> For the complete documentation index, see [llms.txt](https://docs-v3.activeloop.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/storage-synchronization.md).

# Storage Synchronization

## How Deep Lake Datasets are Synchronized with Long-Term Storage

{% hint style="danger" %}
**Using `with` context when updating Deep Lake datasets is critical for achieving rapid write performance.**
{% endhint %}

### BAD PRACTICE - Code without `with` context

By default, any standalone update to a Deep Lake dataset is immediately pushed to the dataset's long-term storage location. Due to the sheer number of discreet write operations, there may be a significant increase in runtime, especially when the data is stored in the cloud. In the example below, an update is pushed to storage for every call to the `.append()` command.

```python
for i in range(10):
    ds.my_tensor.append(i)
```

### Code using `with` context

To reduce the runtime when using Deep Lake, the `with` syntax below significantly improves performance because it only pushes updates to long-term storage after the code block inside the `with` statement has been executed, or when the local cache is full. This significantly reduces the number of discreet write operations, thereby increasing the speed by up to 100X. 

```python
with ds:
    for i in range(10):
        ds.my_tensor.append(i)
```