Data Integration

Why it matters

Most organisations have data spread across multiple systems. An ERP holds transactional data. A CRM tracks customer interactions. Cloud applications collect operational signals. Older systems still hold critical historical data. Third-party SaaS platforms add more sources. Getting data from all these places to a single reliable source is harder than it seems.

Building pipelines between systems is straightforward initially. But as the number of sources grows, maintenance becomes complex. Definitions of key metrics drift across systems. One team calculates revenue one way, another team calculates it differently. Pipelines break when upstream systems change. Teams build manual workarounds—exports, CSV uploads, overnight jobs—to keep reporting working. IT firefights ingestion issues instead of building strategic advantage. Analytics teams wait for data instead of creating insight.

The real problem isn’t technical integration. It’s creating one trusted version of the data. When teams trust the data—when they know definitions are consistent, feeds are reliable, and metadata is current—they use it confidently. Analytics accelerates. AI models train on reliable foundations. Decision-making improves.

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When pipelines are built on metadata frameworks rather than hard-coded logic, changes move faster. A new field in the source system becomes a configuration update, not a development cycle. Teams define data once, reuse the definition across all pipelines. This approach scales across dozens of sources without the maintenance overhead of hand-built integration. Azure Data Factory, Microsoft Fabric, and Synapse all support metadata-driven patterns. The discipline of using them consistently is where value concentrates.

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Most data quality problems stay hidden until they hit reporting. SCC Vision provides 24×7 monitoring of your pipelines, validation checks, and alerting so problems surface immediately. You can measure pipeline health, spot patterns, and anticipate failures. This shifts integration from reactive firefighting to proactive optimisation.

Key features

Assessment and source mapping

We start by understanding your current state. Which systems hold critical data? What’s the cadence—batch overnight, real-time streaming, event-driven? What definitions are inconsistent across teams? Where are the manual workarounds that shouldn’t exist? We map data flows and dependencies so you understand the complexity before designing the future state.

Metadata-driven pipeline design

Rather than building individual point-to-point pipelines, we design metadata-driven frameworks. Data contracts define how information flows. Transformation rules are configured, not coded. Field mappings, validation rules, and lineage are all recorded. When requirements change, you update metadata instead of rebuilding pipelines. This approach works across ETL and ELT patterns depending on your architecture needs.

Performance optimisation for ETL and ELT

Choosing between transforming before loading (ETL) or loading then transforming (ELT) depends on your architecture, data volume, and latency requirements. We help you decide based on scalability, governance, and performance realities. Azure Synapse and Fabric handle both patterns efficiently. We design for your specific volumes and latency targets, not generic best practices.

Continuous monitoring and evolution

Integration isn’t a project. It’s a capability you maintain and evolve. We implement SCC Vision monitoring across all pipelines so problems surface immediately. We establish processes for onboarding new sources and retiring old ones. We optimise based on real usage patterns. As your business changes, integration adapts.

How it works

Step 1

Assess current state and map data dependencies

We document where your data lives, how systems connect today, where manual workarounds exist, and which definitions are inconsistent. This assessment clarifies the scope and identifies areas where integration problems are already affecting business outcome. We identify quick wins—high-value pipelines where improvement yields immediate benefit.

Step 2

Design integration architecture and data contracts

Based on your assessment, we design the integration framework. Which systems are sources? What’s the transformation logic? How do you handle change—batch, streaming, event-driven? We define data contracts so everyone knows what data means. We specify which patterns you’ll use (metadata-driven rather than hand-coded) so the design scales as you add sources.

Step 3

Build and optimise pipelines using Azure/Fabric/Synapse

We build the core pipelines using your chosen platform—Azure Data Factory, Microsoft Fabric, or Synapse. We focus on using metadata-driven patterns so pipelines are maintainable and scalable. We test with real volumes and measure performance. We optimise ETL versus ELT based on latency and scalability requirements.

Step 4

Implement monitoring and validation

We deploy SCC Vision monitoring across all pipelines. We implement data quality checks so bad data is caught before it propagates. We set up alerting so your teams know immediately when problems occur. We document runbooks for common issues. Your operations team gets the visibility they need to keep integration running reliably.

Step 5

Operate, monitor and evolve

Integration is ongoing. We support your teams through the first several months of operation, optimising based on real usage patterns. As requirements change, new sources are added, or performance drifts, we adjust. We help your team build the capability to evolve integration independently, moving from project mode to operations.

Partners

Specialists

Alexander Viljoen

Digital Data Architect

Alexander has spent the last decade helping IT leaders through data platform modernisation. He’s seen the patterns that work—metadata discipline, governance from day one, continuous optimisation—and the patterns that fail.

He combines strategy, architecture, analytics and everything in between. When leaders struggle with “should we build or buy?” or “warehouse or lakehouse?”, Alexander brings clarity grounded in real implementation experience. He’s more interested in what your team can maintain than what’s technically possible.

Start building integration confidence

Data fragmentation is a symptom, not the problem. The problem is that your team doesn’t trust the data. That kills adoption. That wastes AI investment. That slows decision-making.

We help you build the integration foundations that create trust. Consistent definitions. Reliable pipelines. Full visibility. From there, analytics accelerates naturally.

Book a free data assessment to understand where integration is holding you back and what a phased approach could achieve.

Speak to a specialist

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FAQs

What’s the difference between ETL and ELT?

ETL transforms data before loading it into the warehouse. You clean, validate, and shape data in a staging environment, then load the clean data. ELT loads raw data first, then transforms within the warehouse. ETL gives you tight control over data quality but requires more infrastructure. ELT is simpler initially but puts more load on the warehouse. The right choice depends on your architecture, team skills, and latency requirements. Most modern approaches blend both patterns.

How do we integrate legacy systems with cloud platforms?

Legacy systems usually expose data through APIs, database replication, or file exports. We assess what’s available, then design the most maintainable approach. Sometimes that’s an API wrapper around the legacy system. Sometimes it’s direct database replication. Sometimes it’s controlled batch exports. The goal is reducing manual steps while minimising the load on the legacy system. Integration patterns are the same regardless of the direction — legacy to cloud or cloud to cloud.

Can modern integration support real-time reporting?

Yes, absolutely. Most organisations ask for batch integration initially (“update overnight”) because that’s what legacy systems required. Modern frameworks like Azure Event Hubs and Kafka can stream data continuously. Synapse and Fabric can ingest that stream directly. Power BI can consume near-real-time data. The trade-off is complexity—real-time pipelines require more sophisticated monitoring and alerting than batch jobs. Most organisations find that streaming some critical metrics while batch-loading others is the practical balance.

How do we monitor pipeline health and surface problems early?

Automated monitoring, validation checks, and alerting are essential. SCC Vision monitors execution time, data volumes, error rates, and data quality metrics across all your pipelines. We configure alerts for anomalies—if a pipeline takes 50% longer than normal, if volume drops suddenly, if validation checks fail. This visibility shifts integration from reactive (firefighting when reports break) to proactive (addressing problems before they affect business). Metadata tracking ensures you know not just that a pipeline failed, but which downstream reports and analytics are affected.

Is data integration a one-time project or ongoing work?

Integration is continuous work, not a project. Your business changes—new sources appear, definitions evolve, volumes grow. Organisations that treat integration as ongoing capability work—investing in monitoring, documentation, and governance—mature much faster than those that treat it as a series of projects. Initial build takes 3-6 months typically. Then 15-20% of your data team’s time goes to ongoing maintenance and evolution. As you mature, most of that time is optimisation and strategic work rather than firefighting.