SQL Server Analysis Services (SSAS) is a component of the Microsoft SQL Server database software that provides online analytical processing (OLAP) and data mining functionality for business intelligence (BI) applications. It allows you to create and manage multidimensional data structures, called cubes, that can be used to quickly analyze and understand large amounts of data. A cube is a multidimensional data structure that is optimized for fast, complex queries and calculations. It stores data in a hierarchical format, where each level of the hierarchy represents a different aspect of the data, such as time, location, or product. SSAS also supports data mining, which allows you to discover patterns and relationships in your data. It includes several data mining algorithms and it's possible to create custom data mining models to suit your specific needs. It also allows you to create and manage dimensions and hierarchies, which are used to organize the data in a cube and make it easier to navigate and analyze. It also includes a visual design tool, called SQL Server Data Tools (SSDT), which makes it easy to create and manage cubes and dimensions. It supports both on-premise and cloud-based deployment and it can be integrated with other Microsoft products such as Excel and SharePoint, and Power BI. SQL Server Analysis Services (SSAS) is a Microsoft technology for creating and managing multidimensional databases, also known as OLAP (Online Analytical Processing) databases. There are two main editions of SSAS: Standard and Enterprise. The main features of SSAS Standard edition include: Support for creating and managing multidimensional databases Support for creating and deploying cubes, dimensions, and hierarchies Support for creating calculated members and named sets Support for basic data mining The main features of SSAS Enterprise edition include: All the features of SSAS Standard edition Support for advanced data mining, including time series and sequence clustering Support for creating and managing multiple partitions per cube Support for creating and managing multiple aggregations per cube Support for creating and managing translations of metadata Support for data access through OLE DB for OLAP and XML for Analysis (XMLA) Support for creating and managing perspectives Please note that the above list is not exhaustive, and there are other features available in both Standard and Enterprise editions, but the above mentioned are the key features differentiating both edition. SQL Server Analysis Services (SSAS) supports both tabular and multidimensional cubes. Tabular cubes are a newer type of cube model, introduced in SQL Server 2012, that uses a relational model to represent data, similar to a table in a relational database. Tabular cubes can be created using the Power Pivot for Excel add-in, and are designed for use with Power BI and Excel. Multidimensional cubes, also known as OLAP (Online Analytical Processing) cubes, use a multidimensional model to represent data, consisting of dimensions and hierarchies. These cubes have been around since the early days of SSAS and are designed for use with tools like Excel PivotTables and PivotCharts. Both types of cubes can be managed and deployed through SSAS, and can be accessed through the same data access methods, such as OLE DB for OLAP and XML for Analysis (XMLA).

As IT Managers, it’s important to understand the different ways dates can be used in Power BI. Dates are handled as a data type; this knowledge enables you to combine comparisons of many varied types of data and produce succinct visuals that show trends, patterns and anomalies over time. By mastering how to effectively use date ranges in Power BI, you can create meaningful insights that would not otherwise surface. In this blog post we will explore how effective date handling can take your analytics capabilities to new heights and reveal powerful information from your datasets. When you import data into Power BI, it will automatically detect and assign the data type for each column, including date columns. Once a date column is recognized, you can use it in various ways in your visualization, such as using it as a dimension on a chart's x-axis, grouping by year, quarter, month, or week, or filtering by a date range. Power BI also has a built-in date hierarchy, which allows you to drill down and drill up through different levels of date granularity, such as year, quarter, month, and day. Additionally, you can format the date to your desired format in the Modeling tab. It also has a time intelligence feature that allows you to perform time-based calculations, such as year-over-year, quarter-over-quarter, and more. Power BI is a powerful tool for analyzing data. It provides a lot of flexibility in terms of date handling and granularity, as well as time-based calculations. If you're looking for an easy way to visualize and analyze your data,Power BI is definitely worth checking out. What Are M and DAX In Power BI and How Do They Relate To Dates M and DAX are both languages used in Power BI to manipulate and analyze data. M (also known as Power Query) is used to extract, transform, and load data into Power BI. It allows you to connect to a variety of data sources, clean and reshape the data, and create custom calculations. M is a functional language, which means that it works by applying a series of transformations to the data, rather than using loops and conditional statements. M (Power Query): M is primarily used for data transformation and cleansing tasks. M is a powerful and flexible language that allows you to shape and manipulate data in a variety of ways, including filtering, sorting, and grouping data, as well as creating new columns and calculating custom expressions. M is also useful for creating complex queries that include joining, merging, and appending data from multiple sources. Once you've transformed your data, it can be loaded into the Power BI data model for further analysis and visualization. DAX (Data Analysis Expressions) DAX (Data Analysis Expressions) is used to create calculations and aggregations within Power BI, such as creating calculated columns, tables, and measures. It is a formula language, similar to Excel formulas, that allows you to perform calculations on the data that is already loaded into Power BI. DAX is used to define calculated columns, calculated tables and Measures within Power BI. DAX (Data Analysis Expressions): DAX is primarily used for creating calculations and aggregations in the Power BI data model. DAX is a formula language that uses a syntax similar to Excel formulas and can be used to create calculated columns, calculated tables, and measures. DAX is also useful for creating complex calculations that include time intelligence, ranking, and filtering data. DAX is used to define calculations and aggregations that can be used in visualizations and reports. In conclusion, it is important to understand the difference between M and DAX in Power BI. M is used to shape and prepare data before it is loaded into Power BI, while DAX is used to perform calculations and create new values within Power BI once the data has been loaded. By understanding the role of each language, you can more effectively use Power BI to analyze your data. How Do I Format A Date In DAX Power BI? In DAX (Data Analysis Expressions) in Power BI, you can use the FORMAT function to format a date. The basic syntax of the function is: FORMAT(date_expression, format_string) date_expression is the column or expression that contains the date that you want to format. format_string is the format that you want to use to display the date. For example, you can use the format string "MM/dd/yyyy" to display the date as "01/25/2023". You can also use the following date and time formats in the format_string: "yyyy" for the year with century as a decimal number "yy" for the year without century as a decimal number "MMMM" for the full month name "MMM" for the abbreviated month name "MM" for the month as a decimal number "M" for the month as a decimal number "dddd" for the full weekday name "ddd" for the abbreviated weekday name "dd" for the day of the month as a decimal number "d" for the day of the month as a decimal number "HH" for the hour (24-hour clock) as a decimal number "H" for the hour (24-hour clock) as a decimal number "hh" for the hour (12-hour clock) as a decimal number "h" for the hour (12-hour clock) as a decimal number "mm" for the minute as a decimal number "m" for the minute as a decimal number "ss" for the second as a decimal number "s" for the second as a decimal number "AM/PM" to use the 12-hour clock and display "AM" or "PM" M Code For Date Transformations In Power BI, you can use the "M" language to write custom expressions to transform date formats. Here are the steps to use M code for date transformations in Power BI: Open the Power Query Editor by clicking on the "Edit Queries" button in the Home tab. Select the column that contains the date that you want to transform. Click on the "Transform" tab, and then click on the "Format" button in the "Any Column" group. In the "Format" dialog box, select "Custom" as the format option, and enter the M code expression that you want to use to transform the date format. -For example, you can use the following M code expression to convert a date in the format "dd-MM-yyyy" to the format "MM-dd-yyyy": = Table.TransformColumnTypes(Source,{{"Column1", type date}}), Click "OK" to apply the transformation. You can use the M code to transform the date format and also use other functions such as DateTime.ToText or DateTime.LocalNow and many others to manipulate dates. Power BI Date Format Short Month Name -In Power BI, you can format a date to display the short month name by using the "MMM" code in the custom format field. Here are the steps to format a date to display the short month name: Select the column that contains the date, then right-click and choose "Format Column." In the "Format" section, select "Custom" as the format option. In the custom format field, enter "MMM" to display the short month name (e.g. "Jan" for January) Click "OK" to apply the format. Alternatively, you can use the "MMM" code in the DAX formula to have the short month name in the calculated column or measure. For example, you can use the following DAX formula to create a calculated column with the short month name : =FORMAT(your_date_column,"MMM") You can also use the same code in your visualization to have the short month name in the axis, tooltip or other visualization elements Note that the format codes such as "MMM" are case sensitive, so make sure you use the correct capitalization when entering them in the custom format field. case-sensitive How Do You Change Date Format To MM DD YYYY In Power BI? In Power BI, you can change the date format of a column to "MM DD YYYY" by using the FORMAT function in DAX (Data Analysis Expressions). Here are the steps: Go to the Power Query Editor by clicking on "Edit Queries" in the "Home" tab. Select the date column that you want to change the format for. Click on the "Add Column" tab and then click on "Custom Column". In the "New column name" field, enter a name for the new column. In the "Custom column formula" field, enter the following formula: =FORMAT([Original Column], "MM DD YYYY") Press OK. This will create a new column with the date format "MM DD YYYY" based on the original column. How do I Change The Date Format In Table In Power BI? There are several ways to change the date format in a table in Power BI: Using the "Format" function in a new column: Create a new column in your Power BI dataset, and name it "Formatted Date". In the formula bar, enter the following formula: = FORMAT(, "mm/dd/yyyy") Replace with the name of the column that contains the date you want to format. Press Enter to apply the formula and display the formatted date in the new column Using the "Modeling" tab: Select the column you want to change the format for and go to the "Modeling" tab. Click on "Format" and select the format you want to use. Right-clicking on the column header:Right-click on the column header of the table that you want to change the format for. Select "Format Column" from the context menu. In the "Format" section, select the format you want to use. Using the "Visualizations" pane: Select the table or visual that you want to change the format for. Go to the "Visualizations" pane. Under the "Format" section, select "More formats" -> "Custom" and then enter the format you want. Note that these changes will only affect the table or visual you are working on, and will not change the format of the data in your underlying dataset. Dealing With Inconsistent Date Formats Dealing with inconsistent date formats in Power BI can be challenging, but there are several ways to handle it: Use the "Parse" function: You can use the "Parse" function to convert a string value to a date/time value. This can be useful if the date format is consistent but not recognized by Power BI, for example, when the date is in the format "dd-MM-yyyy" instead of "MM-dd-yyyy". Use the "Format" function: You can use the "Format" function to convert a date/time value to a string value in a specific format. This can be useful if the date format is consistent but not in the format you need, for example, when the date is in the format "MM-dd-yyyy" but you need it in the format "yyyy-MM-dd". Use "Replace Values" feature: You can use the "Replace Values" feature to replace specific values in a column with new values. This can be useful when the date format is not consistent and you have a small number of inconsistent formats. Use "M" Language: You can use the "M" language to write custom expressions to transform the date format. This can be useful when the date format is not consistent and you have a large number of inconsistent formats. Combine the above methods: You can combine the above methods to tackle the inconsistent date formats. It's important to note that, regardless of the method you choose, you'll need to do some data preparation and cleaning before you can use the data in Power BI. It's also a good practice to check and validate the data after you've cleaned it to ensure that it's accurate and in the format you need. How Do I use The Date Hierarchy In Power BI In Power BI, you can use the built-in date hierarchy to drill down and drill up through different levels of date granularity, such as year, quarter, month, and day. Here are the steps to use the built-in date hierarchy: Select a visualization, such as a chart or a table, that you want to use the date hierarchy on. Drag and drop a date column from your data model onto the visualization's x-axis, or into the "Values" or "Axis" field, depending on the visualization type. Click on the "More options" button (represented by three dots) next to the date column on the x-axis, and select "New Group" In the "New Group" dialog box, select the "By" field and choose the date column you want to group by. Power BI will automatically create a new group for each level of the date hierarchy. You can then select the level you want to drill down to, for example, if you want to drill down to the month level, you can select the months group. You can also click on the plus sign next to the date group to drill down to the next level, or the minus sign to drill up to the previous level. You can also group your data by other levels like days, weeks, quarters or years. You can also format your date field to your desired format as well. By using the built-in date hierarchy, you can quickly and easily drill down and drill up through different levels of date granularity, and analyze your data in a way that makes the most sense for your analysis.

