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Migrate your SQL Server database to Azure by using SysTools “SQL to Azure Migration” Tool

September 5, 2018 Leave a comment

 
Microsoft SQL Server is the most popular database management system, which provides flexibility to the database administrator to manage their database. It is a full-featured database and designed for use in corporate applications. But, many times, many users need to migrate their SQL Server database to the Azure SQL Database. Because Azure is an intelligent and fully managed relational cloud database that offers the broadest SQL Server engine compatibility.

However, at the time of SQL database to Azure migration, there are chances that we can miss some important steps or information such as trustworthy property, dependent Jobs, Linked Server, Logins etc. The main headache of any conversion is figuring out how to get the information from database and import into the server. Therefore, users need to use a professional tool to transfer SQL Server Database to Azure SQL Database.

One of the best third-party solutions I came up is SysTools SQL to Azure Migration Tool, which allows users to move all the SQL database objects like Tables, Triggers, Stored Procedures etc. to Azure SQL Database. Apart from this, there are many other features of this tool that are discussed below:
 

Key Features of SQL Server to Azure Migration Tool

 
1. Easily Transfer SQL Database to Azure

With the help of this tool, users can perform direct migration from SQL to Azure SQL database. For this, you need to provide all the credentials like server name, database name, username and password etc. After that, one can easily transfer SQL Server database to Azure.

2. Option to Add MDF/NDF Files

The tool provides an option to add MDF & associated NDF files to the software. You can also migrate all the database objects like Tables, Triggers, and Stored Procedures etc. from SQL to Azure.

3. Migrate Corrupted Database

The best part of this utility is that it can convert the corrupted database as well as the healthy file from local SQL server to Microsoft Azure SQL Database. Before migration, it scans the corrupted database files and repair them using either quick or advanced scan options.

4. Quick and Advance Scan Option

While working with this tool, the user can choose the required scan mode depending on the level of corruption out of the following modes:

– Quick Scan: This mode scans minor corrupted SQL files

– Advance Scan: This mode scan major corrupted SQL files

5. Auto Detect SQL Server File Versions

The SQL to Azure migration tool has an option to detect the versions of added MDF or NDF files automatically. For this, you just need to click on the checkbox next to this option.

6. Export Schema Option

The tool provides a feature to save the SQL database schema into Azure in any of the following ways:

– With the Only Schema: It migrates only schema of tables, views etc.

– With Schema & Date: It allows users to transfer both schema and data of all database objects.
 

Free and Licensed versions of SysTools SQL Server to Azure Migration Tool

The tool can be availed in the following two versions:

1. Demo Version: Users can freely download the trial version of software from the SysTools official website. It is available to understand the working of the software in a much better way.

2. Licensed Version: The licensed version of the tool can migrate SQL server database to Azure from MDF or NDF files. Also, it allows you to transfer the schema and schema & data.
 

System Requirements

The licensed version of SQL to Azure migration tool has been tested by the SQL experts. It will evaluate the performance of the software in terms of quality, reliability, security etc.

However, the testing has been performed in below-mentioned environment:

– Operating System: compatible with Windows 10 and all below versions

– Processor: Intel Pentium 1 GHz processor or any equivalent processor

– RAM: Around 2 GB of RAM is necessary

– Hard Disk: At least 100 MB space is required for installation
 

Advantages:

– It can also migrate corrupted or inaccessible SQL database to the Azure database.
– The software can preview all database components before migrating to Azure.
– The SQL Server to Azure migration tool has a simple and user-friendly interface.
 

Disadvantages:

– It is required to have database created on Azure SQL Server Database before migration.
– The Demo version only exports 25 records of each table.
 

Observational Verdict

After considering all the features of SQL Server to Azure Migration tool, I can say that it is a reliable and effective tool for DBAs and Developers to transfer SQL data to Azure without causing any data loss.


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SQL Server on Linux – Best practices post installation

August 30, 2018 1 comment

 

Setup SQL Server on Linux:

1. Spinning up a new Linux VM on Microsoft Azure

2. Install and Configure SQL Server 2017 on Linux Azure VM

3. Connect SQL Server on Linux with SSMS from a Windows machine
 

Best Practices:

Here are some of the best practices post installing SQL Server on Linux that can help you maximize database performance:

1. To maintain efficient Linux and SQL Scheduling behavior, it’s recommended to use the ALTER SERVER CONFIGURATION command to set PROCESS AFFINITY for all the NUMANODEs and/or CPUs. [Setting Process Affinity]

2. To reduce the risk of tempdb concurrency slowdowns in high performance environments, configure multiple tempdb files by adding additional tempdb files by using the ADD FILE command. [tempdb Contention]

