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 read the same tables instead of each keeping a copy, which removes a category of reconciliation work rather than making it faster.

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: workspace topology and how many you actually 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 the difference between an estate you can govern and one you inherit and 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 rather than duplicates. The next layer cleans, conforms and deduplicates, with data quality expectations declared alongside the transformation instead of checked by a report afterwards. 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, because 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 without a big-bang cutover. The sequence is inventory and dependency mapping first, then choosing what to rebuild rather than translate, then running old and new in parallel with row and aggregate reconciliation until the numbers agree, then cutting consumers over in groups. Decommissioning is planned as part of the project, because 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 rather than being retyped in each dashboard, 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 promotion paths, retraining triggers and rollback are defined 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 treated as an engineering requirement, not a monthly surprise: cluster policies and instance choices, job clusters instead of always-on compute, autoscaling and auto-termination, serverless where the workload suits it, and tagging so spend can be attributed to a team or product. Alerting is set up 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 granted to groups synchronized from your identity provider rather than to individuals, and lineage and audit history available for review. Governance is configured during the build with your security and compliance teams rather than retrofitted after an audit finding.

Engineering Practice and Handover

Code lives in version control, pipelines and jobs are deployed through a promotion path from development to production rather than edited in a notebook in production, and infrastructure and job definitions are declared as configuration. 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.