Data Platform Development Company
FISTA Solutions builds data platforms that produce numbers people trust: reliable ingestion pipelines, a warehouse or lakehouse sized to your workload, a governed metric layer so definitions live in one place, quality monitoring with alerts, and the foundations AI features depend on.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does data platform development include?
Data platform work covers source ingestion with schema handling, warehouse modeling, a governed metric or semantic layer, orchestration and lineage, quality tests and monitoring, and access control including row and column security.
- 01
Ingestion pipelines
Reliable extraction with schema change handling, backfills, and idempotent loads.
Ingest - 02
Warehouse modeling
Modeled layers from raw to consumable, with naming and grain documented rather than implied.
Model - 03
Governed metrics
One definition per metric, versioned and reused by dashboards, reports, and AI features alike.
Semantics - 04
Quality and monitoring
Freshness, volume, and validity tests with alerting and ownership, so problems surface before reports do.
Quality - 05
Access control
Row and column-level security enforced by the warehouse against the asker's identity.
Security
Requirements
Which requirements shape data platform development?
Data platforms fail on trust rather than technology: contradictory numbers, stale data, and unexplained pipeline failures. Requirements centre on governed definitions, tested quality, visible lineage, and cost control as volumes grow.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Definition governance | Contradictory numbers destroy trust. | One governed definition per metric, versioned, with dashboards and AI features inheriting rather than redefining. |
| Data quality | Silent quality failures poison decisions. | Freshness, volume, and validity tests with alerting, ownership, and visible status for consumers. |
| Lineage | Consumers need to know where a number came from. | Column-level lineage from source to metric, with documented grain and transformations. |
| Cost control | Warehouse spend grows quietly. | Partitioning, clustering, incremental models, and per-team cost visibility with budgets. |
| Access control | Analytics can leak sensitive data. | Row and column-level security enforced by the warehouse against the asker's identity, not by dashboard filters. |
Where AI fits
Where does AI fit in data platform development?
AI depends on this platform rather than replacing it: governed metrics make analytics agents trustworthy, clean lineage makes answers explainable, and quality monitoring prevents AI features confidently reporting broken numbers.
- 01
Analytics agents
Natural-language questions answered through governed metrics with the query shown.
- 02
Pipeline triage
Failures explained with upstream context and a proposed owner rather than a raw stack trace.
- 03
Quality investigation
Anomalies correlated with recent changes and proposed causes for the data team.
- 04
Documentation
Tables, columns, and lineage documented from schema and usage, reviewed by owners.
Cost and timeline
How much does data platform development cost, and how long does it take?
Cost is driven by source count, data volume, and modeling depth; ongoing cost by warehouse compute and storage. FISTA does not quote blind: the scoping call returns an architecture, a cost model, and a phased estimate.
Warehouse spend is an ongoing operating cost that grows quietly. FISTA models it against your volumes and query patterns during design, and builds partitioning, incremental models, and budgets in from the start.
Scope by decision, not by source. Ingesting everything produces an expensive swamp; FISTA starts from the decisions the business needs to make and ingests what those require.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver custom software?
FISTA delivers custom software in four phases: a discovery sprint that turns goals into a specification with acceptance criteria, an architecture and data design with integration contracts, two-week builds with automated tests and weekly demos, and a verified release with infrastructure-as-code, runbooks, and monitoring.
- 1
Discover and specify
Workshops, process mapping, and system inventory produce a specification with acceptance criteria and a phased plan.
OutputSpecification, estimate, roadmap
- 2
Architect
Data model, service boundaries, API contracts, security, and infrastructure decisions recorded with trade-offs.
OutputArchitecture decision records
- 3
Build and demo
Two-week sprints with unit, integration, and contract tests; a demo on your environment every week.
OutputWorking increments in your repo
- 4
Verify and release
Performance and security testing against the spec, infrastructure-as-code, runbooks, dashboards, and handover or managed operations.
OutputVerified release with SLOs
Why FISTA
Why choose FISTA Solutions for data platform development?
FISTA builds data platforms where definitions are governed, quality is tested, and cost is modeled before volumes grow. Work is contracted through a US entity with full IP assignment.
Data Platforms specifics
- One governed definition per metric, inherited by dashboards, reports, and AI features rather than reinvented in each.
- Freshness, volume, and validity tests with ownership and alerting, so consumers know when data is trustworthy.
- Column-level lineage from source to metric, with grain and transformations documented.
- Warehouse cost is modeled and budgeted, with partitioning and incremental models applied from the start.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What buyers ask before a custom build.
Straightforward guidance for evaluating scope, fit, and the next step.
01Which warehouse should we use?
Postgres for modest volumes, BigQuery, Snowflake, or Databricks as scale and workload demand. FISTA recommends against your volumes, query patterns, existing cloud, and team skills, and records the trade-offs.
02Why do our dashboards disagree?
Almost always because each dashboard defines metrics independently. A governed metric layer, with dashboards inheriting definitions, is the fix, and it usually resolves long-running disputes between teams.
03Do we need a data platform before AI features?
For analytics agents and anything reporting numbers, effectively yes. Without governed definitions, AI features produce confident answers that contradict official reporting, which erodes trust quickly.
04How do you control warehouse costs?
Partitioning and clustering, incremental models, query patterns reviewed, result caching, and per-team cost visibility with budgets, modeled during design rather than after the first large bill.
05How long does a data platform take?
A focused first slice covering key sources and metrics typically takes a few months, with source access and definition agreement as the usual critical path.
Scoped in writing before you commit
Produce numbers the whole company agrees with.
Bring the decisions you need to support. The scoping call returns an architecture, a metric plan, and a cost model.