Use Bennyhoff Products and Services ( BPS ) BPS’s data warehouse tools offer businesses a sustainable way to report and analyze data to get valuable insights that can assist them in responding to market needs. This helps businesses remain competitive in an ever-changing market.This article describes a data warehouse, its architecture, components, and benefits. What is a Data Warehouse? A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. The data is obtained from various sources like, Microsoft Dynamics, POS (Point-of-Sales) systems, and other centralized business systems. The data is standardized and cleaned before it gets to the warehouse. Since data warehouses store large amounts of data, it offers businesses access to historical data that can be used in business intelligence reporting,data visualization and data mining. The intelligence from data warehouses then helps businesses improve decision-making. How Does Construction of a Data Warehouse Work? ETL - Import Data From Source Systems; Extract Transform Load: This process involves extracting data from various sources, transforming it, and loading it into a data warehouse system. The data is usually extracted from the source system, and the transformation is done in a staging area. Loading data into a data warehouse is usually done during nightly builds. ODS - Operational Data Store - Where We Accumulate Data From Multiple Sources: Operational Data Stores are used as interim areas for data warehouses and are designed to integrate data from various sources for real-time analysis and operational reporting. Data is checked to remove redundancies and ensure it complies with organizational rules. DW - Data Warehouse Either Azure Synapse SQL Server Analysis Services: Data Warehouses tools like Azure SQL are essential for data analysis, allowing users to mine data to get valuable insights. The data warehouse software is used with ETL tools to enable analytics and reporting. End Client - Power BI, Excel, Reporting Services: Business intelligence is a data warehouse’s primary derivative; in this step, all the data you need to assess are in the database. You need to visualize it using tables, grids, and charts in Power BI, Reporting Services and Excel to make informed decisions. Data Warehouse Architecture A data warehouse’s architecture has three tiers which are: Bottom tier: Comprises a relational database system that uses the ETL (Extract Transform Load) process to collect, cleanse, and transform data from multiple sources. Middle tier: Comprises an Online Analytical Processing (OLAP) that enables rapid query speeds. Three models can be used — HOLAP, MOLAP, and ROLAP — depending on the existing database system. Top tier: Consists of a reporting tool or front-end user interface that helps businesses conduct ad hoc analysis of their data. At Bennhyoff Products and Services, we query data from source systems into an ODS, then load this into a data warehouse. The Key Components of a Data Warehouse (Star Schema) A schema is a database’s structure that defines how information and objects are grouped and connected. There are various ways organizations can organize information in a data warehouse, such as star, galaxy, and snowflake schema. The star schema is the most popular method. The diagram is usually a star with points radiating outward from the core. The star schema is used to create dimensional data marts and data warehouses. Benefits of a Data Warehouse 1. Delivers Enhanced Business Intelligence Implementing a data warehouse in your business means you’ll gain insights via enhanced information access. This frees executives and managers from making decisions based on their gut feelings or limited information. The decisions that affect your company’s operations and strategy will be based on credible facts and will be substantiated with actual company data and evidence. 2. Enhances Data Quality And Consistency Setting up a data warehouse for business usually involves converting data from multiple data files and source systems and transforming it into a standard format. Information from the various units is standardized, and the inconsistencies are removed. Business units like operations, finance, marketing, and sales will start using the same information repository as a source for their different reports and queries. Therefore, all the units will produce consistent results, thus increasing overall confidence in company data. 3. Generates A High Return On Investment Implementing business intelligence systems like data warehouses help businesses generate more revenue and save on cost. According to IDC (International Data Corporation), analytics projects significantly impact a company’s financial status. Implementing business analytic systems generates a median 5-year ROI of 112% with an average payback of one and a half years. 4. Provides Competitive Advantage Since a data warehouse helps a business get better insights and enhances decision-making, companies can identify more opportunities in data faster than they would if the data was stored in multiple places. 5. Improves The Decision-Making Process Data warehouses support large-scale business intelligence functions like data mining, machine learning, and artificial intelligence — tools business leaders and data professionals can utilize to get evidence to make better decisions in all business areas, from financial management to inventory management. Start Engaging BPS For Your Data Warehouse Today Partnering with a company that understands your needs is essential when considering implementing modern data solutions. At BPS, we collaborate with our clients to develop the best data analysis solutions to meet your needs. Here’s how BPS can help your business implement a data warehouse system: 1rst Step: Determining the items that are not in scope: The success of a data warehouse implementation project heavily depends on data quality; at BPS, we first identify the problem that guides us on the needed solution. While at it, we identify the data that needs to be availed and what transformations need to be done. 2nd Step: Knowing the Project Budget: BPS helps you adopt data solutions fast at an affordable rate; the rates for our services are $150 per hour with a minimum 15-hour purchase. 3rd Step: BPS confirms the goals and objectives of the project: At BPS, we use the SMART approach (Specific >> Measurable >> Achievable >> Realistic and Time Frame) to shorten the project duration, set realistic goals, and articulate them clearly so our clients can understand the expected outcome. 4th Step: Data Modeling: This is a significant stage in the project where we use a data modeling tool to build the warehouse’s schema that defines how objects and data are connected. 5th Step: ETL: Now that we’ve identified your company’s data sources and elements, we use APIs to extract the data from the sources and then use an ETL tool to load this data into the warehouse database. 6th Step: Work and ODS Database: We implement an ODS database as a buffer between your data warehouse and OLTP. This minimizes the risk of your data warehouse failing during night builds. 7th Step: Population: We test the EPL tool needed using an ODS, and once we’re confident everything is working perfectly, we use the ETL tool to populate the Data warehouse based on the schema we employ. 8th Step: SQL SSAS and Azure Synapse Population Move data from ODS to DW: Integrating data into a data warehouse offers a lot of value; populating the data in your existing SaaS tools and other bases is also essential. BPS uses Azure Synapse to automate loading data from multiple sources to your data warehouse. Final Step: Consumption of Data With Power BI The main reason for implementing a data warehouse is to get business intelligence. At BPS, we implement Power BI to help you visualize the data from the warehouse so that you can make informed decisions. Hire BPS today, and we’ll work with you to help you determine the best way for your company to achieve its modern analytics vision. Here is an Example Of An Excel-based Project Plan

Power BI is a powerful business intelligence and data visualization tool developed by Microsoft that allows users to gain insights from their data, make informed decisions, and create interactive reports. It facilitates connection to various sources such as Excel Spreadsheets, SharePoint Online Services, SQL Server databases, and cloud services like Azure or Google Analytics - enabling timely access of vital information for analysis. Furthermore, it offers advanced functionalities including, the capability to transform & model raw/processed datasets alongside creating visuals with an impressive selection tools covering charts; maps; gauges etc. Additionally, Power BI's simple point-and-click interface provides strong capabilities in refining queries through feature-rich filters while also automatically generating meaningful summaries which can be conveniently shared via dashboards crossing multiple platforms & devices, providing anytime recognition potential! Power BI Pro: Power BI Pro: This is a subscription-based version of Power BI that provides a range of additional features and capabilities for data visualization and analysis. It is designed for individual users or small teams who need to collaborate on creating and sharing reports and dashboards. Power BI Premium: Power BI Premium: This is a subscription-based version of Power BI that provides a range of advanced features and capabilities for data visualization and analysis. It is designed for organizations that need to scale their use of Power BI across a large number of users and workspaces. Power BI Embedded: Power BI Embedded: This is a version of Power BI that can be embedded into other applications or websites. It allows developers to incorporate Power BI functionality into custom solutions. Power BI Mobile: Power BI Mobile: This is a mobile app version of Power BI that allows users to access and interact with their reports and dashboards on the go. It is available for both iOS and Android devices. Where Can Download Power BI https://powerbi.microsoft.com/en-us/downloads/ Overview Video - Power BI Licensing (2023) Power BI Free Power BI Free is a free version of Power BI, a cloud-based business analytics service that enables users to