SQL Server (via Collector method) - v3.2.0

About Collectors

Collectors are extractors that are developed and managed by you (a customer of K).

KADA provides python libraries that customers can use to quickly deploy a Collector.

Why you should use a Collector

There are several reasons why you may use a collector vs the direct connect extractor:

1. You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions

2. You want to push metadata to KADA rather than allow it to pull data for security reasons

3. You want to inspect the metadata before pushing it to K

Using a collector requires you to manage:

1. Deploying and orchestrating the extract code

2. Managing a high water mark so the extract only pulls the latest metadata

3. Storing and pushing the extracts to your K instance

Pre-requisites

Collector Server Minimum Requirements

For the collector to operate effectively, it will need to be deployed on a server with the below minimum specifications:

• CPU: 2 vCPU

• Memory: 8GB

• Storage: 30GB (depends on historical data extracted)

• OS: unix distro e.g. RHEL preferred but can also work with Windows Server

• Python 3.10.x or later

• Access to K landing directory

SQL Server Requirements

• Access to SQL Server - SQL Server 2012 or later

• Create a SQL login (non-domain login) with the following permissions: read-only access to system views, view definition on objects, and impersonate any login

Step 1: SQL Server Setup

To configure SQL Server for use with the collector, you need to enable SQL Server authentication. You also need to create a dedicated SQL Server login and configure it with appropriate permissions.

Step 2: Create the Source in K

Create a SQL Server source in K

• Go to Settings, Select Sources and click Add Source

• Select "Load from File" option

• Give the source a Name - e.g. SQLServer Production

• Add the Host name for the SQL Server

• Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to:

1. Go to Platform Settings - Settings. Note down the value of this setting:

   • If using Azure: storage_azure_storage_account

   • If using AWS:

     • storage_root_folder - the AWS s3 bucket

     • storage_aws_region - the region where the AWS s3 bucket is hosted

2. Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section.

To connect to the landing directory you will need:

• If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)

• If using AWS:

  • An Access key and Secret. Request this from KADA Support (support@kada.ai)

  • OR provide your IAM role to KADA Support to provision access.

Step 4: Install the Collector

You can download the Latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

Step 5: Configure the Collector

kada_sqlserver_extractor_config.json

{
    "user": "",
    "password": "",
    "server": "",
    "host_name": "",
    "database_name": "",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true
}

Step 6: Run the Collector

See Collector Integration General Notes for how to run the collector and example script.

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated in the output_path directory.

High Water Mark File

A high water mark file is created called sqlserver_hwm.txt.

Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.