An instant data lake — governed on arrival.

Point at a location; get governed tables

Point Provisa at a folder, a bucket, or a SharePoint site and it crawls the whole tree recursively, discovers every file, and registers each one as a governed table. Not just CSV and Parquet — Excel workbooks, and even Word, PowerPoint, HTML, and Markdown documents converted to tables. Over local disk, s3://, ftp://, sftp://, or SharePoint.

The result is a data lake that is governed the moment it exists, rather than one that gets a governance project scheduled against it months later.

Systems that aren't databases

Connect SharePoint and its lists enumerate as schemas you can query. Run a Splunk search and the results come back as a table you can JOIN to your business data. A live Google Sheet is just another table — and so is a REST, GraphQL, gRPC, WebSocket, or RSS endpoint, registered as a governed table that federated joins reach transparently.

Shaping is a declaration, not a program

Non-relational data is where pipelines are born: the response is nested, the document has an array inside it, the location is a pile of files, so somebody writes code to flatten it and a procedure enters the path permanently. We start from the premise that the variety is smaller than it looks — a modest set of structure types, each with a short declarative rule for projecting it into rows and columns. The path to the collection and the fields to lift from it. A sampled document schema, with the steward's explicit types overriding what was inferred. A crawled location that registers what it finds. Once it's flat, everything after it is SQL, and SQL is a thing a machine can read back.

Why that removes the downstream pain

Graph and RDF are first-class

Neo4j and SPARQL sources are not adapters bolted onto a relational core. They participate in the same federated schema, the same relationship registry, and the same governance layers — which is what makes a Cypher traversal across a relational source and a graph source a single legal query rather than two queries and a join in application code.

The 52

PostgreSQL, MySQL, MongoDB, Cassandra, Elasticsearch, Neo4j, SPARQL, Kafka, Snowflake, Databricks, BigQuery, Fabric, ClickHouse, DuckDB, Oracle, SQL Server, Google Sheets, CSV, Parquet, Excel, DOCX, PPTX, HTML, Markdown, SharePoint, Splunk, S3, FTP, SFTP, REST, GraphQL, gRPC, RSS, and WebSocket endpoints among them. The full, current list lives in the source reference.

Observability as data

Traces, metrics, and logs are collected via OpenTelemetry, compacted into Iceberg, and registered as queryable tables — so the audit trail is a source like any other, joinable to the business data it describes.