{"id":11166,"date":"2026-06-16T13:22:05","date_gmt":"2026-06-16T11:22:05","guid":{"rendered":"https:\/\/powerbi.pl\/?p=11166"},"modified":"2026-06-11T13:29:01","modified_gmt":"2026-06-11T11:29:01","slug":"databricks-and-power-bi-a-complete-guide","status":"publish","type":"post","link":"https:\/\/powerbi.pl\/en\/blog\/microsoft-power-bi-en\/databricks-and-power-bi-a-complete-guide","title":{"rendered":"Databricks and Power BI \u2013 a complete guide"},"content":{"rendered":"

That is why more and more companies choose to combine the Databricks<\/strong> platform with Power BI<\/strong>. This approach enables the creation of a modern data ecosystem in which advanced data processing and preparation take place in Databricks, while analysis and visualization are performed in Power BI.<\/p>\n

 <\/p>\n

 <\/p>\n

What is Databricks?<\/h2>\n

Databricks<\/strong> is a modern platform for data processing, management, and analysis in a cloud environment. It was created by the developers of Apache Spark for organizations that need a high-performance environment to work with large datasets.
\nThe core idea of the platform is to unify data engineering, analytics, artificial intelligence, and machine learning processes in one consistent environment. This allows business, analytics, and technical teams to collaborate on a shared data platform.<\/p>\n

 <\/p>\n

The role of Apache Spark in data processing<\/h3>\n

At the heart of Databricks lies Apache Spark<\/strong>, one of the world\u2019s most popular data processing engines. Spark is designed for distributed computing, enabling the processing of massive volumes of data much faster than traditional database solutions.
\nThe key benefits of using Apache Spark include:<\/p>\n

    \n
  • parallel data processing across multiple nodes,<\/li>\n
  • support for structured and unstructured data,<\/li>\n
  • high analysis performance,<\/li>\n
  • support for ETL and ELT processes,<\/li>\n
  • the ability to implement advanced Machine Learning projects.<\/li>\n<\/ul>\n

     <\/p>\n

    Processing large data volumes in the cloud<\/h3>\n

    In many organizations, the volume of data grows month by month. Data comes from multiple sources such as:<\/p>\n

      \n
    • ERP systems,<\/li>\n
    • financial and accounting systems,<\/li>\n
    • sales platforms,<\/li>\n
    • production applications,<\/li>\n
    • CRM systems,<\/li>\n
    • social media,<\/li>\n
    • IoT solutions.
      \nDatabricks is designed to handle such environments. The platform leverages cloud computing capabilities, allowing dynamic scaling of resources based on current business needs.<\/li>\n<\/ul>\n

       <\/p>\n

      Key features of Databricks<\/h2>\n

       <\/p>\n

      Data Engineering<\/h3>\n

      One of the main use cases of Databricks is Data Engineering<\/strong>, which involves preparing data for further analysis.
      \nThe platform enables:<\/p>\n

        \n
      • integration of data from multiple sources,<\/li>\n
      • building ETL and ELT processes,<\/li>\n
      • automation of data pipelines,<\/li>\n
      • data cleansing and transformation,<\/li>\n
      • data quality monitoring.
        \nThis allows organizations to build consistent analytical environments based on reliable and up-to-date data.<\/li>\n<\/ul>\n

         <\/p>\n

        Data Science and Machine Learning<\/h3>\n

        Databricks provides an advanced environment for Data Science teams. The platform supports the entire lifecycle of building analytical models\u2014from data preparation to production deployment.
        \nCapabilities include:<\/p>\n

          \n
        • building predictive models,<\/li>\n
        • sales forecasting,<\/li>\n
        • customer behavior analysis,<\/li>\n
        • anomaly detection,<\/li>\n
        • development of AI-based solutions.
          \nAs a result, Databricks is often used in digital transformation and advanced business analytics projects.<\/li>\n<\/ul>\n

           <\/p>\n

          Data Lakehouse<\/h3>\n

          One of the most important elements of the platform is the Data Lakehouse<\/strong> concept.
          \nThis model combines the advantages of traditional data warehouses and data lakes, offering:<\/p>\n

            \n
          • a central data repository,<\/li>\n
          • high scalability,<\/li>\n
          • support for structured and unstructured data,<\/li>\n
          • high data quality,<\/li>\n
          • the ability to perform business analytics and AI projects in one environment.
            \nThe Lakehouse architecture is becoming one of the most popular approaches to data management in modern enterprises.<\/li>\n<\/ul>\n

             <\/p>\n

            Real-time data processing<\/h3>\n

            More and more organizations expect access to up-to-date business information without delays.
            \nDatabricks enables real-time data processing, allowing companies to:<\/p>\n

              \n
            • monitor operational processes,<\/li>\n
            • analyze business events on an ongoing basis,<\/li>\n
            • respond faster to market changes,<\/li>\n
            • support decision-making with current data.
              \nThis is particularly important in sales, production, logistics, and finance.<\/li>\n<\/ul>\n

