SQL Server Management Studio 18.2 is now generally available 

How to Repair MDF Files in SQL Server Database 

Generate Database Scripts With Data In SQL Server 

SQL Server Performance Troubleshooting with SQL Monitor 4 

• Linux support for tier-1, mission-critical workloads – SQL Server 2017 support for Linux includes the same high availability solutions on Linux as Windows Server, including Always On availability groups integrated with Linux native clustering solutions like Pacemaker.
• Graph data processing in SQL Server – With the graph data features available in SQL Server 2017and Azure SQL Database, customers can create nodes and edges, and discover complex and many-to-many relationships.
• Adaptive query processing – Adaptive query processing is a family of features in SQL Server 2017 that automatically keeps database queries running as efficiently as possible without requiring additional tuning from database administrators. In addition to the capability to adjust batch mode memory grants, the feature set includes batch mode adaptive joins and interleaved execution capabilities.
• Python integration for advanced analytics – Microsoft Machine Learning Services now brings you the ability to run in-database analytics using Python or R in a parallelized and scalable way. The ability to run advanced analytics in your operational store without ETL means faster time to insights for customers while easy deployment and rich extensibility make it fast to get up and running on the right model. 

 C:\>sqlcmd -S .
1> create login [MachineName\TestUser] from windows;
2> go
1> exec sp_addsrvrolemember 'MachineName\TestUser','sysadmin'
2> go
1> select is_srvrolemember('sysadmin', 'MachineName\TestUser')
2> go
-----------
1
(1 rows affected)
1>

 C:\>sqlcmd -S .
1> use master
2> go
Changed database context to 'master'.
1> create login test_user with password='123#123'
2> go
1> exec sp_addsrvrolemember 'test_user','sysadmin'
2> go
1>

select '0.4400' as Expected , cast(0.4400 as decimal) as Actual
select '1.3200' as Expected, cast(1.3200 as decimal) as Actual
select '1.7600' as Expected, cast(1.7600 as decimal) as Actual
select '65.0000' as Expected, cast(65.0000 as decimal) as Actual
select '99.50' as Expected, cast(99.50 as decimal) as Actual

 select iif(cast(0.1 + 0.2 as decimal) = 0, 'true', 'false')

modelBuilder.Properties<decimal>().Configure(x => x.HasPrecision(18, 6));

No store type was specified for the decimal property 'Price' on entity type 'Product'.
This will cause values to be silently truncated if they do not fit in the default precision and scale.
Explicitly specify the SQL server column type that can accommodate all the values in 'OnModelCreating'
using 'HasColumnType', specify precision and scale using 'HasPrecision',
or configure a value converter using 'HasConversion'.

protected override void ConfigureConventions(ModelConfigurationBuilder configurationBuilder)
{
    configurationBuilder.Properties<decimal>().HavePrecision(18, 6);
}

string[] mystring = new string[]{"a","b","c","d"};

int i=0;

var s1 = from s in mystring.ToList()
let e = i++
select new {
Row_Number = i,StringName = s
};

s1.Dump();
mystring.Count().Dump("mystring Count");

Experiments with the new WASI workload in .NET 8 Preview 4
.NET 8 adds a new "wasi-experimental" workload, replacing the earlier Wasi.Sdk. This is a step towards giving .NET native, built-in support for server-side WebAssembly scenarios.
This video shows how to get started, then demonstrates how .NET+WASI creates possibilities for new kinds of apps and new ways to use .NET. 

 In this article, we will learn how to use JQuery Datatable in ASP.NET Core with Server Side Processing. We will also be building a simple real-world implementation to help understand JQuery Datatable to it’s fullest. You can find the source code of the entire implementation here. Let’s begin