Dinesh Asanka

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Dinesh Asanka is MVP for SQL Server Category for last 8 years. He has been working with SQL Server for more than 15 years, written articles and coauthored books. He is a presenter at various user groups and universities. He is always available to learn and share his knowledge.

View all posts by Dinesh Asanka

Elastic Jobs in Azure SQL Database

February 18, 2021 by Dinesh Asanka

Introduction

I have dedicated this article to the topic of Elastic Jobs in Azure SQL Database which is in Public Review. This feature allows you to run scheduled tasks in your Azure SQL Databases. This is similar to SQL Server Agent you have in the On-prem SQL Server versions. However, in Elastic Jobs, you can execute the scheduled tasks in multiple Azure SQL Servers and multiple Databases which is an added advantage when considering the features of SQL Server Agent. Further, this execution performs parallelly.

Dynamic Data Masking in SQL Server

February 2, 2021 by Dinesh Asanka

Introduction

In this article, we will be looking at how to perform and access Dynamic Data Masking in SQL Server. Data Masking is the process of hiding data with different rules. One of the main reasons to apply data masking is to protect Personal Identifiable Information (PII) and sensitive data from unauthorized access. Even when unauthorized users access these data, they will not be able to view the actual values. This article will look at the possibilities of applying Dynamic Data Masking in SQL Server and we will look at how Dynamic Data masking can be applied to the Azure SQL Databases as well.

Clustering in Azure Machine Learning

January 13, 2021 by Dinesh Asanka

Introduction

In this article, we will be discussing Clustering in Azure Machine Learning which is another machine learning technique such as Regression analysis, Classification analysis. During this article series, we have discussed the basic cleaning techniques, feature selection techniques and Principal component analysis, Comparing Models and Cross-Validation until today. We will introduce further few techniques that were not discussed before in this article as well. Previously, we have discussed how to perform clustering in SQL Server during the SQL Server Data Mining series.

Cross Validation in Azure Machine Learning

January 4, 2021 by Dinesh Asanka

Introduction

After discussing a few algorithms and techniques and comparison of models with Azure Machine learning, let us discuss a validation technique, which is Cross-Validation in Azure Machine Learning in this article. During this series of articles, we have learned the basic cleaning techniques, feature selection techniques and Principal component analysis etc. After discussing Regression analysis, Classification analysis and comparing models, let us focus now on performing Cross-Validation in Azure Machine Learning in order to evaluate models.

Creating your first Azure SQL Database

December 23, 2020 by Dinesh Asanka

Introduction

You might be thinking why do you want to create Azure SQL Database and what are the best configurations. This article will provide you with the basic configurations of the Azure SQL Database.

Troubleshooting using Wait Stats in SQL Server

November 26, 2020 by Dinesh Asanka

Introduction

Troubleshooting using Wait Stats in SQL Server is an important perspective when it comes to managing databases. As a database professional, you might have come across situations, where your end-users are not happy with reports being slower. It will leave you to find the reason for the report slowness.

Comparing models in Azure Machine Learning

November 23, 2020 by Dinesh Asanka

Introduction

After discussing a few algorithms and techniques with Azure Machine Learning let us discuss techniques of comparison in Azure Machine Learning in this article. During this series of articles, we have discussed the basic cleaning techniques, feature selection techniques and Principal component analysis, etc. After discussing Regression and Classification analysis let us focus more on performing comparison in Azure Machine Learning.

Prediction with Classification in Azure Machine Learning

October 30, 2020 by Dinesh Asanka

Introduction

After discussing Regression in the previous article, let us discuss the techniques for Classification in Azure Machine learning in this article. Like regression, classification is also the common prediction technique that is being used in many organizations. Before the regression we have discussed basic cleaning techniques, feature selection techniques and Principal component analysis in previous articles, now we will be looking at data classification techniques in azure machine learning in this article.

Prediction with Regression in Azure Machine Learning

October 6, 2020 by Dinesh Asanka

Introduction

We will be discussing one of the most common prediction technique that is Regression in Azure Machine learning in this article. After discussing the basic cleaning techniques, feature selection techniques and principal component analysis in previous articles, now we will be looking at a data regression technique in azure machine learning in this article.

Data Disaster Recovery with Log Shipping

September 22, 2020 by Dinesh Asanka

Introduction

In SQL Server, Log Shipping can be used to implement a Data disaster recovery mechanism. Typically, log shipping is used as a disaster recovery option which is implemented at many organizations due to its simplicity among many other factors.

Exporting SSRS reports to multiple worksheets in Excel

September 11, 2020 by Dinesh Asanka

Introduction

SQL Server Reporting Services (SSRS) has multiple options of exporting data into a variety of formats and we will be discussing the options of exporting SSRS Reports to multiple sheets of excel. In SSRS, there are multiple formats available to export reports depending on the user’s needs. Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Tiff file, MHTML (Web Archive), CSV (comma delimited) and XML file with report data are the popular formats that can be exported from SSRS as shown in the below screenshot.

Data Reduction Technique: Principal Component Analysis in Azure Machine Learning

August 31, 2020 by Dinesh Asanka

Introduction

We will be discussing one of the most common Data Reduction Technique named Principal Component Analysis in Azure Machine Learning in this article. After discussing the basic cleaning techniques, feature selection techniques in previous articles, now we will be looking at a data reduction technique in this article.

