Sql Web Server Business Intelligence Advancement Workshop 2017

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Sql Web Server Business Intelligence Advancement Workshop 2017 – With the announcement of Power BI a week ago and with the great features in this product, there are many opportunities to use this product and service. Power BI can be used together with several other Azure services, and even On-Premises services to build end-to-end enterprise BI and Data Analytics Solutions. In this post, I will only explain one example of a Hybrid and End-to-End BI solution scenario that uses the following technologies;

The above technologies together provide a reliable and scalable data analysis solution. Obviously the above scenario is only one of 100 scenarios where these products can be used together to build an end-to-end solution. I consider the data source to be on-premises because most of the data still resides in data stores at corporate and organizational locations. store data in place can be of any type such as SQL Server database, Oracle, MySQL DB, or even CSV and Excel files…. As long as there is some data stored somewhere, we can use it as a resource.

Sql Web Server Business Intelligence Advancement Workshop 2017

Azure Data Factory is a cloud-based computing service for data extraction and loading, supporting data sources such as SQL Server on-premises, Oracle on-premises, CSV files and several other sources. Azure Data Factory connects to on-premises data sources with a gateway named Data Management Gateway. Data Factory can load data into on-premises as well as cloud-based data stores such as Azure SQL Database and Azure SQL Data Warehouse. If you want to learn more about Azure Data Factory, read the article here: Building your first Azure Data Factory.

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SQL Server Integration Services (SSIS) is a familiar name in the database world. SSIS is a leading data transfer and consolidation tool that supports stacks of data sources, has many transformations that can be applied to datasets, and results can be loaded into any destination. SSIS Packages can expose data to Azure data stores especially with the new Azure Packages released for SSIS. To learn more about SSIS, please watch the SSIS tutorial video series here: SSIS Tutorial Videos.

There are two main azure services that are recommended for use as Data Warehouses in the cloud. Azure SQL Database is the database engine of SQL Server, and Azure SQL Data Warehouse was released recently and is highly optimized and configurable for big data and analytical use. Fortunately there is a lot of information about Azure SQL Database that you can read and watch. you can use the Azure SQL Database documentation as a starting point. Azure SQL Data Warehouse is very new, and there are not so many blogs, or articles about it, but the Azure SQL Data Warehouse documentation page has some informative videos about it.

Power BI 2.0 was released for general availability almost a week ago, and it has many new features. Power BI is made up of three main components: Power Query for data extraction and transformation, Power Pivot for data modeling, and Power View for data visualization. Power BI dashboards and visualizations can be deployed to a cloud website (PowerBI.com), then accessible from anywhere, and on every platform. There is a mobile app for Power BI for Android, and Apple as well as Windows phones. Power BI reports are interactive, and user-friendly. Power BI has a lot to say in this section, so I’ll just refer you to a few Power BI blog posts for more information.

Power BI can connect to many data sources, and Azure SQL Database and Azure SQL Data Warehouse are on the list. Connections can be easily created, and this connection also supports scheduled refresh. So the data can be updated based on the required schedule. This lists all data sources supported for Scheduled Data Refresh.

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Explaining all components of the above solution is not possible in one post. I will write several more blog posts explaining each section in detail. But for this post I would like to mention only part of the work that can be helpful as a starting point for you. In this post I would like to mention how easy the connection from Power BI to Azure SQL Database is.

Then credentials. You have the option to use existing credentials, or enter user credentials (you may need Azure Active Directory for this), or you can enter SQL Database credentials.

Now you can start building models and visualizations, and deploy reports to Power BI websites. After deployment you can set up a Scheduled Data Refresh.

Reza Rad is a Microsoft Regional Director, Author, Trainer, Speaker and Consultant. He holds a BSc in Computer engineering; he has more than 20 years of experience in data analysis, BI, databases, programming, and development mostly in Microsoft technologies. He was Microsoft Data Platform MVP for 12 consecutive years (from 2011 to present) due to his dedication in Microsoft BI. Reza is an active blogger and founder. Reza is also the co-founder and co-organizer of Difinity conference in New Zealand, Power BI Summit, and Data Insight Summit.

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Reza is the author of more than 14 books on Microsoft Business Intelligence, most of them published in the Power BI category. Among books such as Power BI DAX Simplified, Pro Power BI Architecture, Power BI from Rookie to Rock Star, Power Query book series, Row-Level Security in Power BI and others.

He is an International Speaker at Microsoft Ignite, Microsoft Business Applications Summit, Data Insight Summit, PASS Summit, SQL Saturday and SQL user groups. And He is a Microsoft Certified Trainer.

Articles on various aspects of the technology, especially on MS BI, can be found on his blog: https:///blog.Python integration for data science, graph processing for NoSQL-like functionality, and running on Linux as well as Windows. At nearly 30 years old, Microsoft’s flagship database has learned many new tricks.

Today, at the Ignite conference in Orlando, Microsoft announced the general availability of a new version of its flagship operational database, SQL Server 2017.

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SQL Server is a product that I have worked with for most of my career. I started with version 4.2 for Windows in 1993 and have worked with and carefully reviewed every subsequent release, including this one. The thing I learned from all this work and evaluation is that while SQL Server has improved, while it has modernized and added many features and bundled technologies, in many ways, it is still the same database that I started working with almost. 25 years ago.

Sometimes, I think developers and database administrators need that familiarity. Other times, they need innovation and confidence that the product team has more new things in the pipeline. Customers need SQL Server to stay the same, but to change and improve. This is indeed the unique challenge of SQL Server: it must be true to its heritage, but avoid stasis, remaining competitive with the seemingly endless variety of new data technologies, both commercial and open source.

This new version of SQL Server continues to meet these twin demands. It adds new features from the world of data science and NoSQL. It offers cross-platform capabilities and Docker container compatibility. But it also reinforces investments in core database engine performance, ease of index maintenance, high availability and data warehouse performance. That’s a difficult balance and one that other database vendors don’t have to meet. While this may be Microsoft’s crucifixion, the company has done it well, turning a daunting challenge into a positive market differentiator.

Neo-retro: SQL Server for Linux In a move that may seem ironic in the market and not quite consistent with the legacy of SQL Server, the release of SQL Server 2017 heralds the return of SQL Server to the *NIX platform, with a new Linux version of the product. And even if this development is consistent with the legacy (early versions of SQL Server, developed together with Sybase, also run on UNIX), it is still a big problem.

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SQL Server has grown in popularity over the years. But the popularity of Linux on the server side is growing, and the Mac has become popular with developers. This means that some customers have run multiple Windows servers in their data centers just for SQL Server. This also means that Mac and Linux development machines must connect to SQL Server over the network, and cannot run locally except in a Windows virtual machine. Both of these issues are roadblocks to adoption.

With Linux compatibility, Microsoft began the work of removing these adoption blocks. SQL Server can now run on Windows servers, Linux-based servers, or some combination of the two. The Linux version can also run in Docker containers (which can also be Windows- or Linux-based) so it’s almost trivial to run on a Mac. In fact, if the developer’s machine has Docker installed, running and has allocated at least 4GB of memory for it, then two commands, typed in the Mac or Linux Terminal window of the machine, will download, install and start the product running. This same technique works on Windows machines, although Windows versions can be installed on metal.


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