DatriseAI-first ETL

CSV File to Azure Data Lake Storage Integration

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

How Datrise loads CSV File into Azure Data Lake Storage

Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

CSV File: Delimited flat files from exports and drops.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How CSV File entities map to Azure Data Lake Storage

CSV File entityAzure Data Lake Storage objectNotes
rowscsv_file_rowsid PK · custom fields → nested struct/map fields in Parquet
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 Data Lake Storage?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.

How does the CSV File to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

Related pipelines

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