Azure Synapse (via Collector method) - v3.0.0

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About Collectors

Collector Method

Pre-requisites

Collector server minimum requirements

Azure Synapse Requirements

Setting up Azure Synapse for metadata extraction using a service principal (Application).

Step 1: Establish Azure Synapse Access

Apply in MASTER using an Azure Synapse Admin user

CREATE USER [<SERVICE_PRINCIPAL_NAME>] FROM EXTERNAL PROVIDER;

Apply per database in scope for metadata collection.

CREATE USER [<SERVICE_PRINCIPAL_NAME>] FROM EXTERNAL PROVIDER;
GRANT VIEW DEFINITION TO [<SERVICE_PRINCIPAL_NAME>];
GRANT VIEW DATABASE STATE TO [<SERVICE_PRINCIPAL_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

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

Collector Method

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.whl

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

Note that you will also need an ODBC package installed at the OS level for pyodbc to use as well as a SQLServer ODBC driver, refer to https://docs.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver15

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

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 `Example: “<workspace-name>.sql.azuresynapse.net,1433”`	“<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”
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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+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

Collector Method