DatriseAI-first ETL

Polygon Stock API to Azure Data Lake Storage Integration

AI-first ETL from Polygon Stock API into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads Polygon Stock API into Azure Data Lake Storage

Datrise syncs Polygon Stock API's tickers, aggregate bars, trades, quotes, splits, and dividends 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

Polygon Stock API: Equities market data, trades, and aggregates.

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

How Polygon Stock API entities map to Azure Data Lake Storage

Polygon Stock API entityAzure Data Lake Storage objectNotes
tickerspolygon_stock_api_tickersid PK · custom fields → nested struct/map fields in Parquet
aggregate barspolygon_stock_api_aggregate_barsid PK · linked to polygon_stock_api_tickers
tradespolygon_stock_api_tradesid PK · linked to polygon_stock_api_tickers
quotespolygon_stock_api_quotesid PK · linked to polygon_stock_api_tickers

FAQ

How does Datrise handle Polygon Stock API'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 Polygon Stock API 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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