Azure Synapse (via Collector method) - v3.0.0
- Ibrahim
- Ben
About Collectors
Pre-requisites
Collector server minimum requirements
Azure Synapse Requirements
Setting up Azure Synapse for metadata extraction using a service principal (Application).
Step 1: Registing an Entra App for KADA Azure Synapse Collector Application
Create an Entra Application for the kada synapse collector or reusing an existing kada application in Entra. TBD (Same steps as setup for power bi / AD integration. https://docs.kada.ai/home/powerbi#PowerBI-Step2)RegisteringPowerBIAppinAzureAD
Generate a secret for the Entra Application and note down the application id, tenant id.
Step 2: Establish Azure Synapse Access
Apply in MASTER using an Azure Synapse Admin user
CREATE USER [<ENTRA APPLICATION NAME>] FROM EXTERNAL PROVIDER; Apply per database in scope for metadata collection.
CREATE USER [<ENTRA APPLICATION NAME>] FROM EXTERNAL PROVIDER;
GRANT VIEW DEFINITION TO [<ENTRA APPLICATION NAME>];
GRANT VIEW DATABASE STATE TO [<ENTRA APPLICATION NAME>];The following table should also be available to SELECT by the user created in each database
INFORMATION_SCHEMA.ROUTINES
INFORMATION_SCHEMA.VIEWS
INFORMATION_SCHEMA.TABLE_CONSTRAINTS
INFORMATION_SCHEMA.CONSTRAINT_COLUMN_USAGE
INFORMATION_SCHEMA.TABLES
INFORMATION_SCHEMA.COLUMNS
INFORMATION_SCHEMA.VIEWS
sys.foreign_key_columns
sys.objects
sys.tables
sys.schemas
sys.columns
sys.databases
Synapse has the concept of a serverless and dedicated sql pool per workspace.
If you use both serverless and dedicated sql pools in a workspace you will need to onboard each as a separate source in K.
For serverless sql pools the master database can’t be selected for metadata extraction.
To onboard multiple Synapse workspace each workspace will need to be onboarded as a new source in K.
Step 1: Create the Source in K
Create a 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 Azure Production
Add the Host name for the SQLServer Azure Instance
Click Next & Finish Setup
Step 2: Getting Access to the Source Landing Directory
Step 3: Install the Collector
It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.
Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.
You can download the Latest Core Library and Azure Synapse whl via Platform Settings → Sources → Download Collectors
Run the following command to install the collector
pip install kada_collectors_extractors_azure_synapse-3.0.0-py3-none-any.whlYou will also need to install the corresponding common library kada_collectors_lib-x.x.x for this collector to function properly.
Step 4: Configure the Collector
The collector requires a set of parameters to connect to and extract metadata from Azure Synapse.
FIELD | FIELD TYPE | DESCRIPTION | EXAMPLE |
|---|---|---|---|
client | string | Onboarded client in Azure to access Azure Synapse |
|
secret | string | Onboarded client secret in Azure to access Azure Synapse |
|
tenant | string | Tenant ID of where Azure Synapse exists |
|
server | string | Azure Synapse server. If using a custom port append with comma | “<workspace-name>.sql.azuresynapse.net,1433” |
host | string | The onboarded host value in K, generally this would be the same as the server value, depending on what you onboard it as. | “<workspace-name>.sql.azuresynapse.net,1433” |
database_name | string | The name of the database that will be used to test the connection | master Note for serverlesss. sqlpools master is not accessible. |
databases | list<string> | A list of databases to extract from SQLServer Azure | [“dwh”, “adw”] |
driver | string | This is the ODBC driver, generally its ODBC Driver 17 for SQL Server, if you another driver installed please use that instead. | “ODBC Driver 17 for SQL Server” |
meta_only | boolean | Do you want to extract metadata only without enabling extended events? We currently only support true | true |
output_path | string | Absolute path to the output location where files are to be written | “/tmp/output” |
mask | boolean | To enable masking or not | true |
compress | boolean | To gzip the output or not | true |
connection_timeout | integer | Timeout in seconds allowed against Synapse Sql Pool connection, this is defaulted as 30 | 30 |
These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.
kada_azure_synapse_extractor_config.json
Step 5: Run the Collector
The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.
This can be executed in any python environment where the whl has been installed.
This code sample uses the kada_azure_synapse_extractor.py for handling the configuration details
Advance options:
If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location
If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method
client: The Client ID for the registered application for access to Azure Synapse
secret: The Secret for the registered application for access to Azure Synapse
tenant: The Tenant ID for the registered application for access to Azure Synapse
server: Azure Synapse server
host: the onboarded host value in K, generally it will be the same as the server
driver: sqlserver driver name
databases: list of databases to extract
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
meta_only: To extract without extended events or not
connection_timeout: Synapse SQL Pool timeout for the connection in seconds
Step 6: Check the Collector Outputs
K Extracts
A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details
High Water Mark File
A high water mark file is created in the same directory as the execution called azure_synapse_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.
Step 7: Push the Extracts to K
Once the files have been validated, you can push the files to the K landing directory.
You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)
Example: Using Airflow to orchestrate the Extract and Push to K