1. Install PostgreSQL: Follow the official documentation to install PostgreSQL on your system.

2. Install Elasticsearch: Similarly, follow the Elasticsearch documentation to install and configure Elasticsearch.

We'll use Python for this guide since it has good support for both PostgreSQL and Elasticsearch.

1. Install Python: Make sure Python is installed on your system.

2. Set up a Python virtual environment (optional but recommended):

   ```

   python -m venv venv

   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

   ```

3. Install the necessary Python packages:

   ```

   pip install psycopg2-binary elasticsearch

   ```

1. Connect to PostgreSQL:

   ```python

   import psycopg2   

   conn = psycopg2.connect(

       dbname="your_database",

       user="your_username",

       password="your_password",

       host="your_host"

   )

   cursor = conn.cursor()

   ```

2. Query the data you want to move:

   ```python

   cursor.execute("SELECT * FROM your_table")

   rows = cursor.fetchall()

   ```

1. Define a mapping for the Elasticsearch index if necessary. Elasticsearch can create mappings automatically, but defining one can give you more control over the indexing process.

2. Transform the PostgreSQL data into a JSON format suitable for Elasticsearch. This typically involves converting each row into a dictionary where the keys are the column names:

   ```python

   columns = [desc[0] for desc in cursor.description]

   data_to_index = [dict(zip(columns, row)) for row in rows]

   ```

1. Connect to Elasticsearch:

   ```python

   from elasticsearch import Elasticsearch

   es = Elasticsearch(hosts=["localhost:9200"])

   ```

2. Create an index in Elasticsearch if it doesn't already exist:

   ```python

   index_name = "your_index"

   if not es.indices.exists(index=index_name):

       es.indices.create(index=index_name)

   ```

3. Bulk index the data into Elasticsearch:

   ```python

   from elasticsearch.helpers import bulk   

   actions = [

       {

           "_index": index_name,

           "_type": "_doc",

           "_source": data,

       }

       for data in data_to_index

   ]

   bulk(es, actions)

   ```

1. Check the data count in both PostgreSQL and Elasticsearch to ensure they match.

2. Query Elasticsearch for a few records to confirm that the data has been indexed correctly.

1. Close the PostgreSQL cursor and connection:

   ```python

   cursor.close()

   conn.close()

   ```

2. Close the Elasticsearch connection if necessary (Elasticsearch's Python client uses persistent connections).

Additional Notes:

- Error Handling: Make sure to add error handling to your script to deal with issues that may arise during the data migration process.

- Logging: Implement logging to track the progress and any issues that occur.

- Data Transformation: Depending on the complexity of your data, you may need to perform more complex transformations before indexing.

- Performance: For large datasets, consider batching the data transfer to avoid memory issues and to improve performance.

- Security: Ensure that any sensitive data is handled securely and that both your PostgreSQL and Elasticsearch instances are properly secured.

By following these steps, you should be able to move data from PostgreSQL to Elasticsearch without using third-party connectors or integrations. Remember to test your migration process with a small dataset first before proceeding with the full migration.