SQL Assessment API provides a mechanism to evaluate the configuration of your SQL Server for best practices. The API is delivered with a ruleset containing best practice rules suggested by SQL Server Team. This ruleset is enhancing with the release of new versions but at the same time, the API is built with the intent to give a highly customizable and extensible solution. So, users can tune the default rules and create their own ones. The API can be used to assess SQL Server versions 2012 and higher and Azure SQL Managed Instance.
In this episode with Aaron Nelson, we’ll show you the basics of the SQL Assessment PowerShell commands, and how you can run the assessments for your Azure SQL Managed Instances as well as your on-prem SQL Server instances.
SQL Server Samples: https://github.com/microsoft/sql-server-samples/tree/master/samples/manage/sql-assessment-api?WT.mc_id=dataexposed-c9-niner
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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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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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In this article, we will see how we can access data from an Azure SQL database from Azure Data Lake Analytics.
In the previous part of the Azure Data Lake Analytics article series, we learned how to process multiple file sets stored in the Azure Data Lake Storage account using U-SQL. Often data is stored in structured as well as unstructured formats, and one needs to access data from structured stores as well apart from data stored in repositories like Azure Data Storage Account. We will go over the process to access data from an Azure SQL database using Azure Data Lake Analytics.
We need to have a few pre-requisites in place before we can start our exercise. In the previous part of this article series, we set up an Azure Data Lake Analytics account and created a database on it. We would also need an Azure database with some sample data in it. While creating an Azure database it offers the option to create it with sample data. In this exercise, we are going to use such a database with the sample data as shown below. In the below screen, it shows the azure-sql-server-001 is the name of the database, azure-sql-server-001 is the name of the Azure SQL Server instance, and SalesLT.Address is the name of the table that we intend to access from Azure Data Lake Analytics. Once this setup is in place, we can proceed with the next steps.
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