This article will walk you through the way to export data out of Azure SQL Databases in a compressed format using Azure Data Factory.
Transactional databases host a large volume of data often in relational databases in a normalized format. When this data is required to be analyzed or augmented with other data in different formats or repositories, there may be a need to extract this data and channel it to the desired data repository or processing engine. As it’s not possible to have integration of any given relational database with every possible data processing or data analytics engine, data is often staged or exported to a file store. In the case of Azure SQL Database – which is a transactional and relational database, data may be exported to containers hosted on Azure Data Lake Storage Gen2. While it’s a straight-forward process to export data out of a database and store it in the form of files, the larger the size of data the bigger would be the cost of storing the data. Ideally, the data that is exported can be stored in a compressed data format like parquet or compressed file format like gzip or bzip. In this article, we will learn how to export data out of Azure SQL Databases in a compressed format using Azure Data Factory.
As we are going to be discussing exporting data out of SQL Database on Azure, the first thing we need is an instance of Azure SQL Database with some sample data populated in it. This article will assume that it’s already in place.
Next, we need an instance of Azure Data Factory created, using which we will build a data pipeline that would extract the data from Azure SQL Database and save it in a compressed file or data format. As this data would be stored on the Azure Data Lake Storage Gen2 account, we would need at least one such account with at least one container created on it, where we will store the compressed data. It’s also assumed this setup is in place too. Once the pre-requisite is met, we can proceed with the next steps.
In case, you are not sure about the above set up, check out the below articles that will get you started with these services.
#azure #database #sql
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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The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.
SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.
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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.
#azure #sql azure #azure sql #azure data sync #azure sql #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.
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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