1. Identify Data: Determine which tables or datasets you need to transfer from Teradata to your AWS Data Lake.

2. Assess Data Volume: Estimate the size of the data to ensure you have enough storage and to plan for transfer time.

3. Prepare Teradata: Ensure that you have the necessary permissions to export data from Teradata.

1. Choose a Format: Decide on a data format for the export (e.g., CSV, Avro, Parquet).

2. Use Teradata Utilities: Use Teradata's built-in utilities like `BTEQ`, `FastExport`, or `TPT` to export the data.

   - Example using `BTEQ` to export data to CSV:

     ```sql

     .LOGON your_teradata_server/your_username,your_password;

     .EXPORT REPORT FILE = your_export_path/your_data.csv;

     SELECT * FROM your_database.your_table;

     .EXPORT RESET;

     .LOGOFF;

     ```

3. Compress Data: Optionally, compress the exported files to reduce size and transfer time (e.g., gzip).

1. Set Up AWS CLI: Install and configure the AWS Command Line Interface (AWS CLI) with the necessary permissions.

2. Create S3 Bucket: If not already done, create an S3 bucket in your AWS account where the data will be stored.

   ```bash

   aws s3 mb s3://your-datalake-bucket --region your-region

   ```

3. Upload Data to S3: Use the AWS CLI to upload the exported files to the S3 bucket.

   ```bash

   aws s3 cp your_export_path/your_data.csv s3://your-datalake-bucket/path/to/data/ --recursive

   ```

1. AWS Glue: Set up an AWS Glue Data Catalog for your data lake to catalog the data.

   - Define a crawler to scan the S3 bucket and populate the Data Catalog with table definitions.

   - Run the crawler to catalog the data.

2. Amazon Athena or Redshift Spectrum: Set up Athena or Redshift Spectrum to query data directly from S3 using SQL.

   - Define the schema corresponding to your data in S3 if not already defined by AWS Glue.

   - Use Athena or Redshift Spectrum to run queries on your data.

1. Validate Data: Run test queries to ensure that the data has been correctly transferred and is accessible.

2. Optimize Storage: Convert data into columnar formats like Parquet or ORC for better performance and cost savings.

3. Partition Data: If you have large datasets, consider partitioning the data in S3 for more efficient queries.

1. Remove Local Copies: If you have exported data to a local machine, remove the copies once the transfer is verified.

2. Secure S3 Bucket: Implement proper access control policies on the S3 bucket to secure your data.

3. Monitor Usage: Set up Amazon CloudWatch to monitor access and usage of your Data Lake.

Additional Considerations

- Networking: Ensure that you have a reliable and fast network connection for the data transfer, especially for large datasets.

- Incremental Updates: If you need to synchronize data regularly, plan for incremental updates rather than full transfers.

- Compliance and Data Governance: Make sure that your data transfer complies with data governance and regulatory requirements.

By following these steps, you should be able to move data from Teradata to an AWS Data Lake without third-party connectors or integrations. Keep in mind that while this method avoids third-party tools, it may require more manual effort and maintenance than using dedicated data integration services.