What are Data Pipelines and How They Support Insights?

Data helps organizations make informed decisions, enhances operational efficiency, fosters innovation, and supports strategic planning. It also drives growth and competitive advantage in the business environment.

But what are data pipelines? Well, this guide shows you what data pipelines are, the importance of data pipelines, and how to build data pipelines.

Table of Contents:

1. What are Data Pipelines?
2. What is the Importance of Data Pipelines?
3. How to Build Data Pipelines?
4. How to Create Data Pipelines in Power BI?
5. How to Create Visualizations in Power BI?
6. Wrap Up

First…

What are Data Pipelines?

A data pipeline helps in moving data from one place to a desired destination (like a data warehouse) while simultaneously transforming and optimizing the data. This way, the data comes in a state that can be used and analyzed to develop business insights.

What is the Importance of Data Pipelines?

• The Imperative for Real-Time Data Access

Nowadays, businesses need real-time data access. With a data pipeline, you’ll get a continuous data flow, which can be visualized using data flow diagrams, helping companies react to market changes and make informed decisions.

• Upholding Data Quality and Integrity

Data is as good as its quality and integrity. With a data pipeline, you’ll have clean, consistent, and reliable data. It automates the process of correcting and detecting errors, and that helps maintain the integrity of the data. It also keeps business owners from making misguided decisions.

• Enhancing Analytical Capabilities

Insights obtained from data analysis are timely and accurate as the data feeds into the analytical tools. Data pipelines automate data preparation and delivery to these tools, and that ensures that insights generated are based on the most current and well-processed data available.

• Streaming Data Pipeline

With a data pipeline, businesses are sure to meet the regulatory standards in their region. Data pipelines also provide a clear and controlled data flow with audit trails and governance controls.

How to Build Data Pipelines?

• Define Objectives

What are the goals of the data pipeline? You should understand the data sources, destination, and the transformations needed. In the context of Power BI transform data, this means ensuring the data is properly cleaned, reshaped, and prepared for analysis, enabling effective visualizations and insights. The transformation step is crucial for ensuring that data flows seamlessly from source to destination while meeting the specific needs of your analysis.

• Choose Tools and Technologies

Your choice of tools and technologies should depend on your requirements. You should consider cloud-based solutions like Google Cloud Dataflow or AWS Glue.

• Data Extraction

Data should be extracted from the source system. Relevant metadata should be captured during the process.

• Data Transformation

Clean, transform, and enrich the data. This step also involves filtering out irrelevant information, converting data types, and handling missing data.

• Data Loading

The transformed data should be loaded into the destination system. The destination system could be a data warehouse, database, or other storage solution.

• Monitoring and Logging

Incorporate logging and monitoring mechanisms to track the pipeline’s health, identify bottlenecks, and troubleshoot issues.

• Testing

Thoroughly test the data pipeline under various scenarios, including failure conditions and edge cases to ensure the reliability and robustness of the data pipeline.

• Version Control

Implement version control for your data pipeline code and configurations to track changes and facilitate collaboration.

• Continuous Improvement

Always review and optimize your data pipeline. Consider feedback from users, monitor performance metrics and KPIs, and make adjustments to meet evolving business needs and enhance efficiency.

How to Create Data Pipelines in Power BI?

Here’s a general guideline to help you create data pipelines in Power BI.

• Get Data

Open Power BI Desktop and click on “Get Data.” After that, choose your preferred data source. It could be Excel, Server, SQL, or Web.

• Transform Data with Power Query

To clean and transform data, use filter columns, remove duplicates, and also use the Query Editor to apply transformations.

• Apply Transformations

If needed, create calculated columns (or measures) using DAX (Data Analysis Expressions).

• Model Data

You should define relationships between tables in the Power BI data model. You can also create hierarchies and organize data to fit your reporting needs.

• Create Visualizations

To design reports and dashboards, drag and drop fields onto the report canvas. Also choose appropriate visuals like charts, graphs, or tables.

• Implement Interactivity

Add filters, slicers, and drill-through actions to make interactive reports. To create custom navigation experiences, use bookmarks and buttons.

• Schedule Data Refresh

If the data has a live connection, configure scheduled data refresh to keep the data up-to-date.

• Publish to Power BI Service

Save the Power BI Desktop file and publish it to the Power BI service.

• Collaborate and Share

Share your dashboards and reports with stakeholders (or colleagues).

• Monitor and Maintain

Keep a close eye on data refreshes and report performance.

How to Create Visualizations in Power BI?

Stage 1: Log into Power BI, enter your email, and click “Submit.”

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Create a Data Set and Select the Data Set to Use in the Sankey Chart

• Go to the left-side menu and click the “Create” button.
• Select “Paste or manually enter data“.

• We’ll use the sample data below for this example.

Total Cost	Company Type	Company Name	Expertise Categories	Expertise	Cost
Total Cost	Subcontractor	Skyline Contractors	Mechanical Installation	Plumbing & Heating	15456
Total Cost	Subcontractor	Skyline Contractors	Mechanical Installation	Mechanical Work	10159
Total Cost	Subcontractor	Onyx General Contractors	Mechanical Installation	Plumbing & Heating	18045
Total Cost	Subcontractor	Onyx General Contractors	Mechanical Installation	Mechanical Work	12695
Total Cost	Subcontractor	Living Well Remodeling	Mechanical Installation	Plumbing & Heating	14589
Total Cost	Subcontractor	Living Well Remodeling	Mechanical Installation	Welding	11456
Total Cost	Supplier	Power-up Builders	Raw Material	Cement	20561
Total Cost	Supplier	Power-up Builders	Raw Material	Steel	32456
Total Cost	Supplier	Five-star Construction	Raw Material	Bricks	10253
Total Cost	Supplier	Five-star Construction	Raw Material	Timber	9000

• Paste the data table above into the “Power Query” window. After that, select the “Create a dataset only” option.

