Using Power Query to Clean and Merge Data from Multiple Sources Automatically

In today's data-centered world, organizations rely upon information collected from a range of repositories: Excel spreadsheets, SQL databases, web API endpoints, cloud services, etc. Trying to manage this fractured data is slow and encourages mistakes. Power BI, Microsoft's leading business intelligence tool, has a solution: a built-in data preparation engine called Power Query.

Power Query is where analysts are able to automate the data cleansing, data transformation, and data merges from multiple sources, which makes it part of the foundation of a productive and accurate data analytics process. This includes customer data, sales metrics, operational metrics from different systems, and many other analytic processes. As far as overcoming the fragmented data issue, Power Query can help aggregate and shape all your data into a structured dataset ready for analysis.

What is Power Query?

Power Query is a fast and powerful data transformation tool that is part of Power BI. Power Query allows users to connect to a range of data sources, shape that data using a graphical interface or M code (the formula language for Power Query), and then allows that data to load into the Power BI data model. Users can automate and schedule repetitive tasks like cleansing data, formatting data, and joining datasets with Power Query, maximizing efficiency and consistency.

Why Automating Data Cleaning and Merging is Important

As organizations expand, the management of their data becomes increasingly complex. Companies may be tracking sales in one program, customer comments in another, and inventory data in yet another place, causing analysis of combined data with mixed sources to be a difficult logistics problem in the best case. Finding a solution to do the clean and merge efficiently is important.

This where the automated capabilities of Power Query can provide value:

• Consistency: your merged and cleaned data is reusable without having to run a manual process.

• Speed: your automated process will run on a calendar or on demand refresh/select.

• Accuracy: you reduce the potential for human error to ensure it has been cleaned and makes up trustworthy reports.

• Scales: you will be able to easily scale your documented reporting if you have gone through the effort to include other data sources as your organization's needs grow.

Connecting Multiple Data Sources

There are many data connectors available in Power Query, examples include:

• Excel and CSV files

• SQL Server, MySQL, PostgreSQL

• SharePoint lists

• REST and Web API

• Azure services (Blob, Data Lake, Synapse)

• Cloud such as Google Analytics, Salesforce, etc.

Once you connect in Power Query, the interface lets you preview your data, filter, sort, rename, and whatever else you would like to do to the data without ever affecting the original source. So if your organization has thousands of lines of historical customer information in Excel and transaction history in SQL Power Query can extract, clean the mismatched rows, merge the two sets of data, and display as a cohesive table.

Cleaning Datasets with Power Query

Cleaning data means fixing or removing inaccurate records, nulls, duplicate rows, formatting inconsistencies, and extreme values. Power Query has several features to help with this:

1. Remove Duplicates and Errors

Power Query identifies rows or values that are duplicates or erroneous and can also remove duplicates using the original ones.

2. Filter and Replace Values

You can filter rows you do not need or replace incorrect entries on the text file (e.g. changing a misspelled category name).

3. Change Data Types

You can change information from text to a number, dates to dates, or put fields into categories for better optimization on the models.

4. Split and Merge Columns

Split names into first and last, or merge city and state fields—these transformations give your data consistency for modeling.

5. Trim and Clean Text

You can trim and clean characters or spaces from strings—this is especially helpful for data imported from Excel or CSV files.

These cleaning tests as applied steps means that, every time data gets refreshed, the same transformations taken will be done automatically.

Merging Multiple Datasets

Once datasets have been cleaned, they need to be generally merged or appended into a coordinated data model.

- Merging Queries

Merging Queries will join two tables together using a common value in each (such as Customer ID, or Product Code). It supports Left Join, Right Join, Full Outer Join, Inner Join and is functionally sufficient to meet any join needs you will come across.

- Appending Queries

If you are using four different Excel files from four different departments, or four different months, you would want to stack your queries into one master dataset.

Use case: A regional manager is getting four different sales reports every month, from four different branches, all in their own Excel file. Instead of manually copying it all into one sheet, Power Query can automatically append every file in a folder, clean up differences, and you can refresh the data with one click.

Complete Automation

Once your query steps have been completed:

1. Save your Power BI file

2. Use Power BI Service to schedule regular data refreshes (i.e., every day at 9 a.m. for new sales data)

3. If your sources are Excel or SQL and hosted on premise, you need to connect a data gateway.

4. Then you can define refresh intervals (once a day at 9 a.m.), and you can set alerts if any steps fail.

Your analysts will then be free to interpret data and build spreadsheet free analytical products, rather than waste their time preparing endless information.

Power Query Real-World Examples

• FInance: Merging multiple general ledgers and bank account transactions from different systems

• Sales & Marketing: Combining customer relationship management (CRM) data with campaign data

• Operations: Aggregating and monitoring inventory and logistic data in real-time

• Human Resources (HR): Cleaning and merging records of employee performance or employee attendance

These real-life use cases are more important in the professional environment. That’s the reason that so many people that currently work, decide to obtain Professional Power BI Training in Pune, which teach these real-life guided scenarios in a practical method.

Advantages of Learning Power Query

• Decreases the amount of manual reporting needed

• Detects business users to self-serve their own data

• Improves report performance and accuracy

• Provides a good base for Power BI Advanced modeling

If you want to become job-ready and obtain these skills, joining a Power Bi Course in Pune is a great idea! A lot of these programs have live-projects, modules run by subject matter experts and real-life Case Studies to practice building complex ETL workflows using Power Query.

Plus, if you’ve considered career change or upskilling, Power Bi Classes in Pune will be a fantastic place to immerse yourself in learning Power Query with access to titles such as Tabular Editor, DAX Studio etc.

Conclusion

Power Query is a game changer for data professionals looking to automate the cleaning and merging of data from various sources. The amount of connectors it supports, user-friendly UI, and powerful transform functions allows users to save time, errors, and create dynamic reporting to support decision-making.

By automating their data preparation through Power Query, analysts will not only increase their efficiency, but also have data of the same quality in all of their reports. Whether you are an aspiring analyst or seasoned data professional, you will want to incorporate Power Query into your repertoire to fully utilize the capabilities of Power BI.

If you're in the business of being able to utilize these products and make your data workflows that much better, now is the time to explore your hands-on learning opportunities with certified Power BI training programs.