visualize, analyze, and share data. It allows users to create and share basic reports and dashboards, and includes a limited set of features. With Power BI Free, users can connect to and import data from a wide range of sources, create interactive reports and dashboards using a variety of visualizations, and share their work with others. However, Power BI Free has a number of limitations compared to the paid versions of Power BI, including limits on the size and number of datasets that can be imported, the number of users who can access a workspace, and the number of reports and dashboards that can be created. Power BI Free Limits The maximum size of a dataset that can be imported into Power BI is 1 GB. The maximum number of rows that can be imported into a dataset is 30,000 rows. The maximum number of columns in a dataset is 16,000 columns. The maximum number of fields that can be used in a single report is 30,000 fields. The maximum number of visuals that can be added to a report is 30 visuals. The maximum number of workspaces 1 workspace. Power BI Pro If you’re interested in Power BI Pro, you’re in a good place. Offering a cost of $9.99 per month, Power BI’s Pro option allows you access to all its modern features with Microsoft 365. The option offers mobile app access, and your users will have the ability to collaborate and publish shared reports. The model size limit is 1 gigabyte and refresh rate is 8/day. Power BI offers encryption and metrics consumption and publishing across the board; with AI visuals and controls for API, this option may be the right one for your work. Limits Of Power BI Pro The maximum dataset size that can be imported into Power BI is 1 GB. The maximum number of rows in a dataset is 150 million rows. The maximum number of columns is 16,000 columns. The maximum number of fields that can be used in a single report is 30,000 fields. The maximum number of visuals that can be added to a report is 1,000 visuals. The maximum number of users in a Power BI Pro workspace is 500 users. The maximum number of workspaces by a single user is 50 workspaces. The maximum number of reports in a single workspace is 1,000 reports. The maximum number of dashboards in a single workspace is 1,000 dashboards. The maximum number of tiles that can be added to a dashboard is 1,000 tiles. Power BI Premium – For Each User Premium for each user offers all the features of Pro, but is able to be licensed to users who have a necessity for data management and access at an organization level. There is advanced AI, paginated reports, and the model size limit is 100 GB. Additionally, there is a 48/day refresh rate, AI visuals and API controls, and XMLA endpoint connectivity. Power BI offers the basic data security and metrics creation option, as well as lifecycle management. At a cost of $20 a month, Premium on Power BI may be the option for you. Power BI Premium – Per Capacity Power BI Premium – per capacity allows users to access data at enterprise scale. You need a Pro license for publishing into Premium capacity, and you can connect Azure to scale to Power BI Premium. With Premium per capacity, you also have mobile access, but can create content without a per-user license. On-premises reporting with report server also comes with this choice, and model size is 400 gigabyte limit. With per capacity, you have access to advanced AI, XMLA, dataflow, and datamart. Some tools with capacity are multi-geo deployment management, bring your own key, and add-on availability. Maximum storage is 100 terabytes and the cost is $4995 a month. Per capacity might be the best for you. Power BI Premium Power BI Premium includes all of the features and capabilities of Power BI Pro, as well as a number of additional features and capabilities that are not available in Power BI Pro. Some of the key differences between Power BI Pro and Power BI Premium are: Capacity: Power BI Premium includes dedicated capacity in the Power BI service, which allows organizations to scale their use of Power BI to meet their needs. Power BI Pro does not include dedicated capacity and is subject to the capacity limits of the shared Power BI service. Data storage and processing: Power BI Premium includes unlimited data storage and processing, while Power BI Pro is limited to 1 GB of data storage and 1 hour of data processing per user per day. Data refresh: Power BI Premium includes the ability to schedule data refresh for datasets up to eight times per day, while Power BI Pro is limited to eight data refresh per day. Paginated reports: Power BI Premium includes the ability to create and publish paginated reports, which are reports that are optimized for printing and have a more traditional layout. Power BI Pro does not include this capability. Report server: Power BI Premium includes the ability to use a on-premises report server to host and share Power BI reports and dashboards, while Power BI Pro does not include this capability. Limits For Power BI Premium The maximum size of a dataset In Power BI is 10 GB. The maximum number of rows that can be imported into a dataset is 10 billion rows. The maximum number of columns in a dataset is 16,000 columns. The maximum number of fields that can be used in a single report is 30,000 fields. The maximum number of visuals that can be added to a report is 1,000 visuals. The maximum number of users in Power BI Premium workspace is 50,000 users. The maximum number of workspaces by a single user is 50 workspaces. The maximum number of reports in a single workspace is 1,000 reports. The maximum number of dashboards in a single workspace is 1,000 dashboards. The maximum number of tiles that can be added to a dashboard is 1,000 tiles. The maximum number of paginated reports in a single workspace is 50 reports. Power BI Embedded: Power BI Embedded is a version of Power BI that can be embedded into other applications or websites. It allows developers to incorporate Power BI functionality into custom solutions, such as custom web or mobile applications. Power BI Embedded is typically used by developers who want to build custom applications or websites that include data visualization and analysis functionality. It allows developers to leverage the powerful data visualization and analysis capabilities of Power BI, while also giving them the flexibility to customize the user experience and integrate the Power BI functionality into their own applications or websites. In general, Power BI Embedded is more suitable for developers who want to build custom applications or websites that include data visualization and analysis functionality, while Power BI Pro is more suitable for individual users or small teams who need to create and share reports and dashboards. Power BI Embedded is available in two pricing tiers: Embed for Customers: This pricing tier is designed for developers who want to embed Power BI into applications or websites that will be used by their customers. It is based on a pay-per-use model, with charges calculated based on the number of users who access the embedded reports and dashboards, and the amount of data they consume. Embed for Your Organization: This pricing tier is designed for developers who want to embed Power BI into applications or websites that will be used by their own organization. It is based on a monthly subscription model, with charges calculated based on the number of users who will be accessing the embedded reports and dashboards. The specific pricing for Power BI Embedded will depend on the specific features and capabilities that you need, as well as the number of users who will be accessing the embedded reports and dashboards. You can contact Microsoft or a Microsoft partner for more information on pricing and to get a quote for your specific needs. Power BI Mobile: Power BI Mobile is a mobile app version of Power BI that allows users to access and interact with their reports and dashboards on the go. It is available for both iOS and Android devices. Power BI Mobile is available as a free download from the App Store or Google Play. It can be used to access any Power BI content that you have permission to view, including reports, dashboards, and datasets. Note that to use Power BI Mobile, you will need a Power BI Pro or Power BI Premium subscription. Power BI Mobile is not included with the free version of Power BI. Can I try Power BI before I Sign up For An Account yes, there is a free trial available for Power BI. The free trial gives you full access to all of the features and capabilities of Power BI Pro for a period of 60 days. During the trial, you can create and share reports and dashboards, connect to and import data from a wide range of sources, and collaborate with others in real-time. To sign up for the free trial, you will need to create a free Microsoft account and then visit the Power BI website. From there, you can click the "Start free" button to begin the trial. You will need to provide your billing information when you sign up, but you will not be charged until the trial period is over, and you can cancel at any time. Sign Up For An Account Here https://powerbi.microsoft.com/en-us/ Power BI is also included as part of several different Office 365 subscriptions. Here are the Office 365 subscriptions that include Power BI: Office 365 E5: This is the top-tier Office 365 subscription that includes a range of advanced features and tools, including Power BI. Office 365 A5: This is a specialized Office 365 subscription that is designed for security and compliance professionals. It includes Power BI. Office 365 F1: This is an Office 365 subscription designed for front-line workers. It includes Power BI. Office 365 Business Premium: This is an Office 365 subscription designed for small and medium-sized businesses. It includes Power BI. Microsoft 365 E5: This is a subscription that includes Office 365, Windows 10, and Enterprise Mobility + Security. It includes Power BI. Microsoft 365 A5: This is a specialized subscription that includes Office 365, Windows 10, and Enterprise Mobility + Security. It is designed for security and compliance professionals and includes Power BI. Microsoft 365 F1: This is a subscription that includes Office 365, Windows 10, and Enterprise Mobility + Security. It is designed for front-line workers and includes Power BI.

