Ensure you have the required Python libraries to read Parquet files and interact with Redis. You will need `pyarrow` to handle Parquet files and `redis-py` to interact with Redis. Install them using pip:
```bash
pip install pyarrow redis
```

Use `pyarrow` to read the Parquet file into a Pandas DataFrame. This allows you to easily manipulate and iterate over the data. Here's a basic example:
```python
import pyarrow.parquet as pq

parquet_file = 'your_data.parquet'
table = pq.read_table(parquet_file)
dataframe = table.to_pandas()
```

Establish a connection to your Redis server using the `redis` library. You need to know the host and port of your Redis server:
```python
import redis

redis_client = redis.StrictRedis(host='localhost', port=6379, db=0)
```

Before inserting data into Redis, ensure it fits the desired structure. You may need to loop through the DataFrame and convert rows to the appropriate format for Redis, such as JSON strings or hash maps.

Iterate over the DataFrame and insert each row into Redis. Choose the appropriate data structure (e.g., string, hash) based on your use case. For example, to store each row as a hash:
```python
for index, row in dataframe.iterrows():
redis_key = f"row:{index}"
redis_client.hmset(redis_key, row.to_dict())
```

After insertion, verify that the data is correctly stored in Redis by retrieving a few entries and checking their content. This can be done using:
```python
retrieved_data = redis_client.hgetall("row:0")
print(retrieved_data)
```

Consider optimizing your data storage in Redis by selecting the right data types and structures. Ensure that your script handles exceptions and errors gracefully. Finally, clean up any temporary data or close connections if necessary.

This step-by-step guide helps in moving data from a Parquet file to Redis without relying on third-party connectors, ensuring you have full control over each part of the process.