3. Use mssql-conf to configure the memory limit and ensure there’s enough free physical memory for the Linux operating system.

sudo /opt/mssql/bin/mssql-conf set memory.memorylimitmb 1024
sudo systemctl restart mssql-server

4. On multi-node Non-Uniform Memory Access (NUMA) installations, auto NUMA balancing needs to be disabled to allow SQL Server to operate at maximum efficiency on a NUMA system.

sysctl -w kernel.numa_balancing=0

5. You can also change the kernel settings value for virtual address space to 256K, as the default value of 65K may be insufficient for a SQL Server installation.

sysctl -w vm.max_map_count=262144

6. Use the noatime attribute to disable last accessed timestamps with any file system that is used to store SQL Server data and log files.

7. For the most consistent performance experience, you must leave the Transparent Huge Pages (THP) option enabled.

8. Virtual machine (VM) features like Hyper-V Dynamic Memory shouldn’t be used with SQL Server installations. When using VMs, be sure to assign sufficient fixed-memory sizes.

9. Make sure you have a properly configured swapfile to avoid any out of memory issues.


SQL Python Error – ‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable it.

August 29, 2018 Leave a comment

 
You are running a Python script by using sp_execute_external_script SP but its throwing this error:
 

Msg 39023, Level 16, State 1, Procedure sp_execute_external_script, Line 1 [Batch Start Line 27]
‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable it.

 

You can refer to my blog post on setting up ML with Python with SQL Server, link: https://sqlwithmanoj.com/2018/08/10/get-started-with-python-on-sql-server-run-python-with-t-sql-on-ssms/

This fix will also work with R support with SQL Server.

 


Get started with Python on SQL Server – Run Python with T-SQL on SSMS

August 10, 2018 1 comment

 
With SQL Server 2016 Microsoft added Machine Learning support with R Language in SQL engine itself and called it SQL Server R Services.

Going ahead with the new SQL Server 2017 version Microsoft added Python too as part of Machine Learning with existing R Language, and thus renamed it to SQL Server Machine Learning Services.
 

Installation/Setup

Here are few steps to get you started with Python programming in SQL Server, so that you can run Python scripts with T-SQL scripts within SSMS:
 

1. Feature Selection: While installing SQL Server 2017 make sure you’ve selected below highlighted services


 

2. Configure “external scripts enabled”: Post installation run below SQL statements to enable this option

sp_configure 'external scripts enabled'
GO
sp_configure 'external scripts enabled', 1;
GO
RECONFIGURE; 
GO
sp_configure 'external scripts enabled'
GO


 

3. Restart SQL Server service: by “services.msc” program from command prompt, and run below SQL statement, this should show run _value = 1

sp_configure 'external scripts enabled'
GO


 

If still you don’t see run _value = 1, then try restarting the Launchpad service mentioned below in Step #4.

4. Launchpad Service: Make sure this service is running, in “services.msc” program from command prompt. Restart the service, and it should be in Running state.

Its a service to launch Advanced Analytics Extensions Launchpad process that enables integration with Microsoft R Open using standard T-SQL statements. Disabling this service will make Advanced Analytics features of SQL Server unavailable.

Post restarting this service, it should return run _value = 1 on running the query mentioned at Step #3
 

Run Python from SSMS

So as you’ve installed SQL Server with ML services with Python & R, and enabled the components, now you can try running simple “Hello World!” program to test it:

EXEC sp_execute_external_script 
	@language = N'Python', 
	@script = N'print(''Hello Python !!! from T-SQL'')'


 

Let’s do simple math here:

EXEC sp_execute_external_script 
@language = N'Python', 
@script = N'
x = 5
y = 6
a = x * y
b = x + y
print(a)
print(b)'

 

If you still face issues you can go through addition configuration steps mentioned in [MSDN Docs link].

 


Azure Databricks learning resources (documentation and videos)

August 7, 2018 1 comment

 

Databricks Introduction

What is Azure Databricks [Video]

Create Databricks workspace with Apache Spark cluster

Extract, Transform & Load (ETL) with Databricks

– Documentation:
   – Azure
   – Databricks
 

From Channel 9

1. Data Science using Azure Databricks and Apache Spark [Video]

2. Data ingestion, stream processing and sentiment analysis using Twitter [Video]

3. ETL with Azure Databricks using ADF [Video]

4. ADF new features & integration with Azure Databricks [Video]

5. Azure Databricks introduces R Studio Integration [Video]

6. Run Jars and Python scripts on Azure Databricks using ADF [Video]
 

From Microsoft Build Conf