# Technical Details

- [Best Practices](/v3.4.0/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](/v3.4.0/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](/v3.4.0/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](/v3.4.0/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](/v3.4.0/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](/v3.4.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](/v3.4.0/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](/v3.4.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](/v3.4.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](/v3.4.0/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](/v3.4.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.