common starting point. The trio forms around your incumbent platform: Qlik supplies the movement, quality and governed data products the platform does not, and Artha integrates the two with your sources and controls. Nothing about the model requires a platform change, and the first engagement usually improves what the existing platform receives rather than replacing anything.

Artha, in writing, for the delivered solution: incidents come to us first, we run diagnosis across the whole chain, and we drive vendor escalations with evidence when the fault is in a product. What the model removes is the triangle where each party's first move is establishing that the problem belongs to someone else. You keep one throat to choke, chosen deliberately.

Through the partner channels attached to our credentials: Qlik Elite Channel Partner status on one side, and platform partnerships including our Databricks and Snowflake practices on the other. Tickets go in with reproductions, environment detail and business impact already assembled, which is the difference between a fault fixed on a product timeline and one that ages in a queue.

No. It is a delivery stance, not a bundle: you contract licences with the vendors on whatever commercial terms suit you, and Artha's engagement is the architecture, implementation and operation of the combined estate. Where our channel status helps commercially we use it transparently, but the model's value is accountability at the joins, not margin on the paper.

Underneath them, and the seams still need owning: Databricks and Snowflake run on the hyperscalers, Qlik Open Lakehouse runs in your own AWS account, and networking, identity, keys and egress costs live at that layer regardless of which logo is on the warehouse. In practice the trio is sometimes four parties, and the accountability model is exactly why that does not become four directions.