               <\/p>\n

              What is Power BI?<\/h2>\n

              Power BI<\/strong> is a Business Intelligence platform created by Microsoft that enables data analysis, report creation, and monitoring of key business metrics in real time. The tool is designed for both data analysts and business users who need fast access to information supporting decision-making processes.
              \nPower BI allows integration of data from various sources such as ERP and CRM systems, Excel spreadsheets, databases, cloud applications, and marketing platforms. This enables organizations to create a single, consistent source of information for the entire company.<\/p>\n

               <\/p>\n

              The role of Power BI in data analysis<\/h3>\n

              In many companies, data is scattered across different systems and departments. Power BI enables combining and transforming it into clear business insights.
              \nKey use cases of Power BI include:<\/p>\n

                \n
              • financial performance analysis,<\/li>\n
              • sales monitoring,<\/li>\n
              • profitability analysis of products and customers,<\/li>\n
              • control of operational processes,<\/li>\n
              • KPI tracking.
                \nThis allows users to quickly identify trends, detect anomalies, and make decisions based on current data.<\/li>\n<\/ul>\n

                 <\/p>\n

                Creating reports and dashboards<\/h3>\n

                One of the biggest advantages of Power BI is its ability to create interactive reports and dashboards.
                \nReports can include:<\/p>\n

                  \n
                • charts,<\/li>\n
                • tables,<\/li>\n
                • maps,<\/li>\n
                • KPI indicators,<\/li>\n
                • advanced business visualizations.<\/li>\n<\/ul>\n

                   <\/p>\n

                  Self-service business analytics<\/h3>\n

                  Power BI is often referred to as a Self-Service BI<\/strong> tool.
                  \nThis means business users can:<\/p>\n

                    \n
                  • analyze data independently,<\/li>\n
                  • create their own reports,<\/li>\n
                  • explore information without programming knowledge,<\/li>\n
                  • respond faster to business needs.<\/li>\n<\/ul>\n

                     <\/p>\n

                    Why combine Databricks and Power BI?<\/h2>\n

                    In a modern data architecture, each platform is responsible for a specific stage of working with information.
                    \nCombining Databricks and Power BI<\/strong> allows you to leverage the strengths of both solutions:<\/p>\n

                      \n
                    • Databricks handles data integration, processing, and preparation,<\/li>\n
                    • Power BI enables analysis and presentation of results in reports and dashboards.<\/li>\n<\/ul>\n

                       <\/p>\n

                      Databricks as the data processing layer<\/h3>\n

                      Databricks acts as a central platform for data management and processing.
                      \nIn this area, it is responsible for:<\/p>\n

                        \n
                      • integrating data from multiple sources,<\/li>\n
                      • ETL and ELT processes,<\/li>\n
                      • data cleansing,<\/li>\n
                      • business transformations,<\/li>\n
                      • storing data in a Lakehouse architecture.
                        \nAs a result, data delivered to the reporting layer is prepared, structured, and ready for analysis.<\/li>\n<\/ul>\n

                         <\/p>\n

                        Power BI as the reporting and analytics layer<\/h3>\n

                        Power BI uses the data prepared in Databricks to build business reports and dashboards.
                        \nThis enables users to:<\/p>\n

                          \n
                        • monitor key metrics,<\/li>\n
                        • analyze business performance,<\/li>\n
                        • track trends,<\/li>\n
                        • make decisions based on current data.
                          \nThis separation of responsibilities significantly improves the performance of the entire solution.<\/li>\n<\/ul>\n

                           <\/p>\n

                          Benefits of separating data processing from presentation<\/h3>\n

                          In traditional reporting solutions, one platform often handles both data processing and presentation.
                          \nIn the Databricks and Power BI<\/strong> architecture, these tasks are separated, which provides:<\/p>\n

                            \n
                          • higher performance,<\/li>\n
                          • easier scalability,<\/li>\n
                          • better data control,<\/li>\n
                          • higher reporting quality,<\/li>\n
                          • greater system flexibility.
                            \nThis approach is especially important in organizations processing large volumes of data.<\/li>\n<\/ul>\n

                             <\/p>\n

                             <\/p>\n

                            How does Databricks\u2013Power BI integration work?<\/h2>\n

                             <\/p>\n

                            Available connection methods<\/h3>\n

                            Organizations can integrate Databricks with Power BI in several ways, depending on business and technical requirements.<\/p>\n

                             <\/p>\n

                            Databricks SQL Warehouse<\/h3>\n

                            The most commonly used method is connection via Databricks SQL Warehouse<\/strong>.
                            \nThis solution allows running SQL queries on data stored in Databricks and exposing them to Power BI in a performant and secure way.<\/p>\n

                             <\/p>\n

                            Native Connector in Power BI<\/h3>\n

                            Microsoft provides a dedicated Databricks connector in Power BI.
                            \nThanks to this, connection configuration is relatively simple and does not require building additional integration layers.<\/p>\n

                             <\/p>\n

                            DirectQuery<\/h3>\n

                            DirectQuery<\/strong> mode allows Power BI to send queries directly to Databricks while users interact with reports.
                            \nAdvantages of this approach:<\/p>\n

