Before you can extract data from Datadog, you need to understand the structure of the Datadog API and how to authenticate requests. Go to the Datadog API documentation and familiarize yourself with the endpoints that you'll be using to extract the data you need.

1. Install Redis on your server if it's not already installed. You can download it from the Redis website or use a package manager.

2. Start the Redis server by running the `redis-server` command.

3. Check if Redis is working by connecting to it using the `redis-cli` command.

1. Log in to your Datadog account.

2. Navigate to 'Integrations' > 'APIs'.

3. Find your API Key and Application Key or create new ones if necessary.

4. Store these keys securely; you'll need them to authenticate your API requests.

1. Choose a programming language that you are comfortable with and that supports HTTP requests and JSON parsing (Python, Node.js, etc.).

2. Write a script that uses the `requests` library or an equivalent to make authenticated HTTP requests to the Datadog API.

3. Parse the JSON response to extract the data you need.

4. Handle any errors or rate limits encountered during the API request.

Here's a Python example for making a simple request to the Datadog API:

```python

import requests

import json

api_key = 'your_datadog_api_key'

app_key = 'your_datadog_application_key'

# Replace with the appropriate Datadog API endpoint

url = 'https://api.datadoghq.com/api/v1/query?api_key={}&application_key={}&query=<YOUR_QUERY>&from=<TIME_FROM>&to=<TIME_TO>'.format(api_key, app_key)

response = requests.get(url)

data = response.json()

# Extract the necessary data from the response

# data = data['series'][0]['pointlist'] or similar, depending on the structure of the response

```

1. Use the Redis client in your script to connect to the Redis server.

2. Choose the appropriate Redis data structure (string, list, set, sorted set, hash, etc.) for the data you are storing.

3. Use Redis commands to insert the data into Redis.

Here's a Python example using `redis-py` to insert data into Redis:

```python

import redis

# Connect to Redis

r = redis.Redis(host='localhost', port=6379, db=0)

# Assuming data is a list of tuples (timestamp, value)

for point in data:

    timestamp, value = point

    # Use an appropriate Redis command to insert the data

    # For example, storing time series data in a sorted set:

    r.zadd('datadog_data', {json.dumps({'timestamp': timestamp, 'value': value}): timestamp})

```

To keep your Redis instance up to date with the data from Datadog, you may want to schedule your script to run at regular intervals.

1. Use a task scheduler like `cron` on Linux or Task Scheduler on Windows to run your script.

2. Determine the frequency of updates based on the volume of data and your use case.

1. Monitor your script's performance and error logs to ensure it's running as expected.

2. Update your script as needed if Datadog's API changes or if you need to modify the data being transferred.

1. Ensure that the connection to your Redis server is secure, especially if it's accessible over the internet.

2. Consider using TLS for encryption and authentication if sensitive data is being transferred.