Power Query in Excel & Power BI: The Ultimate Data Transformer

Power Query is Microsoft’s powerhouse tool for data transformation and automation, built into Excel and Power BI. If you’ve ever spent hours copy-pasting and cleaning data manually (and let’s face it, most of us have), Power Query is here to save the day. This blog post will take a casual yet detailed dive into Power Query, its benefits, and how to use it, from basic to advanced scenarios. We’ll walk through practical examples with sales, trade, and technical data, including some Power Query M code snippets (the formula language behind Power Query in Excel & Power BI: The Ultimate Data Transformer). We'll even throw a few screenshots of the Power Query Editor in action. By the end, whether you’re a beginner or a pro, you’ll have a solid grasp of how to turbocharge your data tasks with Power Query.

What is Power Query (and Why Should You Care)?

Power Query is a data transformation and preparation engine, equipped with a friendly interface for getting data from various sources and shaping it to your needs. It’s available in Excel (you might know it as Get & Transform on the Data tab) and in Power BI Desktop’s Power Query Editor. In a nutshell, Power Query lets you connect to data, transform and clean it, combine data from different sources, and then load the refined data where you need it. This process is often called ETL (Extract, Transform, Load).

Why use it? Because it can drastically cut down the time you spend wrestling with messy data. In fact, business users often spend up to 80% of their time just preparing data (cleaning, reshaping, fixing issues) which delays actual analysis. Power Query tackles this pain by letting you build a repeatable data-cleaning process that you can refresh with one click whenever your data updates. No more manual re-cleaning of new data exports every week! It also provides a huge library of transformations (over 350 functions) and connectors to hundreds of sources, so you can work with almost any data in any shape.

To put it simply, Power Query is like a magic kitchen for data: you bring in raw ingredients (raw data from files, databases, etc.), follow a recipe of steps to chop and cook (transform the data by filtering, pivoting, merging, etc.), and then serve the finished dish (load the cleaned data into Excel or Power BI). And the best part – the recipe is saved so you can re-use it anytime on new batches of raw data.

Logo from Power Query principle

Key phases of using Power Query

1. Connect—Connect to one or more data sources (Excel files, CSVs, databases, online services, etc.).

2. Transform – Clean and shape the data (e.g. remove unwanted columns, filter rows, change data types, split or merge columns) while leaving the source data untouched.

3. Combine – If needed, combine data from multiple sources, such as merging queries (joining tables) or appending queries (stacking datasets). This gives you a unified view of your data.

4. Load – Once the data is shaped to your liking, load it into Excel (as a table or into the data model) or Power BI’s model. You can then build pivot tables, charts, or reports on it. Whenever the source data updates, hit Refresh to rerun all the steps and update the final output.

These phases make Power Query a game-changer for automation: you define your query steps once, and then you can refresh repeatedly to get up-to-date data without repeating manual work. It’s useful for everything from one-off data cleanup tasks to robust, automated data pipelines feeding your dashboards.

Getting Started: Power Query in Excel and Power BI

Getting started with Power Query is easy because it’s already built into Excel and Power BI. You’ll find it under the Data tab in Excel's Get & Transform Data section. For example, you can click Get Data -> From File -> From Excel Workbook (or CSV, database, etc.) to import data, then choose Transform Data to open it in the Power Query Editor. In Power BI Desktop, you launch Power Query by clicking the Transform Data button on the Home ribbon (this opens the Power Query Editor window).

Power Query Editor Interface: Once you’re in the Power Query Editor, you’ll see a user-friendly interface with a few key parts:

 The Power Query Editor window in Power BI Desktop (and similarly in Excel) has a ribbon of transformation commands (top), a Queries pane listing all queries (left side, labeled 1), the data Preview of the selected query (center, labeled 3), and the Query Settings pane (right, labeled 4) which includes the name, properties, and most importantly the Applied Steps for that query. Each transformation you apply (like a filter or column rename) is recorded as a step in the Applied Steps list. You can click on these steps to see the data state at that step, reorder or delete steps, and rename steps for clarity. This visual step-by-step history is one of Power Query’s superpowers – it’s like having an automatic macro recorded for your data transformations.

The Power Query editor is the same in both Excel & Power BI

Notice above the data preview, there’s a formula bar showing the M code for the selected step. Power Query automatically generates this M code in the background as you click around and apply transformations. You don’t have to write code to use Power Query, but you can view or tweak the code in the Advanced Editor if you want to get more technical. (For pro users, the Advanced Editor is accessible from the Home tab and lets you see the full script of your query. It’s great for learning the M language or fine-tuning complex transformations.)

