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The interoperability chain of PyArrow components

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The Interoperability Chain

Here is how PyArrow concepts flow together in a real-world pipeline:

  1. Storage (Parquet/Cloud): You have a massive dataset of Struct Arrays stored in Parquet on S3.

  2. Discovery (Dataset API): You use the Dataset API to scan those files. It uses Partition Pruning to only look at the folders you need.

  3. Processing (Streaming): You don't load the whole thing. You Stream the data as a sequence of RecordBatches.

  4. Hand-off (IPC): You send those batches from your Python worker to a Golang service using the IPC Stream format.

  5. Zero-Copy: The Go service receives the bytes and, because it understands the Arrow IPC protocol, it accesses the data with Zero-Copy. It doesn't "import" the data; it just starts reading the memory.

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Where does ADBC fit in the chain?

If Arrow is the shared language, and IPC is the telephone line, then ADBC (Arrow Database Connectivity) is the standardized connector to databases.

Before ADBC, if you wanted to get data out of Postgres or Snowflake into Arrow, you had to use ODBC or JDBC. Those protocols are "row-based," so the database had to convert its data into rows, send it to you, and then you had to re-package it back into Arrow columns. That's a massive waste of CPU.

ADBC allows the database to stream Arrow RecordBatches directly to your application.

The updated chain:

  1. Source: Snowflake/Postgres (via ADBC).

  2. Transfer: Data arrives in your Python/Go app already as RecordBatches.

  3. Processing: You use the Dataset API or Compute functions.

  4. Storage: You save the result to S3 (Parquet) or Local (Feather).

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