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idataraya
idataraya

Platform Work.

The infrastructure your data team builds on.

We build the foundational layer that makes everything else possible: the compute, storage, orchestration, and access patterns your data team uses every day. Delivered as a durable platform that compounds in value over time.

  • Data platform architecture and implementation
  • Self-service tooling for analysts and engineers
  • Access control and governance frameworks
  • Cost optimization and capacity planning
data-platform · kl-westLive
  • Computeauto-scale
  • Storagetiered
  • OrchestrationDAG deploy
  • AccessRLS ready
DOMAIN · revenueDataset contract · fct_orders
Promoted
OwnerSLOLineage
platform · scaffold → deployCookiecutter → CI → prod
Running
ScaffoldcookiecutterCIlint · test · contractReviewPR gatesDeployDAG synced
42 pipelines · one pattern
catalog · analytics domainMaterialized
fct_ordersFactdata-eng team rows
dim_customerDim · PIIcrm team rows
mart_revenueMartfinance team rows
ml_features_v4Featureml team rows
482 datasets · owners on all

Infrastructure that makes your data team faster.

A data platform isn't a warehouse. It's the compute, access, governance, and tooling layer that makes everything else productive.

One pipeline pattern, every team uses it.

Templated orchestration with conventions for ingestion, transformation, quality, and promotion. New pipelines come from a scaffold, not a blank Airflow DAG copied from Stack Overflow.

platform · scaffold → deployCookiecutter → CI → prod
Running
ScaffoldcookiecutterCIlint · test · contractReviewPR gatesDeployDAG synced
42 pipelines · one pattern

Catalog, ownership, and lineage in one index.

Every dataset has a steward, a classification, a retention policy, and an upstream chain. Analysts find what exists before building a duplicate, and PII stays tagged end to end.

catalog · analytics domainMaterialized
fct_ordersFactdata-eng team rows
dim_customerDim · PIIcrm team rows
mart_revenueMartfinance team rows
ml_features_v4Featureml team rows
482 datasets · owners on all

Spend you can explain per team, per pipeline.

Compute tagged by workload and owner. Idle detection, spot-vs-reserved recommendations, and anomaly alerts so the monthly bill matches the monthly plan.

Spend · week to date
data-eng · computeSLO RM 3,600RM 2,840
analytics · warehouseSLO RM 2,400RM 1,920
ml · trainingSLO RM 1,800RM 1,860
ad-hoc · explorationSLO RM 400RM 520
Reviewed Friday · budgets per team

Self-service queries that can't misbehave.

Row-level security and column masking enforced at the platform, not per dashboard. Analysts query the full catalog, every query carries an audit trail, and PII never leaks through a forgotten filter.

Query audit · last hour
analyst_04 · mart_revenueRLS · region=KL
280ms
bi_service · fct_ordersservice acct
1.2s
ml_pipeline · dim_customerPII masked
running
analyst_11 · raw.* blockedpolicy violation
denied
Audit retained · 90 days

A platform your team owns and extends.

  • Platform architecture

    Documented architecture with compute, storage, orchestration, and access layers, deployed and running, not a slide deck.

  • Self-service tooling

    Templates, CLI tools, and CI/CD pipelines that let your data team ship without waiting for infrastructure changes.

  • Governance framework

    Data catalog, classification policies, and access controls implemented and enforced at the platform level.

  • Operations playbook

    Capacity planning, cost optimization, and incident response procedures for the team that runs the platform day to day.

Ready to talk about platform work?

Book a discovery call. We will walk through how this fits your business, scope, timeline, and what you will get at the end.