There are several commonly misconfigured settings in SQL Server that can cause performance issues, security vulnerabilities, or other problems. Some of the most common misconfigurations include: Max Server Memory: This setting controls the maximum amount of memory that the SQL Server instance can use. If it is set too low, the SQL Server instance may not have enough memory to perform well, and if it is set too high, it can cause other processes on the server to run slower. The maximum amount of memory that should be configured for SQL Server depends on the specific requirements of your system and usage. However, there are some general guidelines that can be followed: Reserve 1 GB of memory for the operating system and other applications running on the server. Reserve additional memory for non-SQL Server related services that are running on the server. For a dedicated SQL Server instance, configure the max server memory setting to be slightly less than the total physical memory on the server. This will allow for some memory to be used for disk caching by the operating system, which can help improve disk performance. Monitor the server's memory usage over time to ensure that SQL Server is not consistently using all of the available memory. If the server's memory usage is consistently high, consider increasing the max server memory setting. Consider the specific requirements of your workload. For example, if you are running a large data warehouse, you may need to set a higher max server memory setting than for a smaller OLTP workload. It's also important to note that the max server memory setting is not a hard limit, and SQL Server may temporarily exceed the specified value if necessary. It's just a way to control how much memory SQL Server will use, but it doesn't mean that SQL Server will use the whole memory configured. Also, it's recommended to monitor the server's performance and adjust the max server memory setting accordingly. If the server is not performing well, or you see SQL Server is not using all the memory, you may want to increase the max server memory setting. On the other hand, if you see the server is running out of memory, you may want to decrease the max server memory setting. Cost Threshold for Parallelism: This setting controls the threshold at which SQL Server starts using multiple processors to execute a single query. If it is set too low, the SQL Server instance may not take full advantage of the available processors, and if it is set too high, it can cause performance issues. Here are some general guidelines for configuring the cost threshold for parallelism: For a dedicated SQL Server instance, start by setting the cost threshold for parallelism to a relatively low value, such as 5 or 10, to allow more queries to be executed in parallel. Monitor the performance of the server and the number of parallel worker threads being used. If the server is not performing well and there are a large number of parallel worker threads, consider increasing the cost threshold for parallelism. If the server is over-utilized, and the number of parallel worker threads is high, consider increasing the cost threshold for parallelism to reduce the number of parallel operations and ease the pressure on the server. Consider the specific requirements of your workload. For example, if you have a data warehouse with large and complex queries, you may want to set a lower cost threshold for parallelism to allow more queries to be executed in parallel. If you have an OLTP workload, you may want to set a higher cost threshold for parallelism, to reduce the number of parallel operations and improve the performance of small and simple queries. It's important to note that the cost threshold for parallelism is not a hard limit and it's not only the cost of the query but also the resources available on the server that determine if a query can be executed in parallel or not. Also, the default value of 5 is suitable for most general scenarios, but monitoring and testing are needed to find the optimal value for your specific environment. Recovery Model: This setting controls how the SQL Server instance handles transactions and backups. If it is set to the wrong value, it can cause data loss or other problems Simple recovery mode in SQL Server has the following pros and cons: Pros: Faster backups: Backups in simple recovery mode only need to back up the data pages, which can be faster than backing up the entire transaction log in full recovery mode. Smaller backups: Backups in simple recovery mode are smaller than backups in full recovery mode because they do not include the transaction log. Cons: No point-in-time recovery: In simple recovery mode, you can only restore the database to the point of the most recent backup. You cannot restore the database to a specific point in time. No log shipping: Log shipping is not possible in simple recovery mode. Log shipping is a feature that allows you to automatically send transaction log backups from one server to another, so that you can use the second server as a disaster recovery solution. Full recovery mode has the following pros and cons: Pros: Point-in-time recovery: In full recovery mode, you can restore the database to a specific point in time. Log shipping: Log shipping is possible in full recovery mode. Cons: Slower backups: Backups in full recovery mode need to back up the entire transaction log, which can be slower than backing up just the data pages in simple recovery mode. Larger backups: Backups in full recovery mode are larger than backups in simple recovery mode because they include the entire transaction log. It's important to note that the recovery mode you choose will depend on the specific requirements of your application and the level of data protection you need. Auto Create Statistics: This setting controls whether or not the SQL Server instance automatically creates statistics on columns that are used in queries. If it is disabled, the SQL Server instance may not perform well, and if it is enabled, it can cause performance issues. It is generally recommended to enable the "Auto Create Statistics" setting in SQL Server. This is because, without statistics, the query optimizer cannot accurately estimate the number of rows that will be returned by a query, which can lead to poor query performance. Enabling this setting can improve query performance by allowing the query optimizer to make more accurate estimates, which can result in better query plans. It's worth noting that Auto Create Statistics can also have some negative impact on performance and disk space if it is creating statistics on too many columns or tables. It's important to monitor the statistics and manage them accordingly. Additionally, if the data in the table is frequently updated or inserted, the statistics may become outdated and could lead to poor query performance. In such cases, it's important to consider schedule regular statistics update or set the AUTO_UPDATE_STATISTICS_ASYNC option to ON to avoid blocking queries while statistics are updated. Auto Update Statistics: This setting controls whether or not the SQL Server instance automatically updates statistics on columns that are used in queries. If it is disabled, the SQL Server instance may not perform well, and if it is enabled, it can cause performance issues. Auto Shrink: This setting controls whether or not the SQL Server instance automatically shrinks the database files. If it is enabled, it can cause performance issues, and if it is disabled, it can cause the database files to grow too large. Instead of enabling the auto shrink, it's recommended to use a more controlled approach to managing the size of a database. This can include: Using the Database Engine Tuning Advisor to identify and remove unnecessary indexes and fragmentation. Scheduling regular index maintenance, such as rebuilds and reorganizes, to reduce fragmentation. Truncating or dropping large tables or partitions that are no longer needed. Monitor the disk space usage and adjust the file growth settings of the database files to prevent them from becoming too large. In summary, the "Auto Shrink" setting in SQL Server is not recommended and can cause performance issues and data corruption. Instead, it is better to use a more controlled approach to managing the size of a database. I

SQL Server Integration Services (SSIS) is a platform for building high-performance data integration and workflow solutions. It is a component of the Microsoft SQL Server database software that can be used to perform a wide range of data integration and migration tasks, such as extracting data from various sources, transforming and cleaning it, and then loading it into a destination database or data warehouse. SSIS also supports the creation of custom tasks and transformations, as well as the integration of data from a variety of sources, including databases, flat files, and Web services. SQL Server Integration Services (SSIS) can import data from a variety of sources, including: Relational databases: SSIS can import data from various relational databases, such as SQL Server, Oracle, MySQL, and others. Flat files: SSIS can import data from flat files, such as CSV, Excel, and text files. OLE DB and ADO.NET data sources: SSIS can import data from OLE DB and ADO.NET data sources, such as Access, Excel, and other data sources that support the OLE DB or ADO.NET interfaces. XML data: SSIS can import data from XML files or Web services. Web services: SSIS can import data from Web services through the use of Web Service Task. Data from other sources: SSIS can import data from other sources using the Script Task or the Execute Process Task. It's important to note that SSIS can also import data from multiple sources at the same time and also supports real-time data integration using the CDC (Change Data Capture) feature.

