// datavine :: solutions

Data engineering services built for Malaysian enterprises

Three focused services that address distinct layers of your data environment — from raw ingestion through to the trust and governance your analysts need to work with confidence.

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// methodology.overview

How each engagement is structured

01

Scoping

Understand your environment, objectives, and constraints before committing to a scope.

02

Discovery

Source mapping, access review, and architecture planning with your technical team.

03

Build

Incremental delivery with regular check-ins, so working components surface early.

04

Handover

Documentation, knowledge transfer sessions, and monitoring in place before we close.

// solution :: stage.01

Data Pipeline Architecture

Design and implementation of data pipelines that collect, transform, and deliver data from diverse sources to analytics-ready formats. The service covers source integration, ETL/ELT workflow design, scheduling, and monitoring setup.

Built for organizations that need reliable, maintainable data infrastructure as a foundation for AI and analytics work — not a temporary fix, but something your team can extend over time.

What's included

  • Source inventory and integration mapping
  • ETL/ELT workflow design and implementation
  • Scheduling and orchestration setup (Airflow or equivalent)
  • Pipeline monitoring and alerting configuration
  • Technical documentation and handover session

Process steps

  1. 1.Source systems audit and access provisioning
  2. 2.Data flow design and schema mapping
  3. 3.Incremental pipeline build with staging reviews
  4. 4.Quality validation and error handling setup
  5. 5.Monitoring configuration and team handover

starting.price

RM 6,500

Typical duration: 3–6 weeks

Enquire
Data Pipeline Architecture

// suitable_for

  • Organizations with data in multiple disconnected systems
  • Teams currently using manual exports and spreadsheet consolidation
  • Businesses preparing for an analytics or AI initiative
  • Companies whose pipelines are fragile or poorly documented
Data Lake & Warehouse Setup

// cloud.options

AWS S3 + Redshift Azure ADLS + Synapse GCS + BigQuery Snowflake Databricks Delta Lake
// solution :: stage.02

Data Lake & Warehouse Setup

Architecture and deployment of cloud-based data lakes and warehouses tailored to your organization's data volumes and query patterns. The engagement includes schema design, partitioning strategy, access control configuration, and cost optimization guidance.

Suitable for Malaysian enterprises transitioning from fragmented data storage to centralized analytics platforms. We'll help you choose the right platform and get the architecture right from the start.

What's included

  • Platform selection guidance (cloud-agnostic)
  • Schema design and partitioning strategy
  • Storage tiering and cost optimization setup
  • Role-based access control configuration
  • BI tool connection setup and validation

Process steps

  1. 1.Data volume assessment and query pattern analysis
  2. 2.Platform recommendation with cost modelling
  3. 3.Schema and layer architecture design
  4. 4.Deployment, access controls, and initial data loading
  5. 5.BI connection validation and documentation handover

starting.price

RM 8,200

Typical duration: 4–8 weeks

Enquire
// solution :: stage.03

Data Quality & Governance Framework

Establishment of data quality monitoring, lineage tracking, and governance policies that ensure analytical outputs can be relied upon. Services include quality rule definition, validation pipeline setup, metadata cataloguing, and role-based access policy design.

Designed for organizations where data trust is foundational to decision-making — where an analyst acting on inaccurate data has real consequences.

What's included

  • Data quality rule definition and documentation
  • Validation pipeline configuration
  • Data lineage tracking setup
  • Metadata catalogue configuration
  • Access policy design aligned with PDPA requirements

Process steps

  1. 1.Data quality audit across existing datasets
  2. 2.Governance policy design with stakeholder input
  3. 3.Validation pipeline implementation
  4. 4.Catalogue setup and lineage mapping
  5. 5.Policy documentation and team walkthrough

starting.price

RM 4,000

Typical duration: 2–4 weeks

Enquire
Data Quality & Governance Framework

// suitable_for

  • Organizations where data reliability is questioned by internal stakeholders
  • Regulated industries with audit or compliance data requirements
  • Companies preparing for AI/ML models that require clean training data
  • Businesses handling personal data under Malaysia's PDPA
// comparison.features

Choosing the right service

Not sure where to start? This overview may help. If you're still uncertain, a scoping call is the most practical next step.

// feature Pipeline Architecture Lake & Warehouse Quality & Governance
Source integrationPartial
ETL/ELT workflows
Cloud storage architecturePartial
Warehouse / lake deployment
Data quality rulesPartialPartial
Lineage tracking
Access control setupPartial
Monitoring & alerting
Documentation included
starting.price RM 6,500 RM 8,200 RM 4,000

Services can be combined. Many engagements start with Pipeline Architecture and extend into Quality & Governance once the core infrastructure is in place.

// standards.shared

Technical standards applied across all services

Security by Default

Least-privilege access, encryption at rest and in transit, and credential management from day one.

Version Control

All code and configuration managed in version control. Changes are tracked and reversible.

Observable Pipelines

Monitoring and alerting in place so your team has visibility into pipeline health without manual inspection.

// cta.next_step

Not sure which service fits your situation?

A brief scoping conversation is a reasonable place to start. Describe your current data environment and what you'd like to do with it — we'll give you an honest assessment.

Get in Touch