Data Pipelines & Medallion Architecture

Data Engineering & Integration Services

Scalable data pipelines, automated ETL/ELT workflows, lakehouse engineering, and enterprise system integration that make your business data reliable, structured, and query-ready.

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Data Engineering & Integration Services Architecture

OneLake

Unified Microsoft Fabric medallion data lakehouse architecture processing batch and streaming telemetry into Gold star-schema semantic models.

The Challenge

Fragile Data Ingestion and Unreliable Analytics

When data pipelines are built with ad-hoc scripts and brittle scheduled jobs, silent data corruption occurs, schema drift breaks reporting dashboards, and operational teams spend their days firefighting data errors rather than driving business insights.

Fragile manual exports and custom scripts failing silently without alerting
Unstructured data sources creating mismatched data types and broken reports
Lack of historical point-in-time snapshotting and audit lineage
High latency between operational transactions and analytical dashboard updates

How AgenorIT Delivers Data Engineering & Integration Services

AgenorIT delivers reliable data engineering and system integration services for Australian organisations. We architect automated ELT pipelines using Apache Spark, Azure Data Factory, and Microsoft Fabric, building medallion lakehouses that transform raw transactional data into validated, business-ready models.

AgenorIT Engineering Practice

Measurable Outcomes

Expected Business & Architectural Impact

Clean Medallion

Guaranteed Data Integrity

Automated schema validation, deduplication, and anomaly testing catching bad data before it impacts reports.

High Throughput

Scalable High-Throughput Pipelines

Processing millions of transactional records in parallel using PySpark and Delta Lake table optimizations.

100% Traceability

Full Data Lineage & Auditability

Complete trace tracking from source systems through transformation logic to final reporting dimensions.

Sub-Minute Ingest

Real-Time & Batch Ingestion

Unified pipelines supporting both scheduled daily batch processing and event-driven micro-batch ingestion.

What We Deliver

Tangible Engineering Deliverables

We deliver concrete, production-ready artefacts into your repositories and cloud tenants—not slide decks or vague advisory hours.

Pipelines & Transformation

Production ETL/ELT pipelines developed in Azure Data Factory, Fabric Data Pipelines, or dbt
Modular PySpark / SQL transformation scripts implementing medallion data layers
Automated error handling, retry backoff algorithms, and instant webhook alerts

Storage & Schema Modeling

Delta Lake table architectures with partitioned directories and V-Order compression
Star-schema dimensional models (Fact and Dimension tables) optimized for fast analytical queries
Data contract definitions and schema validation test suites

Governance & CI/CD

Version-controlled data pipeline repository with automated deployment to staging and production
Data dictionary and metadata catalog documentation detailing every transformed column
Operational runbook covering pipeline recovery, backfilling historical data, and maintenance

Technologies & Toolchains

Engineered using verified, production-grade tools and industry-standard frameworks.

Apache Spark

PySpark

Azure Data Factory

Microsoft Fabric

Delta Lake

dbt

T-SQL

Azure Data Lake Storage Gen2

Python

Engagement Model

Structured Delivery Process

A disciplined, transparent delivery framework designed for predictability and rapid time-to-value.

Step 01

Data Landscape Discovery

Map source database schemas, API limits, ingestion frequencies, data volume growth, and business reporting logic.

Timeline: 1–2 Weeks

Key output: Source-to-Target Data Mapping Spec

Step 02

Medallion Lakehouse Design

Design raw (Bronze), cleansed (Silver), and analytical star-schema (Gold) data models and partition keys.

Timeline: 2 Weeks

Key output: Data Model & Pipeline Architecture

Step 03

Pipeline Engineering & Testing

Build ingestion pipelines, PySpark data cleansing scripts, and automated validation rules with historical backfill.

Timeline: 3–5 Weeks

Key output: Tested Data Pipelines & Lakehouse

Step 04

Validation & Operational Handover

Verify row counts and calculated metrics against source systems, configure monitoring, and train your data team.

Timeline: 1 Week

Key output: Validation Report & Operations Manual

Architecture Decision Guide

Evaluating Your Technical Approach

Data Architecture: Legacy Batch Pipelines vs Medallion Lakehouse Engine
Pipeline Attribute	Legacy ETL / Staging DB	Medallion Lakehouse (Fabric/Azure)
Data Ingestion Speed	Overnight batch scripts that lock operational transactional databases	Continuous micro-batch and real-time streaming into Bronze Delta Parquet
Data Quality & Cleansing	Ad-hoc SQL stored procedures with opaque transformation logic and errors	Automated Silver schema enforcement, deduplication, and lineage tracking
Analytics Serving	Duplicate data extracts loaded into separate data marts for reporting	Unified Gold star-schemas queried in-place via OneLake Direct Lake mode
Governance & Lineage	Undocumented spreadsheets and lost table origins across departments	Centralised Microsoft Purview metadata cataloguing and access permissions

Verified Engineering Impact

Automated Medallion Delta Lakehouse Pipelines

Client Context

Victorian logistics and freight enterprise transitioning from legacy overnight stored procedures to PySpark Delta Lake ingestion.

Architectural Outcome

Batch processing window reduced from 8 hours to 12-minute real-time delta intervals with idempotent error recovery.

When custom data engineering is not the right fit

If your data volume is small and all reporting needs are satisfied by querying your live transactional database directly without performance degradation, complex lakehouse data engineering is premature.

Technical FAQ

Data Engineering & Integration Services — Technical FAQ

Direct engineering answers to common technical and commercial queries.

The Medallion Architecture organizes data into three distinct quality layers: Bronze (raw ingested data), Silver (cleansed, deduplicated, enriched data), and Gold (business-aggregated dimensional star schemas), ensuring traceability and high performance.

We implement schema evolution rules and data contracts that catch unexpected column additions or type modifications, quarantining malformed records while continuing pipeline execution.

Yes. We deploy secure On-Premises Data Gateways and private self-hosted integration runtimes that extract data securely over TLS tunnels without exposing database ports to the public internet.

Traditional ETL transforms data on a separate server before loading it. Modern ELT loads raw data directly into the lakehouse and uses powerful distributed compute engines (like Spark) to transform it in place, which is significantly faster and more scalable.

We implement automated reconciliation assertions that check row counts, financial totals, and hash sums between source systems and lakehouse tables at the conclusion of every pipeline run.

Connected Solutions