In SQL Server, an index is a data structure that improves the performance of queries by allowing the database engine to quickly locate and retrieve the requested data. An index is similar to an index in a book, it allows you to quickly find a specific page based on a specific keyword or value. Indexes in SQL Server can be created on one or more columns of a table or view, and they can be of different types, such as: Clustered index: A clustered index determines the physical order of data in a table. A table can have only one clustered index, because the data rows themselves can be stored in only one order. A non-clustered index does not affect the physical order of the data rows, but it contains a copy of the indexed columns and a pointer to the actual data row. A table can have multiple non-clustered indexes. Unique index: A unique index ensures that no two rows of a table have duplicate values in the indexed column(s). Full-Text index: A full-text index is used to improve the performance of full-text searches on large text columns. Columnstore index: A columnstore index is a type of non-clustered index that is optimized for data warehousing scenarios and it's designed to work with very large data sets. XML index: An XML index allows you to create a non-clustered index on an XML data type column in a table. Spatial index: A spatial index is a type of index that is used to improve the performance of spatial queries on data that is stored in a geometry or geography data type column. TLDR - Indexes OR Detailed Info :) A clustered index in SQL Server is a type of index that determines the physical order of data in a table. In other words, it determines how the data rows are stored on disk. A table can have only one clustered index, because the data rows themselves can be stored in only one order. When a table is created, the primary key is automatically used as the clustered index unless another column or set of columns is specified as the clustered index. If a clustered index is not defined, then a unique, non-clustered index is created by default and is known as a heap. Clustered Index A clustered index is created on a column or set of columns that have a high degree of uniqueness and are often used in queries to retrieve data. The column or set of columns used for the clustered index are known as the key columns. The values in the key columns are used to order the data rows in the table. The clustered index also includes a non-clustered index, which contains a copy of the key columns and a pointer to the actual data row, this allows the database engine to quickly locate and retrieve the requested data. Queries that use the key columns of the clustered index to filter or sort the data will generally perform better than those that don't. However, having too many clustered indexes on a table can cause performance issues because of the extra overhead of maintaining them. It's important to consider the usage of the table, the number of inserts, updates, and deletes and the selectivity of the column before creating a clustered index. Non-clustered indexes Non-clustered indexes are generally used to improve the performance of queries that filter or sort the data based on columns that are not included in the clustered index. They are also used to enforce unique constraints on columns that are not the primary key. Non-clustered indexes can also be filtered, this means that the index only includes a subset of the rows in the table, allowing the database engine to quickly locate and retrieve the requested data from a smaller set of rows. Like clustered indexes, non-clustered indexes also have their own trade-offs, creating too many non-clustered indexes can also cause performance issues due to the extra overhead of maintaining them. It's important to consider the usage of the table, the number of inserts, updates, and deletes and the selectivity of the column before creating a non-clustered index. Unique Index A unique index in SQL Server is a type of index that ensures that no two rows of a table have duplicate values in the indexed column(s). It is similar to a non-clustered index, but it also enforces the constraint of unique values in the indexed columns. When a unique index is created, the database engine checks that there are no existing rows with duplicate values in the indexed column(s) and it will prevent new rows with duplicate values from being inserted. If a new row is inserted that would cause a duplicate value, the insert will fail and an error will be returned. A unique index can be created on one or more columns of a table. The columns that are included in a unique index are known as the key columns. The values in the key columns are used to ensure the uniqueness of the data. Unique indexes are commonly used to enforce unique constraints on columns that are not the primary key, such as a unique identifier or an email address. They can also be used to improve the performance of queries that filter or sort the data based on unique values in the indexed columns. It's important to note that a unique index can be created on a nullable column, however, it will allow only one NULL value. Like other types of indexes, creating too many unique indexes can cause performance issues due to the extra overhead of maintaining them. It's important to consider the usage of the table, the number of inserts, updates, and deletes and the selectivity of the column before creating a unique index. Spatial Index A spatial index in SQL Server is a type of index that is used to improve the performance of spatial queries on data that is stored in a geometry or geography data type column. Spatial data is data that represents the position and shape of objects in two-dimensional space, such as points, lines, and polygons. Spatial indexes are designed to work with very large sets of spatial data, allowing the database engine to quickly locate and retrieve the requested data. When a spatial index is created, it is built using a grid-based data structure called a spatial indexing grid. The grid divides the spatial data into a set of smaller, regularly-shaped cells, which makes it possible to quickly locate and retrieve the requested data. Spatial indexes are used in geographic information systems (GIS) and other applications that involve working with spatial data, such as location-based services, transportation systems, and environmental monitoring. They can be used to perform operations such as spatial joins, spatial filtering, and spatial analysis on large sets of spatial data. Spatial indexes are also used to enforce spatial constraints, such as ensuring that a point is within a specific area or that two lines do not intersect. Spatial indexes can greatly improve the performance of spatial queries, but they also add overhead to data modification operations, such as INSERT, UPDATE, and DELETE. It's important to consider the usage of the spatial data, the number of inserts, updates, and deletes and the size of the data before creating a spatial index. Full-Text Index A full-text index in SQL Server is a type of index that is used to improve the performance of full-text searches on large text columns. Full-text search allows you to search for specific words or phrases in large amounts of text data, such as documents, emails, or articles. When a full-text index is created, the database engine uses a process called full-text indexing to create a separate index of the text data. This index contains a list of the words and phrases that appear in the text data, along with their frequency and location. Full-text indexing is performed on the text data column and it can be done on one or more columns. The indexed columns are called Full-Text indexed column. A full-text search query can then be used to search the index for specific words or phrases, allowing the database engine to quickly locate and retrieve the requested data. The full-text search query can include wildcard characters and Boolean operators, such as "AND" and "OR", to further refine the search. Full-text indexes can be used to improve the performance of searches on large text columns, such as those that contain documents, emails, or articles. They can also be used to improve the performance of searches on columns that contain large amounts of unstructured data, such as product descriptions, resumes, or customer feedback. It's important to note that full-text indexing requires additional disk space, and also it's not available on all editions of SQL Server. Full-text indexes are not recommended for small text columns or for columns that are frequently updated. Columnstore Index A columnstore index in SQL Server is a type of non-clustered index that is optimized for data warehousing scenarios and it's designed to work with very large data sets. It uses a column-based storage approach, which is different from the traditional row-based storage approach used by other types of indexes. When a columnstore index is created, it stores the data in columns rather than rows. This allows the database engine to compress the data more effectively and to retrieve only the specific columns that are needed for a query, rather than the entire row. Columnstore indexes are best suited for large data warehousing scenarios, where large amounts of data need to be analyzed and where queries typically retrieve a small subset of the data. They can also be used for large reporting scenarios, where the queries are typically read-only and where the performance of the queries is more important than the performance of data modification operations. Columnstore indexes can greatly improve the performance of data warehousing queries, but they also add overhead to data modification operations, such as INSERT, UPDATE, and DELETE. They are not recommended for small tables or for tables that are frequently updated. It's important to note that columnstore indexes are not available on all editions of SQL Server, and they also require additional disk space.

