Storage
Where the data sits and what it costs to keep it there, including moving the estate onto a platform that can carry the tooling you want to use.
Data
Engineering
Where your data lives, what shape it arrives in, and whether it gets there on schedule.
Data Engineering Services
Transformations
Turning what you hold into something usable: cleaned tables, structured data out of documents and free text, and synthetic records where the real ones are too few.
Pipelines
The scheduled path from source to output, for data feeds and for models, with alerts when either the feed or its quality checks fail.
Frequently Asked Questions
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At Gini we build your warehouse on the platform that suits you, whether that’s Databricks, Snowflake, BigQuery, Redshift, SQL Server, SAS or any other. We clean and reshape what you already hold, and wrap it in quality rules, anomaly detection and drift reports. Feeds arrive on schedule and raise an alert when they fail or when the data fails its checks.
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Gini is agnostic to the platform. The engineering follows from first principles, so it holds wherever your data sits, and we’ve built on Databricks, Snowflake, BigQuery, Redshift, SQL Server and SAS. Partitioning and cost controls are designed in from the start, since the bill on a cloud warehouse is a design decision long before it’s a running cost. A migration in scope often puts analytic tooling within reach for the first time, such as running your SAS processes in Python.
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Yes. At Gini we extract contracts, reports and scanned documents into a standardised queryable format you can use directly as model variables, refreshed automatically, with a confidence score flagging what needs human review. Where you hold too few examples of a case to test against, we generate realistic synthetic data for it.
