DatriseAI-first ETL

CSV File to Azure Synapse Integration

AI-first ETL from CSV File into Azure Synapse. Governed entities, incremental sync, typed landing tables.

How Datrise loads CSV File into Azure Synapse

Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings into Azure Synapse as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.

Ideal for Azure analytics estates feeding Power BI.

Endpoints

CSV File: Delimited flat files from exports and drops.

Azure Synapse: Microsoft analytics workspace with SQL pools.

How CSV File entities map to Azure Synapse

CSV File entityAzure Synapse objectNotes
rowscsv_file_rowsid PK · custom fields → NVARCHAR(MAX) JSON columns
columnscsv_file_columnsid PK · linked to csv_file_rows
headerscsv_file_headersid PK · linked to csv_file_rows
delimiterscsv_file_delimitersid PK · linked to csv_file_rows

FAQ

How does Datrise handle CSV File's custom fields in Azure Synapse?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Synapse types.

How does the CSV File to Azure Synapse sync stay up to date?

It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.

Related pipelines

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