SQL Server constantly checks several things to ensure the proper functioning of the databases and the instance. Some of the checks that SQL Server performs include: Data consistency checks: SQL Server regularly checks the consistency of the data in the databases to ensure that the data is valid and that there are no corruption issues. These checks include checks for consistency among the different pages of a database, checks for consistency of indexes, and checks for consistency of database structures. Performance monitoring: SQL Server constantly monitors the performance of the instance and the databases, including monitoring of resource usage such as CPU, memory, and disk I/O, to identify and troubleshoot performance issues. Security checks: SQL Server checks the security of the instance and the databases, including monitoring of login attempts, validating the authenticity of users, and monitoring for security breaches. Backup and recovery checks: SQL Server checks the backups of the databases to ensure that they are complete and can be used for recovery in case of a disaster. This includes checking the backup files for consistency and completeness, and monitoring the backup schedule. Job execution: SQL Server checks the status of scheduled jobs, such as backups, indexing, and other maintenance tasks, to ensure they are running as expected. Indexing: SQL Server regularly checks and updates indexes on the databases to ensure that data can be retrieved quickly and efficiently. Logging: SQL Server logs events, errors, and other information about the instance and databases, which can be used for troubleshooting and auditing. Memory management: SQL Server constantly checks and manages the memory usage of the instance and the databases to ensure that resources are used efficiently and that there are no memory leaks. Automatic Updates: SQL Server also checks for updates that can improve the security and performance of the instance, and it can also automatically install them. These are some of the checks that SQL Server performs, but it's important to mention that SQL Server also performs many other checks and tasks to ensure the proper functioning of the instance and the databases. The frequency at which SQL Server performs certain checks and tasks can vary depending on the specific check or task, as well as the specific needs of the organization. Here are some general guidelines on the frequency of some of the checks and tasks that SQL Server performs: Data consistency checks: These checks are typically performed on a regular basis, such as daily or weekly, to ensure that the data is valid and that there are no corruption issues. Performance monitoring: Performance monitoring is a constant process, SQL Server is constantly monitoring the performance of the instance and the databases, which allows it to quickly identify and troubleshoot performance issues. Security checks: These checks are also performed on a regular basis, such as daily or weekly, to ensure that the instance and databases are secure and that there are no security breaches. Backup and recovery checks: SQL Server checks the backups of the databases on a regular basis, such as daily or weekly, to ensure that they are complete and can be used for recovery in case of a disaster. Job execution: SQL Server checks the status of scheduled jobs on a regular basis, such as daily or weekly, depending on the job's schedule. Indexing: SQL Server regularly checks and updates indexes on the databases to ensure that data can be retrieved quickly and efficiently. The frequency of indexing is determined by the data modification rate on the databases, it can be done daily, weekly or monthly. Logging: SQL Server logs events, errors, and other information about the instance and databases on a constant basis, which can be used for troubleshooting and auditing. Memory management: SQL Server constantly checks and manages the memory usage of the instance and the databases, to ensure that What Command Do I Run To See This Data? DBCC CHECKDB is a command in SQL Server that is used to check the physical and logical consistency of a database. It performs a variety of checks on the data and the database structures, including checks on the data pages, index pages, and allocation structures, as well as checks on the database consistency. When DBCC CHECKDB is executed, it performs the following checks on the database: Consistency checks: DBCC CHECKDB checks the consistency of the data and database structures, such as the data pages, index pages, and allocation structures. It verifies that the structures of the database are consistent with the data stored in the pages. Integrity checks: DBCC CHECKDB checks the integrity of the data and the database structures, such as the data pages, index pages, and allocation structures. It verifies that the data is consistent with the structures of the database. Allocation checks: DBCC CHECKDB checks the allocation of the data and the database structures, such as the data pages, index pages, and allocation structures. It verifies that the data and the structures of the database are properly allocated. Repair options: DBCC CHECKDB can also repair errors found during the consistency checks, such as allocation errors, consistency errors, and integrity errors. DBCC CHECKDB can be run on a specific database by calling it with the name of the database, or it can be run on all databases by using the command DBCC CHECKDB (‘ALL_DATABASES’)

When putting data into matrices or tables in Power BI, there are features you can add to enhance data and ease of visibility. Titles and graphics add to a table and help users visualize your data with ease. “In Power BI, you also have a lot of formatting options,” says Bas from “How to Power BI.” “In PowerPoint we have all of the tools that we need to build the placeholders for the visualizations and the background of our report. Now, the big benefit is also, then we just have one image that we can use as the background instead of having all different kinds of objects that need to load separately.” With a new slide, incorporate colors, shapes, and font and size to maximize visual acuity and appeal for your analysis. Cards use same shapes, and you can use visualizations and colors: gradients and text enhance matrices as well. Consider the 2 below data graphs. The first was tabulated without the use of Microsoft PowerPoint: which is more enticing to you? With colors, graphics, graphs, and text changes, your information pops in the slides. Mini data graphs also incorporate data into the table, which is invaluable to users who are investigating your data to learn more. Guy in a Cube offers advice for improving Power BI reports. Custom Backgrounds and Themes Backgrounds can offer readability for users and make a report more eye-appealing. The first graph is plainer, but the second incorporated different colors and gradients that increased appeal in the graph. Themes also enhance reports: titles and colors truly improve graphs. Adam advises to ensure colors “complement each other as well as are accessible for folks (who) may be visually impaired.” Tool Tips and Drillthrough Guy in a Cube advises using Tool Tips which are available in Power BI. Report Page Tool Tips make reports enhanced. Extra info will improve usability for users. Drillthrough is something that offers users choice to “drill into … additional information for a specific item” or “help separate from a summary-level down to a detail-level.” Conditional drillthrough is an option, too. Conditional drillthrough makes a report “an app-like experience.” Decomposition in the report alters visuals – switches visuals and enhances graphs. Bookmarks and buttons also increase user efficacy. Conclusion Tips and tricks that allow users to make improved tables and matrices in Power BI are easy to use. Try out Power BI for incredible and improved data analysis.

