Microsoft Azure provides a wide number of services for managing and storing data. One product is Microsoft Azure SQL. Which gives us the capability to create and manage instances of SQL Servers hosted in the cloud. This project, demonstrates how to use these services to manage data we collect from different sources.
To use this project you will need to install some dependencies to connect to the database. To download the drivers needed go to to Microsoft SQL Drivers for Python. Once you download it, run through the installation process.
Resources - PYODBC with Azure:
If you would like to read more on the topic of using PYODBC in conjunction with Microsoft Azure, then I would refer you to the documentation provided by Microsoft.
Right now, this project is not planned to be hosted on PyPi so you will need to do a local install on your system if you plan to use it in other scrips you use. First, clone this repo to your local system.
Setup - Local Install:
If you plan to use this project in other projects on your system, I would recommend you either install this project in
editable mode or do a
local install. For those of you, who want to make modifications to this project. I would recommend you install the library in
If you want to install the library in
editable mode, make sure to run the
setup.py file, so you can install any dependencies you may need. To run the
setup.py file, run the following command in your terminal.
pip install -e .
If you don't plan to make any modifications to the project but still want to use it across your different projects, then do a local install.
pip install .
This will install all the dependencies listed in the
setup.py file. Once done you can use the library wherever you want.
Setup - Requirement Install:
If you don't plan to use this project in any of your other projects, I would recommend you just install the dependencies by using the
pip install --requirement requirements.txt
Here is a simple example of using the
azure_sql_pipeline library to grab a specific database from our SQL Server.
from pprint import pprint from configparser import ConfigParser from azure_data_pipeline.client import AzureSQLClient # Initialize the Parser. config = ConfigParser() # Read the file. config.read('config/config.ini') # Grab the Azure Management Credentials. subscription_id = config.get('azure_credentials', 'azure_subscription_id') tenant_id = config.get('azure_credentials', 'azure_tenant_id') client_id = config.get('azure_credentials', 'azure_client_id') client_secret = config.get('azure_credentials', 'azure_client_secret') # Grab the Azure SQL Server Credentials. server_username = config.get('server_info', 'administrator_login') server_password = config.get('server_info', 'administrator_login_password') # Initialize the client. azure_pipeline_client = AzureSQLClient( client_id=client_id, client_secret=client_secret, subscription_id=subscription_id, tenant_id=tenant_id, username=server_username, password=server_password )
Source Code: https://github.com/riyavishwa981/Azure-SQL-Database-Pipeline-Project
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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In this article, you learn how to set up Azure Data Sync services. In addition, you will also learn how to create and set up a data sync group between Azure SQL database and on-premises SQL Server.
In this article, you will see:
Azure Data Sync —a synchronization service set up on an Azure SQL Database. This service synchronizes the data across multiple SQL databases. You can set up bi-directional data synchronization where data ingest and egest process happens between the SQL databases—It can be between Azure SQL database and on-premises and/or within the cloud Azure SQL database. At this moment, the only limitation is that it will not support Azure SQL Managed Instance.
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This article will walk you through creating a new SQL pool within an existing Azure SQL Server as well as catalog the same using the Azure Purview service.
Data is generated by transactional systems and typically stored in relational data repositories. This data is generally used by live applications and for operational reporting. As this data volume grows, this data is often required by other analytical repositories and data warehouses where it can be used for referential purposes and adding more context to other data from across the organization. Transactional systems (also known as Online Transaction Processing (OLTP) systems) usually need a relational database engine, while analytical systems (also known as Online Analytical Processing (OLAP) systems) usually need analytical data processing engines. On Azure cloud, it is usually known that for OLTP requirements, SQL Server or Azure SQL Database can be employed, and for analytical data processing needs, Azure Synapse and other similar services can be employed. SQL Pools in Azure Synapse host the data on an SQL Server environment that can process the data in a massively parallel processing model, and the address of this environment is generally the name of the Azure Synapse workspace environment. At times, when one has already an Azure SQL Server in production or in use, the need is to have these SQL Pools on an existing Azure SQL Server instance, so data in these SQL pools can be processed per the requirements on an OLAP system as well as the data can be co-located with data generated by OLTP systems. This can be done by creating SQL Pools within the Azure SQL Server instance itself. In this article, we will learn to create a new SQL Pool within an existing Azure SQL Server followed by cataloging the same using the Azure Purview service.
As we intend to create a new SQL Pool in an existing Azure SQL Server instance, we need to have an instance of Azure SQL in place. Navigate to Azure Portal, search for Azure SQL and create a new instance of it. We can create an instance with the most basic configuration for demonstration purposes. Once the instance is created, we can navigate to the dashboard page of the instance and it would look as shown below.
As we are going to catalog the data in the dedicated SQL Pool hosted on Azure SQL instance, we also need to create an instance of Azure Purview. We would be using the Azure Purview studio from the dashboard of this instance, tonregister this SQL Pool as the source and catalog the instance.
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When working in the SQL Server, we may have to check some other databases other than the current one which we are working. In that scenario we may not be sure that does we have access to those Databases?. In this article we discuss the list of databases that are available for the current logged user in SQL Server
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In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.
Fig. 1 System Databases
There are five system databases, these databases are created while installing SQL Server.
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