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Posts Categorized / Business Intelligence

  • Jul 19 / 2017
  • 0
Power BI for beginners
Business Intelligence, Cloud Platform, Power BI

Introduction to Power BI

Power BI was Launched in 2014. It is a very power full tool that allows ETL, modeling, analysis, presentation and sharing. Using Powe BI does not require to learn a new language or scripting skills.

Everything you do in Power BI can be broken down into a few basic building blocks.

  • Visualizations (Graphs, charts, maps etc)
  • Data sets (collection/combination of data used to create visualizations)
  • Reports (collection of visualizations that appear on one or more pages and then arranges)
  • Dashboards (contains pages or set of visualizations on single page called canvas and can be shared)
  • Tiles (single visualization found in a report or dashboard)

Underlying Technologies of Power BI

Following technologies are used underlying in Power BI for different operations

  • Query/Table –> Power Query
  • Model –> Power Pivot
  • Presentation –> Power Map
  • Sharing –> Dashboards
  • Analysis –> Charts/Machine learning

Architecture of Power BI

Power BI has two architectural components

  • Power BI desktop (Desktop application for creating reports and visuals)
  • Power BI service (SaaS for sharing and collaborating the reports and dashboards)

Starting with Power BI

To start with power BI you would require office 360 account that can be created with your organization email. Then login to Power BI website to configure the service and download the desktop application as well. There is lot of work being done by Microsoft and community to add more and more features, visualizations and options in the tool. These additions may be included by regular updates provided.

  • Nov 12 / 2008
  • 0
Business Intelligence, dbDigger

Dimensional database VS multidimensional database

Unlike the SQL Server database engine, which supports OLTP as well as data warehousing, Analysis Services supports online analytical processing (OLAP), which is designed to quickly process analytical queries. Data in an OLAP database is stored in multidimensional cubes of aggregated data, unlike the typical table/column model found in relational and dimensional databases.

OLAP technologies are usually built with dimensionally modelled data warehouses in mind, although products such as SQL Server Analysis Services SSAS can access data directly from relational database. However, it is generally recommended to use a warehouse to support more efficient queries, properly cleanse the data, ensure data integrity and consistency, and support historical data. The data warehouse also acts as a checkpoint (not unlike a staging database!) for troubleshooting data extraction, transformation, and load (ETL) operations and for auditing the data.

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