Begin by examining the structure of your data in Gridly. Identify the columns, data types, and any potential nested structures. This will help you map the data appropriately to Redis, which typically uses key-value pairs.

Export the data from Gridly in a CSV format. Most spreadsheet tools, including Gridly, support exporting data as CSV. This format is universally readable and can be easily parsed using scripts.

Ensure that you have a running instance of Redis that you can connect to. You can set up Redis on your local machine, a virtual machine, or use a cloud-based service. Ensure that you have the necessary permissions and network access.

Write a script in a programming language like Python to parse the exported CSV file. You can use libraries such as `csv` in Python to read and iterate over the rows of the file. This script will help in preparing the data for insertion into Redis.

Decide on how each row of your CSV will be stored in Redis. You might choose to store each row as a separate hash, with each column as a field in the hash. Alternatively, you can use lists or sets depending on your use case and access patterns.

Using a Redis client library in your chosen programming language (e.g., `redis-py` for Python), connect to your Redis instance and write the parsed data. Loop through each row of your CSV and use the appropriate Redis commands to insert data. For example, use `HMSET` to insert a hash or `SET` for simple key-value pairs.

After transferring the data, ensure that the data in Redis matches the original data in Gridly. You can do this by reading back the data from Redis and comparing it with your CSV file. Implement logging in your script to record any discrepancies or errors during the process.

By following these steps, you can effectively move data from Gridly to Redis without the need for third-party connectors or integrations.