Dinesh Asanka

Dinesh Asanka
Exporting SSRS Reports to Multiple Sheets in Excel with dynamic sheets.

Exporting SSRS reports to multiple worksheets in Excel

September 11, 2020 by

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.

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Creating a Weekly schedule for the Standard Subscription in SSRS.

Enhancing Customer Experiences with Subscriptions in SSRS

August 28, 2020 by

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.

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Example of SSAS Dimension Hierachies from Sale sterritory Dimension.

Enhancing Data Analytics with SSAS Dimension Hierarchies

July 22, 2020 by

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.

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After publishing the model.

Prediction in Azure Machine Learning

July 16, 2020 by

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.

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Creating OLAP Perspectives

Improve readability with SSAS Perspectives

July 9, 2020 by

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.

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Final package after inclusion of Conditional Split.

Using the SSIS Script Component as a Data Source

June 25, 2020 by

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.

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Experiement configuration is Azure ML Studio.

Introduction to Azure Machine Learning using Azure ML Studio

June 18, 2020 by

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.

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SQL Service Broker objects in the SQL Server Management Studio after creating them.

Using the SQL Server Service Broker for Asynchronous Processing

June 10, 2020 by

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.

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Text Mining in SQL Server

May 18, 2020 by

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.

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Final SSIS package with Data Mining Query for Data Mining Query in SSIS.

Data Mining Query in SSIS

May 12, 2020 by

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.

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Connect to remote MySQL server without using SSL options in connection string

Measuring the Accuracy in Data Mining in SQL Server

April 29, 2020 by

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.

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Dependancy Network tab in the Mining view in the Association Rule

Association Rule Mining in SQL Server

January 28, 2020 by

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.

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Data types for selected attributes in Time Series Model

Microsoft Time Series in SQL Server

December 12, 2019 by

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.

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