AWS data engineering and analytics

Build dependable data systems on AWS.

V3rim helps data-intensive companies design, modernize and govern AWS data platforms—from ingestion and lakehouse architecture to analytics, lineage, AI and inference data paths, cost control and infrastructure automation.

Built for small and medium-sized companies in Turkey and Europe that operate data-intensive systems on AWS.

Discuss your data platform

Direct access to senior engineering. We normally respond within one business day.

Problem signals

When an AWS data platform needs attention.

Visible reporting problems often begin upstream—in ingestion, ownership, quality, governance, cost and platform operations.

Fragile ingestion

Pipelines rely on manual steps or fail without a clear recovery path.

Slow, expensive analytics

Queries and data layout consume time and AWS budget without clear attribution.

Conflicting metrics

Teams reconcile definitions repeatedly instead of trusting shared data products.

Weak discoverability

People cannot reliably find, understand or request access to the right data.

Governance that does not scale

Catalog, lineage, ownership and access controls lag behind platform growth.

AI blocked upstream

Inference and retrieval paths depend on data that is incomplete, stale or poorly controlled.

Manual infrastructure

Environments drift because platform changes are not repeatable in Terraform.

Unclear operations

Monitoring shows failures after users do, while ownership and next actions remain ambiguous.

Architecture flow

One dependable path from source to use.

Reliable analytics and AI depend on explicit stages for ingestion, storage, transformation, quality, governance and consumption.

Sources

ERPCRMSQL ServerAPIsFiles

Ingestion

BatchCDCEvents

Data platform

RawValidatedCurated

Trust

QualityGovernanceLineage

Consumers

AnalyticsApplicationsAI / ML

Capabilities

Engineering beneath dependable data products.

The architecture follows the problem. Services and tools are selected after the workload, constraints and operating model are understood.

Ingestion and integration

Bring operational and external data into AWS through reliable, observable paths.

Batch
CDC
Events
APIs
Files
Orchestration

Lake, lakehouse and transformation

Organize data into explicit stages of trust with maintainable models and transformations.

Amazon S3
Apache Iceberg
Data modeling
Raw / validated / curated
Transformation

Analytics and performance

Make analytical workloads useful, understandable and proportionate in cost.

Amazon Athena
Amazon Redshift
SQL
Semantic models
Workload optimization

Governance and discoverability

Help teams find, understand, govern and request access to data.

AWS Glue Data Catalog
AWS Lake Formation
Amazon DataZone
Catalog
Data marketplace
Lineage

AI and inference data paths

Prepare governed data paths for retrieval, inference and other AI workloads.

Trusted datasets
Retrieval data
Model-ready data
Governed access
Inference paths

Operational quality and cost

Treat reliability, security boundaries and cost visibility as part of the platform design.

Monitoring
Data quality
Security boundaries
Cost attribution
Terraform

How we engage

From evidence to a platform your team can operate.

Each engagement is scoped around the current system, the decision or outcome required, and the constraints that matter.

Assess
Establish the current architecture, constraints, evidence, cost drivers and priorities.
Design
Define the smallest credible target architecture, governance model and migration path.
Implement
Build the agreed pipelines, platform, access controls and automation.
Validate
Test reliability, data quality, access, performance, cost and recovery behavior.
Hand over
Document decisions, operating procedures and ownership so the platform remains usable.

Typical deliverables

Concrete outputs, sized to the agreed scope.

Exact deliverables depend on the engagement. We define them before implementation begins.

Findings and priorities

A current-state view of material risks, constraints, cost drivers and improvement opportunities.

Target architecture and plan

An implementable design, migration sequence and decision record aligned with the operating model.

Scoped implementation

The agreed pipelines, data layers, access controls, infrastructure automation and operational controls.

Validation and handover

Test evidence, documentation, runbooks and ownership needed to operate and improve the result.

Public client reference

Muhiku

Related capabilities

Support the data platform around the edges that matter.

AWS cost optimization

Identify cost drivers and align cloud spend with the workloads and outcomes that matter.

Explore AWS consulting

Terraform adoption

Move manual AWS infrastructure toward repeatable, reviewable and maintainable infrastructure as code.