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.

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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.

Measuring the Accuracy in Data Mining in SQL Server

April 29, 2020 by Dinesh Asanka

In this article, we will be discussing measuring Accuracy in Data Mining in SQL Server. We have discussed all the Data mining techniques that are available in SQL Server in a series of articles. The discussed techniques were Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network, Sequence Clustering. Data mining is a predicting technique using the existing pattern. It is obvious that we won’t be able to predict 100% accurately. However, since we are using data mining outcomes for better business decisions, the result should have better accuracy. If the accuracy is very low, we tend not to use those data mining models. Therefore, it is essential to find out how accurate your data mining models are.

Implementing Sequence Clustering in SQL Server

April 24, 2020 by Dinesh Asanka

In this article, we will be discussing Microsoft Sequence Clustering in SQL Server. This is the ninth article of our SQL Server Data mining techniques series. Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, Linear Regression, Neural Network are the other techniques that we discussed until this article.

Implement Artificial Neural Networks (ANNs) in SQL Server

April 14, 2020 by Dinesh Asanka

In this article, we will be discussing Microsoft Neural Network in SQL Server. This is the seventh article of our SQL Server Data mining techniques series. Naïve Bayes, Decision Trees, Time Series, Association Rules, Clustering, and Linear Regression are the other techniques that we discussed until this article.

Fuzzy Lookup Transformations in SSIS

April 10, 2020 by Dinesh Asanka

This article helps you to understand the usage of the Fuzzy Lookup Transformation in SQL Server Integration Services (SSIS).

Microsoft Linear Regression in SQL Server

March 24, 2020 by Dinesh Asanka

In this article, we will be discussing Microsoft Linear Regression in SQL Server. This is the next data mining topic in our SQL Server Data mining techniques series. Naïve Bayes, Decision Trees, Time Series, Association Rules, and Clustering are the other techniques that we discussed until today.

Microsoft Clustering in SQL Server

February 20, 2020 by Dinesh Asanka

Microsoft Clustering is the next data mining topic we will be discussing in our SQL Server Data mining techniques series. Until now, we have discussed a few data mining techniques like: Naïve Bayes, Decision Trees, Time Series, and Association Rules.

OLAP Cubes in SQL Server

February 6, 2020 by Dinesh Asanka

An Online Analytical Process (OLAP) cubes are used as an analytical tool. An OLAP Cube is a multi-dimensional database that is optimized for reporting purposes.

Association Rule Mining in SQL Server

January 28, 2020 by Dinesh Asanka

Association Rule Mining in SQL Server is the next article in our data mining article series in which we have discussed Naïve Bayes, Decision Trees, and Time Series until now. Association Rule Mining, also known as Market Basket Analysis, mainly because Association Mining is used to find out the items which are bought together by the customers during their shopping.

Microsoft Time Series in SQL Server

December 12, 2019 by Dinesh Asanka

The next topic in our Data Mining series is the popular algorithm, Time Series. Since business users want to forecast values for areas like production, sales, profit, etc., with a time parameter, Time Series has become an important data mining tool. It essentially allows analyzing the past behavior of a variable over time in order to predict its future behavior.

Truncate Table Operations in SQL Server

September 25, 2019 by Dinesh Asanka

Truncating a table is removing all the records in an entire table or a table partition. TRUNCATE table is functionally similar to DELETE table with no WHERE clause. However, TRUNCATE table is much faster than DELETE with respect to the time and the resource consumptions which we will look at in this article. TRUNCATE statement removes the data by de-allocating the data pages in the table data. This means that TRUNCATE is similar to drop and re-create the table. Also, it records only the page de-allocations in the transaction log, not the row-wise as in DELETE statement.

Alternate Row Colors in SSRS

September 20, 2019 by Dinesh Asanka

Setting alternate row colors in SSRS (SQL Server Reporting Services) is an important visualization configuration for end-users so that they can easily view their reports. However, setting alternate colors in SSRS is not a click of a button configuration like in the Microsoft Excel.

Microsoft Decision Trees in SQL Server

September 12, 2019 by Dinesh Asanka

Decision trees, one of the very popular data mining algorithm which is the next topic in our Data Mining series. In the previous article Introduction to SQL Server Data Mining, we discussed what data mining is and how to set up the data mining environment in SQL Server. Then in the next article, Microsoft Naïve Bayes algorithm was discussed. In this Article, Microsoft Decision Trees are discussed with examples. The Microsoft Decision Trees algorithm is a classification and regression algorithm that works well for predictive modeling. The algorithm supports the prediction of both discrete and continuous attributes.

SSIS Conditional Split overview

September 4, 2019 by Dinesh Asanka

SQL Server Integration Services or SSIS is used as an ETL tool to extract-transform-load data from heterogeneous data sources to different databases. After extracting data from the different sources, most often there are a lot of transformations needed. One of the frequent transformations is SSIS Conditional Split.

SQL Server ALTER TABLE ADD Column overview

August 27, 2019 by Dinesh Asanka

In this article, we will explore SQL Server ALTER TABLE ADD Column statements to add column(s) to an existing table. We will also understand the impact of adding a column with a default value and adding and updating the column with a value later on larger tables.

Naive Bayes Prediction in SQL Server

August 2, 2019 by Dinesh Asanka

In this article, we will walk through Microsoft Naive Bayes algorithm in SQL Server.

Introduction to SQL Server Data Mining

July 23, 2019 by Dinesh Asanka

Prediction, is it a new thing for you? You won’t believe you are predicting from the bed to the office and to back to the bed. Just imagine, you have a meeting at 9 AM at the office. If you are using public transport, you need to predict at what time you have to leave so that you can reach the office for the meeting on time. Time may vary by considering the time and the day of the week, and the traffic condition etc. Before you leave your home, you might predict whether it will rain today and you might want to take an umbrella or necessary clothes with you. If you are using your vehicle then the prediction time would be different. If so, you don’t need to worry about the rain but you need to consider the fuel level you need to have to reach to the office. By looking at this simple example, you will understand how critical it is to predict and you understand that all these predictions are done with your experience but not by any scientific method.

C2 auditing – SQL Server Auditing and Common Criteria Compliance using C2 audit mode

April 1, 2019 by Dinesh Asanka

This article will cover SQL Server C2 auditing using C2 audit mode including an introduction, comparison of auditing technologies, configuration and common criteria compliance

SQL Server auditing with Server and Database audit specifications

November 20, 2017 by Dinesh Asanka

Auditing is a key feature in any application or any system as it provides end users with better analysis for administrators. Apart from analysis, auditing can be used as a troubleshooting mechanism too. Apart from organizational reasons, there are compliance reasons for enabling auditing depending on the domain of operation.

Auditing is mainly about answering four questions, i.e. who, when, what and where. However, depending on the situation, it might be decided what questions of the mentioned four should be answered.