In this article, you’ll learn the approach to integrate MongoDB data source using data virtualization technique in SQL Server 2019. In this article, you can see how SQL Server 2019 provides a platform to create a modern enterprise data hub using data virtualization technology and the PolyBase technique.
The advent of Data virtualization in SQL Server 2019 allows us to solve modern and complex data challenges. Data virtualization with PolyBase in SQL Server 2019 is used as a data hub, and you can directly query the data from several heterogeneous data sources. These data sources include Azure Managed Instance, Oracle, Teradata, SAP HANA, MongoDB, Hadoop clusters, Cosmos DB, and SQL Server. We can query the data source using T-SQL and without separately installing driver software.
The data virtualization in SQL Server 2019 is an improvised solution to the ETL process. The other advantage of Data virtualization is that it allows the integration of data from different sources such as Azure MI, SQL Server, MongoDB, Oracle, DB2, Cosmos DB, and Hadoop-Distributed-File-System (HDFS) without the much data movement around the source and destination. This process is possible with the advent of PolyBase connectors.
In this section, you will learn how to create secure data access from the underlying data source.
In this case, PolyBase uses the security model of the MongoDB model to access the data. In most cases, we need permission to read the data. However, the credentials used to read the data and it is stored inside the PolyBase data hub.
To set-up data virtualization, follow the below steps:
To configure database virtualization, select the database. Right-click the database and select Create External Table that starts the data virtualization wizard.
In this section, we will see how to create a database master key. The master key is created inside the SQL Server database and it acts as a data hub.
The master key is providing a secure way to read data using the credentials in the external data source. It is always recommended to choose a complex password for the master key. In addition, use the BACKUP MASTER KEY command to back up the master key.
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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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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.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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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,
In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.
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Since the release of SQL Server 2017 for Linux, Microsoft has pretty much changed the entire game. It enabled a whole new world of possibilities for their famous relational database, offering what was only available in the Windows space until then.
I know that a purist DBA would tell me right away that the out of the box SQL Server 2019 Linux version has several differences, in terms of features, in regards to its Windows counterpart, such as:
However, I got curious enough to think “what if they can be compared, at least to some extent, against things that both can do?” So, I pulled the trigger on a couple of VMs, prepared some simple tests, and collected data to present to you. Let’s see how things turn out!
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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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