Creating and Cataloging SQL pools in Azure SQL Server

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.

Introduction

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.

Pre-requisite

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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Creating and Cataloging SQL pools in Azure SQL Server
Cayla  Erdman

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Introduction to Structured Query Language SQL pdf

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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Creating and Cataloging SQL pools in Azure SQL Server

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.

Introduction

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.

Pre-requisite

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.

#azure #sql azure #azure sql server #sql #sql #azure

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SCHEMAS in SQL Server -MS SQL Server – Zero to Hero Query Master

Introduction

This is part 3 of “MS SQL Server- Zero to Hero” and in this article, we will be discussing about the SCHEMAS in SQL SERVER. Before getting into this article, please consider to visit previous articles in this series from below,

A glimpse of previous articles
Part 1

In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.

Part 2
  • We learned to create a database and maintain it using SQL statements.
  • Best practice methods were also mentioned.

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How to set up Azure Data Sync between Azure SQL databases and on-premises SQL Server

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:

  • Overview of Azure SQL Data Sync feature
  • Discuss key components
  • Comparison between Azure SQL Data sync with the other Azure Data option
  • Setup Azure SQL Data Sync
  • More…

Azure Data Sync

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.

#azure #sql azure #azure sql #azure data sync #azure sql #sql server

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Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

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