Power BI boasts a swiftly-adapting and efficient method for organizing and formulating data in an ever-changing modern world. As a forerunner in the analysis of data for your organization. Power BI hosts a myriad of avenues for maintaining streamlined analyses and keeping your team’s data organized. still some of them are key to keeping your employees’ data efficiently at their fingertips. Read on for some of the best advantages of Power BI’s accessibility. Power BI Complies with US Section 508 - US Section 508 is a standard that requires governments and federal agencies to make their electronic and information technology accessible to people with disabilities. EN 301 549 - EN 301 549 is the Harmonized European Standard for Accessibility requirements for ICT products and services. Built-in Accessibility Features Here are some of the main accessibility features of Power BI: Keyboard navigation: Power BI supports keyboard navigation, allowing users to use the keyboard to access and interact with the user interface. To use keyboard navigation in Power BI, you can use the following keys: Tab: Move focus to the next focusable element Shift + Tab: Move focus to the previous focusable element Enter: Perform the action for the selected element Space: Perform the action for the selected element Arrow keys: Navigate within the selected element Esc: Close the current dialog or menu You can also use the following keys to navigate between pages in Power BI: Ctrl + PgUp: Move to the previous page Ctrl + PgDn: Move to the next page Ctrl + Home: Move to the first page Ctrl + End: Move to the last page High contrast mode: Power BI provides a high contrast mode that makes it easier to read text and distinguish between different colors. To enable high contrast mode in Power BI, you can follow these steps: Open Power BI and go to the "File" menu. Click on "Options and settings" and then select "Accessibility". In the "Accessibility" window, check the "Use high contrast colors" option. Click on "OK" to apply the changes. You can also enable high contrast mode in Power BI using the shortcut: Alt + H + C. Screen reader support: Power BI supports screen readers, such as Microsoft Narrator, JAWS, and NVDA, allowing users with visual impairments to access and use the product. To enable screen reader support in Power BI, you can follow these steps: Open Power BI and go to the "File" menu. Click on "Options and settings" and then select "Accessibility". In the "Accessibility" window, check the "Use screen reader support" option. Click on "OK" to apply the changes. Power BI supports screen readers such as JAWS and NVDA. You will need to have a screen reader installed on your computer and set as the default screen reader in Windows in order to use it with Power BI. Closed captions and audio descriptions: Power BI provides closed captions and audio descriptions for videos, making it easier for users with hearing impairments to access and understand the content. To enable closed captions and audio descriptions in Power BI, you can follow these steps: Open Power BI and go to the "File" menu. Click on "Options and settings" and then select "Accessibility". In the "Accessibility" window, check the "Show closed captions and audio descriptions" option. Click on "OK" to apply the changes. Note that closed captions and audio descriptions are only available for certain visuals and reports in Power BI. If they are available, they will be displayed automatically when you select the visual or report. Alt text: Power BI allows users to add alternative text (alt text) to images, making it easier for screen readers to describe the content of the images to users with visual impairments. Overall, Power BI provides a range of accessibility features to help users with disabilities access and use the product.

Method #1 Select dateDiff(day,login_time, GetDate()) as 'Days Running' From sys.dm_exec_sessions WHERE login_time = (SELECT MIN(login_time) FROM sys.dm_exec_sessions) This code uses the sys.dm_exec_sessions system view to retrieve the login_time of the session with the earliest login_time value. It then calculates the number of days between that login_time and the current date and time using the DATEDIFF function. The result is returned as the number of Days Running. This code will give you the number of days he SQL Server instance has been running without a reboot. Method #2 SELECT DATEDIFF(day, create_date, GETDATE()) AS 'Days Running'FROM sys.databases WHERE name = 'tempdb' This code uses the sys.databases system view to retrieve the create_date of the tempdb database. It then calculates the number of days between that create_date and the current date and time using the DATEDIFF function. The result is returned as the number of Days Running. This code will give you the number of days that the SQL Server instance has been running without a reboot, based on the create_date of the tempdb database. The tempdb database is re-created every time the SQL Server instance is restarted, so the create_date of the tempdb database can be used as an indicator of when the SQL Server instance was last restarted. This information is also available in my SQL Server M&M product as a daily report (Shown Below)

Working with Power BI, you’re probably familiar with how easy it is to use, and how it has high accessibility. If you’d like to work with reports, Power BI offers paginated reports that will make your life easier. Read on to find out more about paginated reports and Power BI. Paginated Reports Power BI offers users a high-tech way to retain information regarding their data. Working with paginated reports, users can see all data in a table, called “pixel perfect.” With pixel perfect paginations, users can exactly report page layout, skipping tables that have low ease of use. With paginated reports, data could easily be printed from a table, and data will not be left out. Paginated reports offer productive options for printing and seeing data in a table. Power BI Report Builder makes paginated reports an option. This is a new tool that lets you create paginated reports just like Power BI Report Server or SQL Server Reporting. If you have Power BI Report Server for 2016 or 2017, you can use Power BI Report Builder to create reports in the future. Power BI Report Builder is compatible with Power BI functions that were once used for earlier reports. Additional Information on Paginated Reports Working with paginated reports offers users a dynamic interface for analysis and management of data. Without an underlying data model, like Power BI reports, paginated reports offer imbedded data and data sets configuration. Creating a report then allows you to connect to a gateway and redirect to various options. Check out the Power BI Microsoft site to learn more. Different ways to make a report increase ease-of-use for users. For example, with paginated reports, users can pick from a matrix, free-form, or chart report. Matrix reports offer an enhanced efficacy with the use of summarized data; free-form paginated reports are data lists, and chart reports benefit users because they use graphical formats, which could be easier for some analyses. Wizards for Power BI With a Report Builder Wizard, reports excel in accessibility and appeal; drag and drop fields, choose a layout, and customize for a better paginated report. Map wizards allow users to use data against a geometric background. Data can be spatial from an SQL query or ESRI shapefile. Tile backgrounds can also be used. Definitions of Reports Paginated reports are report definitions. It doesn’t have data, but says where to get the data, which data, and how to display it. For ease of use, data and layout are incorporated into one report, then you upload the report to Power BI service, and then redirect data to a gateway. You can create subscriptions for paginated reports, which are similar to Power BI Report Server subscriptions. With paginated reports, you can also use deployment pipelines to test reports before publication. Development, test, and production go into deployment pipelines, and are highly useful for paginated report creators. Tricks for Power BI Paginated Reports With Power BI, you don’t need a license to use Power BI Report Builder. It can be accessed for free. You can publish paginated reports in a Power BI premium capacity space. With Gen1 Power BI, paginated reports are created through the admin portal, and with Gen2 Power BI reports are enabled automatically. Whatever Power BI choice is right for you, will certainly increase improve your data analysis!