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Data Pipelines & Engineering

AI-Ready Data Pipelines on Modern Cloud Platforms

AMFAIHAR designs and operates production data pipelines across AWS, Azure, GCP, Snowflake, Databricks, and Fivetran. From ingestion and modeling to MLOps and workflow automation, we make sure your data is reliable, governed, and ready for AI.

From Raw Events to AI Features

Most organisations sit on fragmented data spread across operational systems, SaaS tools, and legacy warehouses. We help you turn that sprawl into a governed, observable data backbone that powers analytics dashboards, ML models, and automated decisioning. Our teams combine cloud data engineering, n8n-based orchestration, and custom AI integration to ship real outcomes—not just pipelines.

Data Pipelines & Engineering

Pipeline Capabilities

End-to-End Data Engineering Outcomes

  1. 1

    Batch & Streaming Ingestion

    Capture data from databases, event streams, APIs, and SaaS tools using native connectors, CDC, and message queues. Design ingestion layers that are resilient, idempotent, and governance-ready.

  2. 2

    Modelled Warehouses & Lakehouses

    Design dimensional models, data vaults, or medallion-style lakehouses with clear contracts between bronze, silver, and gold layers. Keep data discoverable and stable for BI and AI teams.

  3. 3

    Data Quality & Observability

    Track freshness, volume, schema, and business rules through automated tests and monitors. Alert early when data is missing, late, or out of expected ranges.

  4. 4

    Security & Governance

    Implement row/column-level security, masking, lineage, and role-based access across platforms so compliance and analytics teams trust what they see.

  5. 5

    MLOps-Friendly Design

    Build feature-ready tables, reproducible training datasets, and model inference paths that plug into ML platforms without re-engineering the entire stack.

  6. 6

    Cost-Aware Architectures

    Right-size clusters, control storage tiers, and optimize compute patterns so your data platform scales without runaway spend.

AI & Workflow Automation

Plug AI Into Your Data, Not the Other Way Round

Capability
What It Covers
AI API Integration
Integrate OpenAI and other LLM APIs into your applications and pipelines: enrichment, summarisation, classification, document extraction, and intelligent routing built directly on your governed data.
n8n-Based Workflow Orchestration
Use n8n as an orchestration and automation layer on top of your data stack: trigger flows from webhooks, queues, or schedules; call APIs; fan-out workloads; and push results into CRMs, ticketing tools, and notification systems.
Event-Driven Automation
Drive business actions when data changes: send alerts on pipeline anomalies, auto-create tickets for data quality issues, or kick off ML re-training when new data arrives.
Model Training & Serving Loops
Wire pipelines that prepare features, train ML models, log experiments, and deploy updated models into inference endpoints with clear rollback and monitoring.
Human-in-the-Loop Workflows
Design flows where AI makes the first pass and humans approve, correct, or escalate—keeping critical decisions auditable and compliant.
Monitoring & Feedback
Track latency, error rates, user feedback, and drift for AI-powered features and automated flows, then feed insights back into training and tuning.

Cloud Platforms

Data Pipelines on Your Cloud of Choice

Cloud Data Engineering

AWS Data Engineering

Design and operate data lakes, warehouses, and streaming pipelines on AWS using services like S3, Glue, Redshift, Athena, Kinesis, EMR, and Lambda.

  • Lakehouse and warehouse architectures on AWS.
  • Batch + streaming ingestion with Glue, MSK/Kinesis, and DMS.
  • Governance with Lake Formation, IAM, and centralized logging.
View AWS Data Engineering offering→

Delivery Method

Structured Delivery Without Losing Agility

1Step 1

Discovery & Architecture

Map source systems, data shapes, and business use-cases. Define reference architectures that balance performance, governance, and existing investments.

2Step 2

Platform Setup & Guardrails

Configure accounts, projects, workspaces, networking, and RBAC. Put in place CI/CD, naming standards, and environments so teams ship safely from day one.

3Step 3

Incremental Pipeline Delivery

Ship thin slices: a source, a model, and a dashboard or API that proves value. Iterate until the domain is fully covered instead of waiting for a big-bang migration.

4Step 4

Quality, Testing & Replay

Use unit tests, integration tests, and data checks with replayable pipelines so issues can be investigated and fixed without losing trust.

5Step 5

Run, Operate & Optimise

Take ownership for run-books, on-call, cost tuning, and backlog grooming once pipelines are in production.

Why Choose AMFAIHAR?

What Sets Our Data Engineering Apart

8 reasons why enterprises trust AMFAIHAR to build and run production-grade data pipelines and AI-ready platforms.

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01

Cloud-Native Everywhere

Deep hands-on experience across AWS, Azure, GCP, Snowflake, Databricks, and Fivetran with reference architectures for each.

SELECT AN ITEM TO LEARN MORE

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Ready to get started?

Ready to drive business value at scale with data you can trust?

Power the business
Elevate your data quality
Accelerate business value
Execute with confidence