                              \n
                            • access to up-to-date data,<\/li>\n
                            • no need to import large datasets,<\/li>\n
                            • ability to work with very large volumes of data.<\/li>\n<\/ul>\n

                               <\/p>\n

                              Data import<\/h3>\n

                              An alternative is importing data into the Power BI model.
                              \nThis approach provides:<\/p>\n

                                \n
                              • very high report performance,<\/li>\n
                              • faster response time,<\/li>\n
                              • greater data modeling capabilities.
                                \nThis method works especially well for stable datasets refreshed on a defined schedule.<\/li>\n<\/ul>\n

                                 <\/p>\n

                                 <\/p>\n

                                Most common use cases of Databricks and Power BI<\/h2>\n

                                 <\/p>\n

                                Financial reporting<\/h3>\n

                                The combination of Databricks and Power BI is often used in finance, where data quality and consistency are crucial.<\/p>\n

                                 <\/p>\n

                                Consolidation of data from multiple systems<\/h4>\n

                                Databricks enables integration of data from:<\/p>\n

                                  \n
                                • ERP systems,<\/li>\n
                                • financial and accounting systems,<\/li>\n
                                • Excel files,<\/li>\n
                                • business applications.
                                  \nThis allows organizations to create unified financial reports across the company.<\/li>\n<\/ul>\n

                                   <\/p>\n

                                  Profitability and cost analysis<\/h4>\n

                                  Power BI enables monitoring of:<\/p>\n

                                    \n
                                  • customer profitability,<\/li>\n
                                  • product margins,<\/li>\n
                                  • cost structures,<\/li>\n
                                  • budget execution.<\/li>\n<\/ul>\n

                                     <\/p>\n

                                    Sales analytics<\/h3>\n

                                    Modern sales reporting requires fast access to up-to-date data.<\/p>\n

                                     <\/p>\n

                                    KPI monitoring<\/h4>\n

                                    Power BI enables tracking key sales indicators such as:<\/p>\n

                                      \n
                                    • revenue,<\/li>\n
                                    • margin,<\/li>\n
                                    • number of orders,<\/li>\n
                                    • sales plan achievement.<\/li>\n<\/ul>\n

                                       <\/p>\n

                                      Customer and product analysis<\/h4>\n

                                      Combining Databricks and Power BI supports:<\/p>\n

                                        \n
                                      • customer segmentation,<\/li>\n
                                      • basket analysis,<\/li>\n
                                      • product profitability evaluation,<\/li>\n
                                      • identification of sales trends.<\/li>\n<\/ul>\n

                                         <\/p>\n

                                        Supply chain and logistics<\/h3>\n

                                        Companies increasingly use data analytics to optimize logistics processes.<\/p>\n

                                         <\/p>\n

                                        Inventory level analysis<\/h4>\n

                                        Reports can support monitoring of:<\/p>\n

                                          \n
                                        • stock levels,<\/li>\n
                                        • product turnover,<\/li>\n
                                        • product availability,<\/li>\n
                                        • storage costs.<\/li>\n<\/ul>\n

                                           <\/p>\n

                                          Demand forecasting<\/h4>\n

                                          Historical data processed in Databricks can be used to forecast:<\/p>\n

                                            \n
                                          • future sales,<\/li>\n
                                          • product demand,<\/li>\n
                                          • purchasing and production planning.<\/li>\n<\/ul>\n

                                             <\/p>\n

                                            Production data analysis<\/h3>\n

                                            In production environments, operational data is generated continuously.<\/p>\n

                                             <\/p>\n

                                            Monitoring operational processes<\/h4>\n

                                            Power BI enables real-time tracking of:<\/p>\n

                                              \n
                                            • production line performance,<\/li>\n
                                            • resource utilization,<\/li>\n
                                            • production plan execution.<\/li>\n<\/ul>\n

                                               <\/p>\n

                                              Anomaly detection<\/h4>\n

                                              Databricks supports identification of:<\/p>\n

                                                \n
                                              • production anomalies,<\/li>\n
                                              • downtime,<\/li>\n
                                              • quality deviations,<\/li>\n
                                              • inefficient processes.<\/li>\n<\/ul>\n

                                                 <\/p>\n

                                                 <\/p>\n

                                                Summary<\/h2>\n

                                                At EBIS, we help companies design and implement modern analytics solutions based on Databricks<\/strong>, Power BI<\/strong>, and Microsoft Fabric<\/strong>. We support clients at every stage of the project\u2014from data architecture design, through integration and data modeling, to the creation of reports and dashboards that support business processes.
                                                \nContact our team to see how a modern data platform can help your organization make better decisions and fully leverage the potential of data.<\/p>\n

                                                 <\/p>\n","protected":false},"excerpt":{"rendered":"

                                                Modern organizations generate vast amounts of data from ERP systems, CRM platforms, e-commerce platforms, production applications, and marketing tools. However, simply collecting data is not enough to make sound business decisions. 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