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