Connect · Service 01

Data Engineering & Integration

We design and build the pipelines that move data out of operational systems, files, and APIs into a governed platform, so every downstream report and model starts from the same trusted numbers.

Discuss your data integration

Where it fits01 · Connect

One dependable data foundation instead of disconnected sources.

When this helps

Problems this service solves

The same metric is calculated differently in finance, sales, and operations.
Reports depend on manual exports and spreadsheets that break when a column changes.
Source systems such as ERP databases cannot take heavy analytical queries.
Nobody can say with confidence where a number came from or when it was last refreshed.

Reference architecture

From disconnected systems to a reliable data ecosystem.

Select a layer to see what happens there. This is a conceptual reference architecture; the components in each engagement depend on your sources, platform, and requirements.

Sources

Operational databases such as Oracle, DB2, and MySQL, ERP and CRM applications, SaaS platforms, flat files, and REST APIs. Each source is profiled for volume, change rate, and access method before anything is built.

Example components

Oracle
IBM DB2
MySQL
ERP / CRM
SaaS APIs

Conceptual example. Product names identify technologies we work with and do not imply partnership or endorsement.

What we deliver

Capabilities

Source integration and replication

Bring data from databases such as Oracle, DB2, and MySQL, from enterprise applications, files, and APIs. Where freshness matters, use change data capture so source systems are not overloaded.

Pipelines and transformation

Build ELT and ETL pipelines with clear layering, from raw to cleaned to business-ready, with transformations that are version controlled, tested, and documented.

Orchestration and monitoring

Schedule and sequence workloads with dependency handling, retries, and alerting, so a failed load is noticed before a dashboard shows stale numbers.

Data quality and governance

Add checks for completeness, duplicates, and referential integrity, plus lineage and ownership metadata, so teams know which datasets they can rely on.

Approach

How an engagement runs

1
Step 1
Inventory
Map sources, volumes, refresh needs, and the business questions the data has to answer.
2
Step 2
Architecture
Agree the target platform, ingestion patterns, layering, and security model before building.
3
Step 3
Incremental build
Deliver priority subject areas first and validate them against existing reports.
4
Step 4
Operate
Hand over runbooks and monitoring, or continue under a managed service.

Things we will be honest about

Connector choice depends on source versions, licensing, network access, and required latency. We confirm feasibility during assessment rather than assuming it.
Existing pipelines that work are kept where sensible. Rewrites are justified case by case.

Technical context

Qlik Replicate
Qlik Data Integration
Talend
Informatica
Azure Data Factory
dbt
Databricks
Snowflake
Microsoft Fabric
Google BigQuery
Oracle
IBM DB2
MySQL

Product names belong to their owners. Listing does not imply partnership or endorsement. See all technologies

Relevant industry pathways: Manufacturing, Pharmaceutical & Healthcare, Financial & Business Services, Automotive & Transportation, Energy & Utilities, Education, Real Estate & Construction, Telecom & Technology, Marketing & Advertising, Retail & Consumer Businesses, Logistics & Supply Chain, Professional Services

Ready to talk about Data Engineering?

Describe the problem and your current setup. We will suggest a sensible first step, even if it is a small one.