Before you begin the data transfer, ensure that you have an Elasticsearch cluster set up and accessible. You could use a local instance for testing or a cloud-based service.

1. Download Elasticsearch from the official website or use a cloud service provider like Elastic Cloud.

2. Follow the installation instructions specific to your platform.

3. Start the Elasticsearch server and ensure it is running by accessing `http://localhost:9200` in a web browser or using a tool like `curl`.

You need to create an index in Elasticsearch where the data will be stored. Define the appropriate mappings that correspond to the data types in your SQL Server database.

1. Use the Kibana console or a tool like `curl` to create an index.

2. Define the mappings for the fields based on your SQL data.

Example:

```json

PUT /my_index

{

  "mappings": {

    "properties": {

      "field1": { "type": "text" },

      "field2": { "type": "date" },

      "field3": { "type": "integer" }

      // Add mappings for all fields you plan to transfer

    }

  }

}

```

To extract data from SQL Server, you can use a scripting language like Python with a library such as `pyodbc` or `pymssql`.

1. Install the necessary library (e.g., `pip install pyodbc`).

2. Write a script to connect to your SQL Server database.

3. Execute a query to retrieve the data you want to transfer.

4. Fetch the results and store them in a format that can be ingested by Elasticsearch (typically JSON).

Example Python script using `pyodbc`:

```python

import pyodbc

import json

# Connect to SQL Server

conn = pyodbc.connect('DRIVER={SQL Server};SERVER=your_server;DATABASE=your_db;UID=your_user;PWD=your_password')

cursor = conn.cursor()

# Execute a query

cursor.execute('SELECT * FROM YourTable')

# Fetch the results

rows = cursor.fetchall()

# Convert to JSON

data = [dict(zip([column[0] for column in cursor.description], row)) for row in rows]

# Close the connection

cursor.close()

conn.close()

```

Once you have the data in JSON format, you can use Elasticsearch's Bulk API to load the data.

1. Create a new Python script or extend the existing one to send the JSON data to Elasticsearch.

2. Use the `requests` library to make HTTP POST requests to the Elasticsearch Bulk API endpoint.

Example Python script snippet to load data:

```python

import requests

# Elasticsearch URL

es_url = 'http://localhost:9200/my_index/_bulk'

# Prepare bulk payload

bulk_payload = ''

for record in data:

    # Add index operation metadata

    bulk_payload += json.dumps({"index": {"_index": "my_index"}}) + '\n'

    # Add the document data

    bulk_payload += json.dumps(record) + '\n'

# Set the appropriate content type for bulk upload

headers = {'Content-Type': 'application/x-ndjson'}

# Make the POST request

response = requests.post(es_url, data=bulk_payload, headers=headers)

# Check for errors

if response.status_code != 200:

    print("Error:", response.text)

else:

    print("Data loaded successfully")

```

After loading the data into Elasticsearch, verify that the data has been correctly indexed and is queryable.

1. Use Kibana or `curl` to run a few test queries against the Elasticsearch index.

2. Compare the results with the original data in SQL Server to ensure that the data transfer was successful and the data integrity is maintained.

Example query using `curl`:

```shell

curl -X GET "localhost:9200/my_index/_search?pretty" -H 'Content-Type: application/json' -d'

{

  "query": {

    "match_all": {}

  }

}

'

```

For recurring data transfers, consider automating the process with a scheduled job or script. This could be done using cron jobs on Linux or Task Scheduler on Windows.

Important Considerations

- Ensure that your Elasticsearch cluster is properly secured, especially if it is accessible over the internet.

- Be mindful of the data volume and Elasticsearch's indexing performance. Large data sets may require batch processing and tuning of Elasticsearch settings.

- Always test the entire process with a subset of data before moving to production.

- Make sure to handle any data transformations that are necessary for Elasticsearch during the data extraction or loading phase.

- Keep in mind that this manual approach does not provide real-time synchronization. If real-time data transfer is required, you may need to implement a more complex solution or consider using a third-party connector.