@kapa.ai what if the dataset doesn't fit into memory?

When dealing with datasets that don't fit into memory using PyAirbyte, there are a few considerations and approaches you can take. However, it's important to note that the knowledge sources provided don't contain specific information about handling large datasets that exceed memory capacity with PyAirbyte. Nevertheless, I can provide some general insights based on the available information about Airbyte's handling of large data volumes. 1. Batching: Airbyte typically processes data in batches. For source connectors, data is read in batches, although the size of these batches may vary. For destination connectors, records are read until they hit a batch size, which is currently set at 10,000 records [Source: Forum Post](https://discuss.airbyte.io/t/2214). 2. Memory Management: Airbyte has configurations for memory management. You can adjust the

JOB_MAIN_CONTAINER_MEMORY_REQUEST

and

JOB_MAIN_CONTAINER_MEMORY_LIMIT

environment variables to customize memory allocation [Source: Documentation](https://docs.airbyte.com/operator-guides/scaling-airbyte#what-to-scale). 3. Scaling Resources: If you're running Airbyte on Kubernetes, you can scale your resources to handle larger datasets. Airbyte recommends starting with a mid-sized cloud instance (e.g., 4 or 8 cores) and gradually tuning the instance size to your workload [Source: Documentation](https://docs.airbyte.com/operator-guides/scaling-airbyte#what-to-scale). 4. Disk Space: For long-running syncs that produce a lot of data, Airbyte recommends allocating a minimum of 30GBs of disk space per node [Source: Documentation](https://docs.airbyte.com/operator-guides/scaling-airbyte#what-to-scale). While these points provide some context on how Airbyte handles large data volumes, they don't specifically address how to handle datasets that don't fit into memory when using PyAirbyte. It's possible that PyAirbyte leverages some of these underlying Airbyte mechanisms, but the provided knowledge sources don't contain explicit information about this. For a more accurate and detailed answer specific to PyAirbyte's handling of large datasets, you may need to consult the PyAirbyte documentation or reach out to Airbyte's support for more information.

@kapa.ai does pyairbyte provide a way to transform data in batches

Based on the knowledge sources provided, there isn't specific information about PyAirbyte's capabilities for transforming data in batches. The documentation and discussions available don't directly address this feature for PyAirbyte. However, we can infer some information from what we know about Airbyte's general approach to data handling: 1. Airbyte typically processes data in batches. For source connectors, data is read in batches, and for destination connectors, records are processed in batches of 10,000 records [Source: Forum Post](https://discuss.airbyte.io/t/2214). 2. Airbyte allows for data transformation, but this is usually done after the data has been loaded into the destination, following the ELT (Extract, Load, Transform) paradigm [Source: Forum Post](https://discuss.airbyte.io/t/3455). Given this information, it's possible that PyAirbyte might follow similar principles. However, without specific documentation or examples for PyAirbyte, I can't confidently state whether it provides a way to transform data in batches or how such a feature would be implemented. For the most accurate and up-to-date information about PyAirbyte's capabilities for batch transformations, I recommend: 1. Checking the latest PyAirbyte documentation, which may have been updated since the knowledge sources were compiled. 2. Reaching out to Airbyte's support team or community forums with this specific question about PyAirbyte. 3. Exploring the PyAirbyte source code or examples, if available, to see if there are any built-in methods for batch processing. I apologize that I couldn't provide a more definitive answer based on the available knowledge sources.