Professional retrospective · Martelo
Building and operating a Databricks platform from scratch
End-to-end technical ownership of a Databricks platform for analytics and production ML, including reliability, governance, infrastructure and cost.
Context and ownership
The platform and my responsibility.
Context
Martelo is a startup where I led the technical architecture and implementation of the company's Databricks data platform from the ground up, then owned its ongoing production operation.
Mandate
Connect source data to dependable analytics and ML delivery through common platform patterns, clear ownership and production controls.
What I owned
- Initial architecture, implementation and ongoing platform operations.
- Ingestion, batch and streaming pipelines, and the analytics warehouse.
- Data-quality controls, observability, governed interfaces and production recovery.
- User access, AWS integration, infrastructure operations, compute and memory sizing, and cost management.
- Data foundations and model delivery for production ML consumers.
Decisions and trade-offs
Why I chose this approach.
A common Databricks foundation on AWS
I chose Databricks on AWS to give analytics and production ML a shared path from ingestion to governed delivery.
- Alternative considered
- A more component-based AWS-native design, with separate services and integration paths for processing, analytics and model delivery.
- Why it fit
- The team needed a platform it could operate consistently. A common foundation reduced the number of handoffs and separate operating processes while making reusable ingestion, quality and access patterns practical.
- Trade-off
- A managed platform still required active governance and cost control. I owned those responsibilities alongside implementation and daily operation.
Compute matched to the way each workload runs
I separated scheduled processing, continuous workloads and interactive use, then adjusted compute and memory against workload behaviour.
- Alternative considered
- Relying on shared interactive resources for workloads with different execution patterns.
- Why it fit
- Batch, streaming and interactive users place different demands on the platform. Separating those needs made capacity, reliability and resource use easier to manage.
- Trade-off
- Separate compute paths added configuration to maintain. Reusable patterns and ongoing observation kept the arrangement manageable.
Engineering focus
How the work connected architecture and operation.
Own the source-to-consumer path
My scope connected ingestion, transformation, analytics and model delivery with data-quality and operating controls across the platform lifecycle.
Make reliable delivery repeatable
Established reusable ingestion and transformation patterns, governed interfaces and production controls for analytics and ML teams.
Size resources for the workload
Right-sized compute and memory in Databricks and AWS, balancing workload performance, reliability, scaling requirements and infrastructure costs.
Production outcomes
Results with a defined scope.
Data-quality incidents
Reduction reported for the Databricks platform's data-quality incidents.
Model-serving SLO
Service objective established for model-serving workloads. This target is scoped to model serving.
Measurement context and publication scope
These are professional outcomes described in my career record. Baseline dates, the incident taxonomy and internal monitoring records are not published in this retrospective. The model-serving SLO is reported separately from the data-quality result.