Increasing Efficiency in Data Management: The Use of Microsoft Fabric Compared to Manual Work with Excel

In our current data management architecture, we rely on Microsoft Fabric to ensure the efficiency and reliability of data processing. Below, we explain our process and the use of various tools and technologies, as well as the advantages over manual work with Excel.

Data Storage and Processing

Our workflow begins with storing Excel files, either on a SharePoint or by directly uploading them to our Lakehouse. These files are the starting points for our data pipelines:

  1. Excel Files on SharePoint: Files on SharePoint are read using DataFlowGen2. DataFlowGen2 offers advanced error-handling features such as:

    • Tab Check: Checking if the required tab is present in the Excel document.
    • Size Control: Ensuring that the file has a certain minimum size.

      These measures help us detect and correct data quality issues early.

  2. Files in the Lakehouse: Files that are directly uploaded to our Lakehouse are transferred using the Copy Data Action in our pipelines. This action enables quick and reliable transfer and organization of the files in our data store.

Bronze Layer and Data Model

The ingested data forms the Bronze Layer of our data model. The Bronze Layer is the first stage of our data architecture, where raw data is collected and prepared for further processing. Based on this initial data, we have developed several dataflows to transform the raw data into usable information:

  • Processing and Preparation: We have created dataflows that link multiple source tables. Through joins and various transformations, the data is prepared. These steps include data cleansing, normalization, and enrichment to ensure the data is suitable for analytical purposes.

Automation and Sequencing

To ensure a smooth and continuous data flow, we have consolidated all dataflows and the Copy Data Action into a single pipeline. This pipeline is configured to run sequentially every hour. Regular updates ensure that our data is always up-to-date and synchronized. The pipeline architecture also offers the flexibility to add new data sources and processing steps as needed.

Reporting and Visualization

The visual representation and analysis of the processed data are done through a Power BI app, which consists of multiple reports. This app provides an intuitive and user-friendly interface for data visualization and gaining valuable insights. The main features of the Power BI app include:

  • Interactive Dashboards: Offer a comprehensive overview of key metrics and trends.
  • Detailed Reports: Enable in-depth analysis and identification of patterns and anomalies.
  • Real-Time Updates: Ensure that the data in the reports is always current.

Notebooks and Association Analyses

Another advantage of using Microsoft Fabric is the ability to use notebooks for association analyses. These analyses help us identify correlations in our data and gain deep insights that are invaluable for strategic decisions.

Advantages Over Manual Work with Excel

  1. Automation:

    • Microsoft Fabric: Data pipelines like DataFlowGen2 and Copy Data Action enable automated data transfer and processing, significantly reducing manual effort.
    • Manual Work with Excel: Requires manual inputs and transfers, which is time-consuming and prone to errors.

  2. Data Quality and Error Handling:

    • Microsoft Fabric: Offers advanced error-handling features such as tab checks and size control to detect and correct data quality issues early.
    • Manual Work with Excel: Errors and inconsistencies in the data are difficult to detect and correct.

  3. Efficiency and Scalability:

    • Microsoft Fabric: Enables quick and reliable file transfer and organization, supported by a pipeline architecture that updates hourly.
    • Manual Work with Excel: Processes are often slow and not scalable, especially with large data volumes.

  4. Data Integration and Transformation:

    • Microsoft Fabric: Dataflows enable linking multiple source tables, transformations, and enrichments to prepare data for analytical purposes.
    • Manual Work with Excel: Links and transformations are manual and prone to errors.

  5. Reporting and Visualization:

    • Microsoft Fabric: Power BI offers interactive dashboards and reports that enable intuitive data visualization and analysis.
    • Manual Work with Excel: Creating reports and visualizations is time-consuming and less interactive.

  6. Correlations and Analyses:

    • Microsoft Fabric: Use of notebooks for conducting association analyses to identify correlations and gain valuable insights.
    • Manual Work with Excel: Analyses are limited and require advanced knowledge and manual calculations.

Comparison Table

Aspect

Microsoft Fabric

Manual Work with Excel

Automation

Fully automated data pipelines

Manual input and transfer

Error Handling

Advanced error detection and correction

Errors are hard to detect and correct

Efficiency and Scalability

Fast and scalable processes

Slow and not scalable

Data Integration and Transformation

Linking and transforming multiple source tables

Manual and error-prone processes

Reporting and Visualization

Interactive dashboards and reports with Power BI

Time-consuming and less interactive reports

Correlations and Analyses

Use of notebooks for in-depth analyses

Limited analyses, requiring advanced knowledge

Conclusion

By using Microsoft Fabric, we significantly improve the efficiency, reliability, and quality of our data management compared to manual work with Excel. The automated processes and advanced features enable us to make informed decisions and gain strategic insights, while manual work with Excel often leads to errors, inefficient processes, and data inconsistencies. The use of notebooks for association analyses in Microsoft Fabric also allows us to identify correlations, providing additional value.