• Navigate to the left-side menu, and click on the “Data Hub” option. Power BI will populate the data set list. If no data set has been created, you’ll get an error message.

• Choose the data set you want to use in the Sankey diagram. After that, you’ll see Power BI populate the screen as shown below.

• Click on the “Create a report” dropdown. Next, select “Start from scratch.”

• A Report Canvas, similar to the one below will appear on your screen.

Stage 3: Add the Power BI Sankey Diagram Extension by ChartExpo

• To create the Sankey Diagram, you’ll have to use an add-in or the Power BI visual from AppSource. Navigate to the right side of the Power BI dashboard, and open the Power BI Visualizations panel. Next, click the ellipsis symbol (…) to import the Power BI Sankey Diagram extension by ChartExpo.

• When the menu opens, select the “Get more visuals” option.

• In the following window that opens, enter “Sankey Diagram for Power BI by ChartExpo” in the highlighted search box. You’ll see the “Sankey Diagram for Power BI by ChartExpo.”

• Click the highlighted “Add” button.

• Power BI will add the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.

Stage 4: Draw a Sankey Diagram with ChartExpo’s Power BI extension

• To do that, select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel. After that, a window similar to the one below will open in the report section of your dashboard.

• You have the option to resize the visual. Moving on, navigate to the right side of the Power BI dashboard, and look out for “Fields” next to “Visualizations.”

• Follow the sequence below when selecting the fields to use in the Sankey chart.
  • Total Cost
  • Company Type
  • Company Name
  • Expertise Categories
  • Expertise
  • Cost

• You’ll have to provide your email address or ChartExpo license key.

Stage 5: Activate the ChartExpo Trial or Apply a Subscription Key

• Select the ChartExpo visual. You’ll see three icons below “Build Visual” in the Visualizations panel.

• Select the middle icon, “Format visual.” After that, the visual properties will be populated.

• As a new user, you’ll have to enter your email address in the textbox under the “Trial Mode” section. The license key will be sent to the email upon subscription. Toggle “Enable Trial’ to activate the 7-day trial.

• The Sankey Diagram you create under the 7-day trial comes with the ChartExpo watermark.

• Enter the license key in the “ChartExpo License Key” textbox in the “License Settings” section. After that, slide the toggle switch next to “Enable License” to “On.”

• The Sankey diagram should be ready. It does not come with a watermark.

• To add a Prefix (like the $ sign) with the numeric values in the chart, you’ll have to expand the “Stats” properties. After that, include the Prefix value.

• To add colors to each node, expand the “Level Colors” properties and select the colors.

• The changes will be saved automatically.

Insights

• At Level 1 (Total Cost), the procurement cost is $155K.
• At Level 2 (Company Type), out of the $155K cost, $82.4K (53.3%) was spent on subcontractors, while $72.3K (46.7%) was allocated to the supplier.
• At Level 3 (Company Name), the supplier cost of $72.3K was divided between two companies: Five-star Construction and Power-up Builder, with charges of $19.3K and $53.0K, respectively.
• The subcontractor cost of $82.4K was distributed among three companies: Onyx General Contractors, Skyline Contractors, and Living Well Remodeling. They charged $30.7K, $25.6K, and $26.0K, respectively, for their services.

Enhance Data Accuracy by Streamlining Data Pipelines in Power BI:

Discover the significance of Data Pipelines with this hands-on video tutorial. These essential systems automate the flow of data between sources and destinations, ensuring seamless integration and analysis. Mastering Data Pipelines enables you to streamline data processing, enhance data quality, and optimize your workflows. With efficient pipelines, you can build scalable, reliable systems that support real-time analytics and drive informed decision-making.

FAQs

What is a simple example of a data pipeline?

A basic pipeline involves the extraction of sales data from a CSV file, transforming it by calculating total revenue and loading the results into a database for analysis.

What is a data pipeline vs ETL?

A data pipeline is a comprehensive system automating data flow from sources to destinations, and that encompasses various processes. ETL (Extract, Transform, Load), on the other hand, is a specific subset of a data pipeline that focuses on structured data movement, transformation, and loading for analytics. ETL is a key component within the broader framework of a data pipeline.

Wrap Up

Data pipelines help organizations automate the systematic flow of data, and it also ensures timely, accurate, and organized movement. Data pipelines can be created in Power BI using Power Query.

To get started, you have to import data from multiple sources, transform it using the Power Query Editor, apply necessary transformations, and load it into the Power BI.

One major benefit of using data pipelines is the scalability and flexibility that comes with it. A data pipeline is designed to scale, and it can handle increasing volumes of data without a hitch. The scalability of data pipelines makes it almost impossible for the data infrastructure to crumble under pressure, and that allows the business to expand seamlessly.

By following the steps in this guide, you’ll be able to easily use the ChartExpo visualization tool to create compelling visuals for your business.