Data engineering consulting
We build the data foundations that let your people investigate questions, create shared applications and automate recurring work. Our principal-level team implements Databasin around your sources and business processes. The work covers connections, transformations and governance through to the applications people use, with rollout, training and support.
Engineering for the work people do
The business question determines which engineering work we do first. A workforce review may need historical assignments; an operating review may need records from several departments. We establish the definitions and relationships the application needs before building its data path.
Databasin supplies the connections, storage, transformations and tools for analysis. Our team implements them together, including the measures and access used by the application. People can then explore the prepared records and repeat their reviews without commissioning a separate pipeline for each view of the same question.
Data engineering for your team
Source connections
Connect business systems, databases and files, with scheduled pipelines and a record of each run.
Shared business views
Prepare the relationships and calculations that let reports and AI questions use the same business measures.
Working applications
Build Data Apps and recurring workflows that put the prepared records into the hands of their users.
How does the data reach users?
Databasin keeps the raw records and prepares business views by applying the required mappings and calculations. Those views serve questions, Data Apps, BI tools and notebooks. An automation can run the source refresh and transformations before delivery. Its run records show the preparation and help the team examine any failed step.
Implementation through ongoing operation
We define the business outcome and expected return before implementing the application. The first use case goes live in 90 days. Our work includes communication, rollout, training and success measurement so people can use what we deliver.
Databasin can contribute alongside an existing Databricks, Snowflake or Microsoft Fabric environment. We design its role around that environment. Once the application is running, we can operate the solution, provide monthly support hours or stay on call as the work develops.
What happens next
We would like to hear where your team wants to make progress with data and AI. We can discuss a project you have in mind or explore where Databasin could help with the work.
What happens next
Book a demo
Book a conversation about your organization and the work you want to improve. Questions about where to start are welcome.
Explore the opportunity
We show a working example that answers your business questions, then discuss what it would take to deliver it for your team.
Get the first result
After agreeing the first use case, we implement it with your team. We train the people using it and measure how it performs.
FAQ
Is the work delivered on Databasin's own platform?
Yes. Databasin supplies the connections, preparation, governance and application capabilities our team uses. We implement those capabilities around the business result, including the other systems and data platforms that will remain in the environment.
Which business sources have native connectors?
Epic, Workday, NetSuite, Salesforce and HubSpot are among the native sources. The application determines which records and relationships are needed. We examine that scope when planning the connection and modeling work for each source.
Can you connect a system outside the native catalog?
Yes, where a suitable interface is available. Databasin supports generic REST and SOAP connections and JDBC database connections. We inspect the required records and source interface, then design how to extract and prepare the data.
Will every connected source arrive with a ready-made model?
No. Prepared models are available for some sources; other sources need modeling during implementation. We establish the relationships and calculations for the intended questions, including any local fields or definitions the starting material does not cover.
Can our existing dbt projects run in the workflow?
Yes. A workflow can run a dbt Core project with SQL, notebooks and source syncs. Stages establish the order before delivery. Logs and saved workflow versions record the tasks and setup used for inspection.
How are changes to source structure recorded?
Each raw extract retains schema history. When a source table or column changes, the team can examine that record and follow its downstream mappings. This helps identify which transformations or reports need to be adapted.
What makes historical Workday analysis different?
Workday can store different values for a manager, compensation or cost center at different dates. Databasin preserves that effective-date history. Queries for a past period can then use the assignments that belonged to it.
Can another engine read the stored records?
Yes. Databasin stores synced records in open table formats, including Apache Iceberg, which compatible engines can query. The implementation establishes the engine and connections required by the tools your organization uses.
Can you help when the data arrives but reports disagree?
Yes. We trace the figures through their joins, periods and business definitions. The intended calculations then go into shared views. Reports can reuse those measures while analysts inspect the logic behind them.
What happens if we have no settled technical design?
We can begin with the business work you want to improve. Design establishes the outcome, current process and expected return, then shows a working example. The implementation follows from that understanding.
Discuss your data engineering needs
We can talk through the questions, applications or recurring work your organization wants to support. Whether you have an existing platform or are deciding where to begin, we can explore the implementation and help you need.