Before starting, ensure you have access to both your ClickHouse server and AWS account. You'll need permissions to read data from ClickHouse and write data to AWS S3, which will serve as your data lake storage. Install necessary command-line tools like `clickhouse-client` and AWS CLI.

Use ClickHouse's native export capabilities to extract data. You can run a query and export the result to a CSV or TSV file. For example, use:
```bash
clickhouse-client --query="SELECT FROM your_table FORMAT CSV" > /path/to/exported_file.csv
```
This command exports the data from `your_table` into a CSV file on your local machine.

Ensure the AWS CLI is installed and configured on your system. If not, install it and configure it by running:
```bash
aws configure
```
Enter your AWS Access Key, Secret Access Key, region, and output format when prompted. This configuration will allow you to interact with AWS services.

If you don't have an S3 bucket for your data lake, create one. Use the AWS Management Console or the AWS CLI:
```bash
aws s3 mb s3://your-bucket-name
```
Replace `your-bucket-name` with your desired bucket name. Ensure the bucket is created in a region close to your ClickHouse server for faster data transfer.

Upload the exported file from your local machine to the S3 bucket. Use the AWS CLI to do this:
```bash
aws s3 cp /path/to/exported_file.csv s3://your-bucket-name/path/to/destination/
```
Replace the paths accordingly. This command transfers your data file to the specified location in your S3 bucket.

After the transfer, verify that the data is correctly uploaded to your S3 bucket. You can list the contents of your bucket using:
```bash
aws s3 ls s3://your-bucket-name/path/to/destination/
```
Check if your file is present and has the correct size.

To query your data in the S3-based data lake, set up AWS Athena. Define a table pointing to the location of your CSV file in S3. Use the Athena console or write DDL statements like:
```sql
CREATE EXTERNAL TABLE your_table_name (
column1_name column1_type,
column2_name column2_type,
...
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION 's3://your-bucket-name/path/to/destination/';
```
Adjust column names and types as per your dataset. Now you can use Athena to query the data stored in your AWS Data Lake.

By following these steps, you'll successfully move data from ClickHouse to an AWS Data Lake without relying on third-party connectors or integrations.