Databricks Services

The Databricks Data Intelligence Platform puts one compute and governance layer over data sitting in cloud object storage. Delta Lake adds transactions, schema enforcement and time travel to files in that storage. Spark and the SQL engine query them, Unity Catalog governs who can see what, and MLflow tracks the models built on top. The practical effect is that engineering, analytics and machine learning all read the same tables. Each team stops keeping its own copy, and a whole category of reconciliation work disappears.

Iseyon does the delivery work on that platform: designing the layout, moving workloads onto it from whatever they run on now, governing access, controlling what it costs, and handing the running estate to your team.

Data Platform & Engineering

Platform Foundation

Iseyon sets up the parts that are painful to change later. That means workspace topology and how many workspaces you need, the metastore and catalog structure separating development, test and production, storage layout and external locations, identity and group provisioning from your existing directory, and cluster policies that constrain what users can spin up. Getting this in place before the first pipeline is what makes the estate governable. Skip it and you inherit one you have to audit.

Medallion Architecture and Pipelines

Iseyon builds in layers, with a clear rule about what each one is allowed to do. Raw arrivals land as they came, incrementally and idempotently, so a replay produces the same result every time. The next layer cleans, conforms and deduplicates, with data quality expectations declared alongside the transformation so bad rows are caught as they move. The serving layer holds business-defined tables that BI tools and models read, with the definitions agreed with the people who own them. Table maintenance, file layout and clustering are part of the build. A lakehouse left unmaintained degrades quietly into small files and slow scans.

Migration From Legacy Platforms

Iseyon moves workloads off legacy warehouses and Hadoop estates in stages. Inventory and dependency mapping come first. Then we decide which workloads to rebuild and which to translate, run old and new in parallel with row and aggregate reconciliation until the numbers agree, and cut consumers over in groups. Decommissioning is planned as part of the project. A legacy platform kept alive next to the new one is the most common way a migration fails to pay for itself.

SQL Analytics and BI Serving

Iseyon exposes the serving layer through SQL warehouses to the BI tools your organization already uses, with the concurrency, caching and sizing set against real query patterns. Metric definitions live in the platform, so two reports asking the same question return the same answer.

Machine Learning, Cloud & Governance

Machine Learning and AI Delivery

Iseyon supports the model lifecycle on the same governed tables: feature engineering, experiment tracking and model registry through MLflow, batch and real-time serving, and monitoring for drift and data quality once the model is live. The engineering around a model is usually where projects stall. So we define promotion paths, retraining triggers and rollback before the first model reaches production.

Cloud Architecture and Cost Control

Iseyon deploys on AWS, Azure or Google Cloud, inside your networking and security constraints. Cost is an engineering requirement we design for: cluster policies and instance choices, job clusters for scheduled work, autoscaling and auto-termination, serverless where the workload suits it, and tagging so spend can be attributed to a team or product. Alerting fires on job failure and on cost anomalies alike.

Governance With Unity Catalog

Iseyon implements access control at catalog, schema, table, row and column level, with masking where sensitive fields need it. Permissions go to groups synchronized from your identity provider, and lineage and audit history are available for review. We configure governance during the build with your security and compliance teams, so an audit finds it already in place.

Engineering Practice and Handover

Code lives in version control. Pipelines and jobs move through a promotion path from development to production, and infrastructure and job definitions are declared as configuration. Nobody edits a notebook in production. Handover covers architecture documentation and the reasoning behind it, runbooks for the failure modes we saw during the build, catalog and permission administration, the cost picture with the levers that move it, and enablement for the engineers and analysts who will extend the platform next.

Data Architecture: Legacy Warehouse and Lakehouse Compared

Structural differences between the two architectures, without reference to any particular workload.