Data Binding Blazor FlexGrid to SQL Server with Real-Time Updates

The FlexGrid control and the powerful C1DataCollection library make it easy to data bind and show real-time updates from SQL Server in your Blazor web apps. FlexGrid is a fast Blazor datagrid control that supports displaying and editing data, and C1DataCollection helps manage the collection between the UI and the database.

In this post, we will show you how to display a SQL Server table in a Blazor application using FlexGrid. And with the help of C1DataCollection, the records will be updated automatically as they are modified in the database.

In order to implement this sample, I will show the following steps:

1. Setup the SQLServer data base
2. Create a Blazor application
3. Create a SQL Server DataCollection
4. Bind FlexGrid to the SQL Server DataCollection

Setup the SQL Server Database

For this sample, I will use a database named “TableDependencyDB” hosted in a SqlExpress instance, with a table named “Products”. The table was created using the following SQL statement:

CREATE TABLE [dbo].[Products]( 
[Code] [nvarchar](50) NULL, 
[Name] [nvarchar](50) NULL, 
[Price] [decimal](18, 0) NULL 
) ON [PRIMARY] 

I’ve added some records for demonstration purposes:

Create a Blazor Application

The next step is to create a server-side Blazor application and add the NuGet packages C1.Blazor.Grid, C1.DataCollection.AdoNet and SqlTableDependency, which we will use later.

On the Index.razor page define a FlexGrid with two columns as below:

<FlexGrid ItemsSource="Items" AutoGenerateColumns="false" Style="@("width:100%;")"> 
    <FlexGridColumns> 
        <GridColumn Binding="Name" Width="GridLength.Star"></GridColumn> 
        <GridColumn Binding="Price"></GridColumn> 
    </FlexGridColumns> 
</FlexGrid> 

The grid is bound to a collection of Products, which we’ll populate later from SQL Server. For now I’ve also defined the Product class as below:

public class Product
{
    public string Code { get; set; }
    public string Name { get; set; }
    public decimal Price { get; set; }
}

For more details about how to create the server Blazor application and bind FlexGrid you can read my previous article, upgrading a Blazor HTML table with FlexGrid.

#.net #desktop #blazor

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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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4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

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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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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,

• MS SQL Server – Zero to Hero Query Master – Part 1
• MS SQL Server – Zero to Hero Query Master – Part 2

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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Your Data Architecture: Simple Best Practices for Your Data Strategy

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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