Data Engineering Services & Integration on Google Cloud
Built to keep data moving
Every organisation eventually reaches the point where the systems have to talk to each other properly. Our data engineering consulting starts from the systems you already run: we design and build the pipelines and integrations on Google Cloud that connect them, standardise what comes out, and keep running when a source system changes underneath.
Talk to an expertSee what we deliver
- 20+ years on Google Cloud
- Google Cloud Premier Partner
- 50+ certifications
- ISO 27001:2022
How silos form
Nobody sets out to build a silo. They accumulate from a series of sensible decisions: a quick script to get two systems talking, a scheduled export because the API was awkward, a spreadsheet because it was Friday.
Point-to-point connections, one per problem
Each integration was built for the pair of systems in front of someone at the time. Adding the tenth system means writing nine more connections, and the person who wrote the first three has left.
It breaks when the source changes
A field is renamed upstream, an API rate limit tightens, and the pipeline fails quietly overnight. The failure is usually discovered by someone noticing a number looks wrong.
The same transformation, written five times
Currency conversion, deduplication, date handling: each rebuilt slightly differently in each pipeline, which is how two reports end up disagreeing about the same order.
From point-to-point to platform
The shift is from writing a connection each time to having somewhere connections go: reusable patterns, managed services that scale on their own, and monitoring that tells you about a failure before a business user does.
Before
- A bespoke connection per pair of systems
- Ingestion, transformation and validation done by hand
- Pipelines that fail silently when a schema changes
- Capacity planned in advance and usually wrong
- Every new use case starting from scratch
After
- One orchestration layer connecting the systems you run
- Ingestion, transformation and validation automated
- Connectors built to survive API limits and schema changes
- Serverless services that scale without capacity planning
- Reusable patterns and templates, so the next one is cheaper
Watch
Why bolting one more connector onto a patchwork rarely helps, and what a unified architecture on Google Cloud looks like instead.
Managed services, fewer moving parts
Application Integration, Dataflow, Pub/Sub and Apigee are all things you would otherwise have to build and then operate. Using them means the parts that usually break at three in the morning, scaling and retry and back-pressure, are somebody else's problem to run.
It also keeps the integration layer inside the same environment as the warehouse it feeds, which removes an entire category of network, identity and residency questions.
20+
Years building on Google Cloud, since before most of it had a name
50+
Google Cloud certifications across the team
Premier
Google Cloud Premier Partner, with specialisations in data and infrastructure
What we connect
Engagements usually open with an assessment of the landscape and the business requirements, then move through architecture, pipeline engineering and the operational governance that keeps it all running.
Google Cloud's low-code iPaaS as the orchestration layer for customer data integration, with pay-as-you-go pricing and standard connectors for Salesforce Marketing Cloud, Dynamics 365 and the rest of the platforms most organisations already run.
Orchestration layer
One place where integrations live, instead of a connection written each time two systems need to meet.
Standard connectors
Salesforce Marketing Cloud, Dynamics 365 and the rest, configured rather than written from scratch.
Low-code flows
Integration logic that someone other than its author can read, which matters more over three years than it does in week one.
Pay-as-you-go
Cost that follows what you actually run, rather than an enterprise licence sized for a volume you have not reached.
APIs & streaming
For the cases where scheduled batch is not enough: publishing and governing APIs properly, and moving events as they happen rather than overnight.
API management with Apigee
Publishing, securing and monitoring APIs across the organisation, with a view of who is calling what.
Pub/Sub ingestion
Real-time ingestion and distribution that scales elastically, so a promotion does not take the pipeline down.
Dataflow pipelines
Fully managed, serverless processing with automatic scaling, for the transformations that are too heavy to sit in a flow.
The part that decides whether any of the above is still working in a year: infrastructure as code, monitoring that notices before a user does, and someone paying attention to what it costs.
Infrastructure as code
Environments defined in a repository, so a change can be reviewed, repeated and rolled back.
Automated monitoring
Failures surfaced with enough context to act on, rather than a red dot on a dashboard nobody watches.
Performance tuning
Making the pipelines fast enough that people stop scheduling around them.
Cost management
Attention to what each pipeline costs to run, which is the number that quietly grows while nobody owns it.
A platform migration, in three months
Replatforming an eCommerce business is mostly an integration problem wearing a different hat.
Body&Fit · sports nutrition
Connecting Shopify to everything behind it
Body&Fit needed a new Shopify storefront integrated with warehouse management, contact services, email marketing and BI, across both direct-to-consumer and direct-to-store. We used Google Cloud Application Integration as the orchestration layer rather than writing custom pipelines. Two new external systems were integrated within two months, and the full solution was in production by the third.
Read the case study →
Before you write another script
We have working integrations. Why change them?
If they are stable and somebody understands them, often you should not. The case for changing usually appears when the count grows past what one person can hold, when failures stop being noticed quickly, or when a replatforming project means half of them need rewriting anyway.
Is iPaaS not just another licence?
Google Cloud Application Integration is priced by what you run rather than per seat or per connector, which changes the arithmetic considerably against enterprise iPaaS. For low-volume, stable flows a lightweight custom layer can still be cheaper, and we will say which side of the line you are on.
What happens when a source system changes its API?
Something will, and reasonably often. Connectors are built to handle rate limits and schema changes rather than assume they will not happen, and monitoring is there so that when something does break it is noticed before a report looks wrong.
Can our own team maintain this?
That is the intent. Low-code flows are readable by people who did not write them, infrastructure sits in your repository, and documentation is part of the work rather than something promised at the end.
How do you handle security across integrations?
Integration usually means credentials to several systems in one place, so access and secret handling are designed in from the start. We are ISO 27001:2022 certified, and for regulated clients we work to whatever additional framework applies.
Next step
Want the systems to talk properly?
If you are replatforming, or the integration count has grown past what anyone can hold in their head, we are happy to look at what is there and what it would take to consolidate it.