Enhancing Customer Experiences with Subscriptions in SSRS

August 28, 2020 by Dinesh Asanka

Introduction

We are going to discuss a very important option in SQL Server Reporting Services (SSRS), which is Subscriptions in SSRS. Typically, Reporting service is used to view reports. However, most users would prefer to receive the report to their inbox in the preferred report format, such as Word, Excel, or PDF in a preferred time. Further, you might want these reports to be delivered to a file share. Let us see how we can achieve these options using Subscriptions in SSRS and what are the challenges and pre-configurations.

Feature Selection in Azure Machine Learning

August 19, 2020 by Dinesh Asanka

Introduction

After discussing cleansing and prediction aspects in Azure Machine Learning, we will dedicate this article to another important feature, which is Feature Selection in Azure Machine Learning.

Implementing an SSAS Tabular Model for Data Analytics

August 4, 2020 by Dinesh Asanka

Introduction

Having discussed different aspects of MDM SSAS cubes, we will look at the Microsoft recommended OLAP tool, SSAS Tabular Models for data analytics. There are a few advantages of using Tabular Models over MDM that are listed below.

Enhancing Data Analytics with SSAS Dimension Hierarchies

July 22, 2020 by Dinesh Asanka

Introduction

This article will discuss how SSAS Dimension Hierarchies can be used to analyze data much efficiently. If you are a data analyst, you want to start the analysis with a higher hierarchy. Then navigate the narrow attributes when required. For example, it will be better to start with analyzing revenue by year. If you need to analyze further into the data, you can choose the needed year and expand the Quarter -> Month, respectively. Let us see how we can create SSAS Dimension Hierarchies in OLAP Cubes to suit different requirements.

Prediction in Azure Machine Learning

July 16, 2020 by Dinesh Asanka

Introduction

After discussing the basic features of azure machine learning and how to clean the data from Azure Machine learning, let us look at how to perform prediction in Azure Machine Learning. Prediction is one of the important aspects of machine learning as it will help to make strategic decisions.

Improve readability with SSAS Perspectives

July 9, 2020 by Dinesh Asanka

Introduction

In this article, we will be looking at a feature of SQL Server Analysis Service (SSAS) OLAP Cube that is SSAS Perspectives. We discussed creating SSAS OLAP Cubes in a previous article: OLAP Cubes in SQL Server. In an SSAS OLAP cube, there can be a large number of measures, dimensions and dimension attributes. The following screenshot is the star schema for the selected example that was created from the AdventureWorksDW sample database that can be visible at Data Source View.

Data Cleansing in Azure Machine Learning

July 2, 2020 by Dinesh Asanka

Introduction

After discussing the basic features of Azure Machine Learning in my previous article, Introduction to Azure Machine Learning using Azure ML Studio, we will look at techniques of data cleansing in Azure Machine Learning. Data Cleansing or Data Cleaning is an important aspect when it comes to predicting as quality data will improve the quality of data prediction.

Using the SSIS Script Component as a Data Source

June 25, 2020 by Dinesh Asanka

Introduction

SSIS Script component is one data transformation tasks in SQL Server Integration Services (SSIS). SSIS is an integration tool in the Microsoft BI family to extract data from heterogeneous data sources and transform it to your need. Apart from the standard data sources such as databases, text files, excel files, and web services, there can be instances where you need to retrieve non-traditional data sources. For example, let us say you want to extract the details of text files such as file sizes, created date, etc. In these types of scenarios, traditional data sources cannot be used.

Introduction to Azure Machine Learning using Azure ML Studio

June 18, 2020 by Dinesh Asanka

Introduction

Let us see how Azure ML studio can be used to create machine learning models and how to consume them in this series. As we discussed during the data mining series, we identified the challenges in the predictions in data. In the Azure Machine learning platform, machine learning workflows can be defined in easy scale models in the cloud environment. Today will be looking at how datasets can be uploaded.

Using the SQL Server Service Broker for Asynchronous Processing

June 10, 2020 by Dinesh Asanka

Introduction

In the real-world implementation of SQL Server of an enterprise system, it always needs to implement data in multiple databases. In most of the cases, a single transaction will span across multiple databases. If there are multiple databases, there will be performance issues and implementation issues during the cross-database transactions. However, with the use of SQL Service Broker that was introduced in SQL Server 2005, you can implement asynchronous transactions between databases.

SSAS Database Management

June 1, 2020 by Dinesh Asanka

There are different aspects of SSAS Database Management that we will be looked into detail in this article. We will be looking at backup, processing, and synchronization.

Text Mining in SQL Server

May 18, 2020 by Dinesh Asanka

In this article, we will be discussing how Text Mining can be done in SQL Server. For text mining in SQL Server, we will be using Integration Services (SSIS) and SQL Server Analysis Services (SSAS). This is the last article of the Data Mining series during which we discussed Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Additionally, we discussed the way to measure the accuracy of the data mining models. In the last article, we discussed how models can be extracted from the Data query.

Data Mining Query in SSIS

May 12, 2020 by Dinesh Asanka

In this article, we will be discussing how SQL Server Integration Services (SSIS) can be used to predict data mining models built from SSAS. In this article, we will be looking at the Data Mining Query in SSIS. During the data mining article series, we have discussed all the Data mining techniques that are available in SQL Server. The discussed techniques were Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Further, we discussed how the accuracy of the data mining models can be verified.