Author

Nikolaj Unser-Richter

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Simon Jung

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Increasing Efficiency in Data Management: The Use of Microsoft Fabric Compared to Manual Work with Excel

Increasing Efficiency in Data Management: The Use of Microsoft Fabric Compared to Manual Work with Excel

In our current data management architecture, we rely on Microsoft Fabric to ensure the efficiency and reliability of data processing. Below, we explain our process and the use of various tools and technologies, as well as the advantages over manual work with Excel.

Data Storage and Processing

Our workflow begins with storing Excel files, either on a SharePoint or by directly uploading them to our Lakehouse. These files are the starting points for our data pipelines:

  1. Excel Files on SharePoint: Files on SharePoint are read using DataFlowGen2. DataFlowGen2 offers advanced error-handling features such as:

    • Tab Check: Checking if the required tab is present in the Excel document.
    • Size Control: Ensuring that the file has a certain minimum size.

      These measures help us detect and correct data quality issues early.

  2. Files in the Lakehouse: Files that are directly uploaded to our Lakehouse are transferred using the Copy Data Action in our pipelines. This action enables quick and reliable transfer and organization of the files in our data store.

Bronze Layer and Data Model

The ingested data forms the Bronze Layer of our data model. The Bronze Layer is the first stage of our data architecture, where raw data is collected and prepared for further processing. Based on this initial data, we have developed several dataflows to transform the raw data into usable information:

  • Processing and Preparation: We have created dataflows that link multiple source tables. Through joins and various transformations, the data is prepared. These steps include data cleansing, normalization, and enrichment to ensure the data is suitable for analytical purposes.

Automation and Sequencing

To ensure a smooth and continuous data flow, we have consolidated all dataflows and the Copy Data Action into a single pipeline. This pipeline is configured to run sequentially every hour. Regular updates ensure that our data is always up-to-date and synchronized. The pipeline architecture also offers the flexibility to add new data sources and processing steps as needed.

Reporting and Visualization

The visual representation and analysis of the processed data are done through a Power BI app, which consists of multiple reports. This app provides an intuitive and user-friendly interface for data visualization and gaining valuable insights. The main features of the Power BI app include:

  • Interactive Dashboards: Offer a comprehensive overview of key metrics and trends.
  • Detailed Reports: Enable in-depth analysis and identification of patterns and anomalies.
  • Real-Time Updates: Ensure that the data in the reports is always current.

Notebooks and Association Analyses

Another advantage of using Microsoft Fabric is the ability to use notebooks for association analyses. These analyses help us identify correlations in our data and gain deep insights that are invaluable for strategic decisions.

Advantages Over Manual Work with Excel

  1. Automation:

    • Microsoft Fabric: Data pipelines like DataFlowGen2 and Copy Data Action enable automated data transfer and processing, significantly reducing manual effort.
    • Manual Work with Excel: Requires manual inputs and transfers, which is time-consuming and prone to errors.

  2. Data Quality and Error Handling:

    • Microsoft Fabric: Offers advanced error-handling features such as tab checks and size control to detect and correct data quality issues early.
    • Manual Work with Excel: Errors and inconsistencies in the data are difficult to detect and correct.

  3. Efficiency and Scalability:

    • Microsoft Fabric: Enables quick and reliable file transfer and organization, supported by a pipeline architecture that updates hourly.
    • Manual Work with Excel: Processes are often slow and not scalable, especially with large data volumes.

  4. Data Integration and Transformation:

    • Microsoft Fabric: Dataflows enable linking multiple source tables, transformations, and enrichments to prepare data for analytical purposes.
    • Manual Work with Excel: Links and transformations are manual and prone to errors.

  5. Reporting and Visualization:

    • Microsoft Fabric: Power BI offers interactive dashboards and reports that enable intuitive data visualization and analysis.
    • Manual Work with Excel: Creating reports and visualizations is time-consuming and less interactive.

  6. Correlations and Analyses:

    • Microsoft Fabric: Use of notebooks for conducting association analyses to identify correlations and gain valuable insights.
    • Manual Work with Excel: Analyses are limited and require advanced knowledge and manual calculations.

Comparison Table

Aspect

Microsoft Fabric

Manual Work with Excel

Automation

Fully automated data pipelines

Manual input and transfer

Error Handling

Advanced error detection and correction

Errors are hard to detect and correct

Efficiency and Scalability

Fast and scalable processes

Slow and not scalable

Data Integration and Transformation

Linking and transforming multiple source tables

Manual and error-prone processes

Reporting and Visualization

Interactive dashboards and reports with Power BI

Time-consuming and less interactive reports

Correlations and Analyses

Use of notebooks for in-depth analyses

Limited analyses, requiring advanced knowledge

Conclusion

By using Microsoft Fabric, we significantly improve the efficiency, reliability, and quality of our data management compared to manual work with Excel. The automated processes and advanced features enable us to make informed decisions and gain strategic insights, while manual work with Excel often leads to errors, inefficient processes, and data inconsistencies. The use of notebooks for association analyses in Microsoft Fabric also allows us to identify correlations, providing additional value.

Author

Nikolaj Unser-Richter

Simon Jung

TAGS

SHARE ARTICLE

Latest Updates