Two Clouds, One Country: The Data Pipeline Problem Behind India’s Infrastructure Boom

A growing number of Indian fintechs and insurers now run on two cloud providers, and redundancy is rarely the main reason. Usually a regulator asked where the data lives, a procurement team wanted pricing leverage, and an acquired company arrived with its own stack. The second cloud shows up for business reasons. The engineering bill shows up later, mostly in the pipelines connecting the two.

Localization Draws the Map

For payments companies, the starting point is the Reserve Bank of India’s 2018 directive requiring payment system data to be stored only in India. That one rule shapes a lot of architecture.

Disaster recovery can’t simply replicate to a region in Singapore. Both the primary and backup copies have to stay inside the country, which in practice means pairing regions such as Mumbai with Hyderabad, Pune, or Delhi, depending on what each provider offers. Several global providers now run two Indian regions, but not every service is available in both, and teams sometimes find a managed database or analytics feature in one region that has no equivalent in the other.

The Digital Personal Data Protection Act adds consent and handling obligations for personal data generally. It allows most cross-border transfers by default, but sector regulators can impose stricter rules, so a lender, an insurer, and a retailer may face different constraints on the same kind of data.

Pipelines That Cross Providers

Moving data between two clouds costs money in a way moving it inside one cloud mostly doesn’t. Egress fees apply when data leaves a provider’s network, and a nightly job that copies transaction tables from one cloud to a warehouse on the other pays them every night.

Teams often respond by keeping full copies on both sides, which doubles storage and creates a new problem: the copies drift. A column renamed on one side breaks a downstream report on the other, and nobody notices until a regulatory submission comes out wrong or late. In reporting that runs on a fixed deadline, a failed overnight job at 3 a.m. turns into a scramble before the morning cutoff.

The more sustainable approach is deciding which cloud holds the system of record for each dataset and moving only aggregates or filtered extracts across the boundary. It’s less flexible for analysts, who sometimes want raw data in both places, but it keeps costs and failure points under control.

Where Prophecy Fits

Indian banks and insurers employ large analyst teams, many trained on desktop data-prep tools and spreadsheets. Getting their work onto governed cloud platforms without rewriting everything by hand is a real bottleneck.

Prophecy targets that gap. Its AI agents generate data pipelines from plain-language requests, then show them as visual workflows that run as SQL or Spark code on the major cloud data platforms. An analyst can see each join and filter, check it against the data, and refine it, while the data engineering team keeps control over what reaches production. Recent releases added a review step that shows every change an agent makes before approval.

That review step matters in regulated work, where a pipeline that quietly duplicates rows can misstate exposure figures. The tradeoff is cost and fit. It makes the most sense for organizations with many analysts building pipelines on a cloud data platform, and much less for a small team with one database and a handful of reports.

Managing More Than One Cloud

Running two providers means two consoles, two identity systems, two billing models, and two sets of security alerts. Multi-cloud management platforms promise a single view across all of it, typically covering cost reporting, resource inventory, policy enforcement, and compliance checks.

They help most with cost and governance, the questions finance and audit teams ask, like how much each business unit spends and whether any storage bucket sits outside an approved Indian region. They’re weaker at day-to-day engineering, because they tend to support the features both clouds share and lag behind provider-specific services. Engineers usually end up back in the native consoles to fix things.

A reasonable middle ground is to use a management layer for visibility and policy, and accept that each cloud still needs people who know it well.

Every Boundary Has a Bill

India’s data center growth makes it easier to keep data in the country. It doesn’t make it cheaper to move data between providers, or simpler to keep two copies in agreement. Companies that treat each crossing between clouds as a deliberate decision, with an owner and a cost attached, tend to end up with fewer pipelines and fewer surprises at audit time.