Data lake and architecture
The data exists. Your reporting just can't find it.
You have a CRM, a finance system, a marketing platform, and three spreadsheets someone built in 2022 that are now somehow load-bearing, and a leadership team asking for a single view of the business that nobody can produce. This is not a reporting problem. It is an architecture problem.
The data exists, it is just living in separate systems that were never designed to talk to each other. A data lake solves this by creating a central layer that pulls from every source, standardises and enriches the data, and makes it available for reporting, analytics, and AI - reliably, in one place.
When it is built well, you can add or remove tools from your stack without rebuilding your reporting from scratch every time
“Most data engineering teams don't understand the CRM layer. Most CRM partners don't understand data architecture. That gap is exactly where projects fall over - and it's what we've built our practice around bridging."
Ralph Vugts,
Development Director Engaging.io
When does a business actually need a data lake?
Not every data problem needs a data lake. A single-system business with clean CRM data and straightforward reporting can usually get what it needs from HubSpot's native reporting tools or a BI connector. A data lake becomes the right answer when:
You have data spread across multiple platforms (CRM, ERP, finance, ticketing, marketing) and no single system holds the full picture; your reporting team spends more time reconciling data than analysing it.
You are adding AI or machine learning to your roadmap and need a clean, centralised data foundation to build on.
You need a golden record: one trusted version of a customer or contact, that all systems can read from.
For HubSpot customers in particular, connecting HubSpot to a warehouse like Snowflake or Databricks unlocks reporting beyond what the CRM can natively produce: campaign attribution tied to revenue, customer lifetime value across every touchpoint, and segmentation that actually reflects how people buy.
So how does a data lake get built?
Every engagement follows a four-stage process that starts with a question most vendors skip: is a data lake actually what you need? Or is there another solution that would best suit your business?
1. Assess the stack: We review your current systems, data volumes, and reporting requirements to confirm whether a data lake is the right fit. If a simpler architecture solves the problem, we will tell you.
2. Architect the solution: We design the structure - Databricks, Snowflake, AWS, or Google Cloud, or a combination, based on your workloads, your team's capability, and where you are going, not just where you are now. Platform selection follows strategy, not the other way around.
3. Build and enable: We implement the pipelines, transformations, and integrations, then build the reporting layer so insights reach the people who need them, not just the data team.
4. Handover and empower: Every build includes documentation and structured handover so your team can manage, extend, and evolve the lake confidently after we leave. Dependency on an external team is not a success metric.
Data lake platforms we support:
Databricks - Intelligent pipelines, transformations, and machine learning at scale.
Snowflake - Secure, lightning-fast cloud warehousing with powerful query performance.
AWS & Google Cloud - Flexible, scalable hosting options that grow with your business needs.
"We were up against really aggressive build and deployment plans and Engaging were sensational in how they were able to team with us and help us along the way. We were able to get a customised CRM that really supported the re-launch of our global business. Highly recommend the team at Engaging."
Dylan Price-Brennan
Technical Director, Alta
Common questions about data lake architecture:
Almost always because the data lives in separate systems that were never built to share it. The CRM holds customer data. Finance holds revenue data. Marketing holds campaign data. Each system is accurate on its own terms, but there is no layer pulling them together into one consistent picture. A data lake is that layer - it standardises and centralises the data so leadership can report across all of it.
Usually a combination of schema mismatches, manual exports, and systems that refresh on different schedules. When data is being moved manually or connected by fragile point-to-point integrations, inconsistency is the predictable result. A well-architected data pipeline replaces that with automated, reliable flows that keep every reporting layer current.
It depends on complexity, not size. If your reporting team is reconciling data across three or more systems, if you are planning AI or advanced analytics, or if you need a golden record that all teams can trust, a data lake earns its cost quickly. If you have one primary system and straightforward reporting needs, a simpler solution may be the better answer. We assess this before recommending anything.
The right platform depends on your workloads, your team's existing skills, your budget, and what you plan to do with the data. Snowflake is typically strong for query performance and ease of use. Databricks suits complex transformation and machine learning pipelines. AWS and Google Cloud offer flexible hosting with deep ecosystem support. We design the architecture around your situation, not around a platform preference. Engaging.io is also Snowflake, Databricks and AWS partner - so we have expert knowledge of which platform would be best suited to your business needs.
Through a pipeline that extracts data from HubSpot via API, transforms it into the schema your warehouse expects, and loads it on a defined schedule. We design and build this as part of the data lake architecture, so HubSpot data flows automatically alongside your finance, ERP, and marketing data into one centralised reporting layer.
A focused build with two to four source systems typically runs eight to sixteen weeks from architecture sign-off to go-live. We scope every project individually and will not give you a number before we understand your stack. Anyone who does is guessing.
Platform certifications matter, but they are table stakes. Look for a partner who asks whether a data lake is right for you before selling it, who designs for your team's ability to manage the outcome rather than creating dependency, and who has experience connecting your specific stack - particularly if HubSpot is part of it. We are a certified Databricks partner and HubSpot Elite Partner with delivery across Snowflake, AWS, and Google Cloud. anim id est laborum.
Why data teams choose us:
We are a certified Databricks, Snowflake, and AWS partner and HubSpot Elite Partner, with delivery experience across Snowflake, AWS, and Google Cloud. We have been doing this since 2009, and as the 2025 HubSpot JAPAC Partner of the Year, we bring the CRM depth most data engineering firms do not have - which matters when your lake needs to connect to your go-to-market stack, not just your data warehouse. More importantly, we build for handover. The goal is a data lake your team owns, not one they depend on us to maintain.