Tip: Power Query’s interface is mostly the same as that of Excel and Power BI. So skills learned in one carry over to the other. The main difference is where the data goes after you Close & Load: in Excel, you might load to a table in a worksheet or the data model, whereas in Power BI, the data always goes into the data model for building visuals.

Now that we know where to find Power Query and what the editor looks like, let’s start doing some actual transformations!

Power Query Basics: Cleaning and Shaping Data

In its basic usage, Power Query allows you to perform all sorts of data cleaning and shaping tasks with just a few clicks. Many tasks that would be tedious in Excel (or require writing formulas/VBA) are dead simple with Power Query. Here are some everyday things you can do in the Power Query Editor:

• Filter rows – Remove unwanted rows or keep only those that meet specific criteria (e.g. filter out nulls or keep rows where Region = "EU").

• Sort or Rename columns – Sort your data or rename columns to friendly names.

• Remove or Choose columns—You can remove columns you don’t need or select only those you need (this helps reduce data clutter).

• Change data types – Ensure a column is the correct data type (text, number, date, etc.) for proper analysis. For example, convert a text date column to an actual Date type.

• Split or Merge columns – Split one column into multiple (e.g. split “Full Name” into First and Last) or merge multiple columns into one (e.g. combine Year, Month into a single date string).

• Add calculated columns – Create new columns based on formulas (Power Query has a rich library of functions). For instance, add a column computing Profit = Sales – Cost.

• Remove duplicates – Easily remove duplicate rows based on one or more columns.

• Pivot or Unpivot data – (More on this later) Turn rows into columns or vice versa to reshape how your data is organized.
  

Most of these transformations can be done via the ribbon or right-click menus. Power Query will apply the step and show you the result instantly in the preview. If it’s not right, you can tweak or remove the step.

Let’s look at a simple example. Suppose we have a sales table and want to filter out any transactions where the Sales amount is below 1000 (maybe those are small deals we don’t need in our summary). In Power Query Editor, we would: click the filter drop-down on the Sales column, set a number filter for greater than or equal to 1000, and press OK. That’s it – the table preview now shows only the rows with Sales ≥ 1000, and a new Filtered Rows step appears in Applied Steps.

Under the hood, this generated an M formula using the Table.SelectRows function, which we can see in the formula bar or Advanced Editor. It looks like this:

#"Filtered Rows" = Table.SelectRows(#"PreviousStep", each [Sales] >= 1000)

Here, #"PreviousStep" refers to the step before the filter (Power Query auto-names it based on the last action, e.g., an import or change type step), and each [Sales] >= 1000 is the condition to keep rows. You don’t need to write this code manually—it was created for you when you set up the filter through the UI—but it’s nice to know what’s happening behind the scenes.

This idea extends to all fundamental transformations: click the UI option, and Power Query writes the M code for that step. You can then continue applying more steps. For example, after filtering, you might remove a few unnecessary columns and change a data type from text to number; each action becomes a new line in your query’s script and a new step in the Applied Steps.

By stacking these simple transformations, you can gradually shape your data. Even at this basic level, you’re achieving a lot:

• Automation – The steps will replay on refresh, so if you get a new batch of data tomorrow, refresh it, and all the filters, removals, etc., will apply again automatically.

• No coding required – You’re using a friendly interface, but still getting the robustness of a scripted transformation (which you can always inspect or modify).

• Undo/Adjust anytime—Because steps are recorded, you can edit a previous step (say, change the filter threshold to 500), and the subsequent steps will update accordingly. It’s like non-destructive editing for your data.

Now that we’ve covered the basics, let’s move into more advanced scenarios and transformations that showcase Power Query's power.

Advanced Transformations with Power Query

Power Query truly shines when you tackle more advanced data challenges. Here we’ll explore a few techniques — merging queries, pivoting data, and using parameters — that are standard in real-world data projects. These correspond to more complex tasks you might have faced: e.g. combining two tables (instead of doing a bunch of VLOOKUPs in Excel), reshaping data layouts, or making your queries dynamic. Don’t worry, we’ll break each one down with an example.

Power Query’s engine has built-in functions for all of these. Microsoft lists merge, append, group by, pivot, and unpivot among its advanced transformation options. Let’s look at how to use a few of these in practice.

Merging Queries (Joining Tables)

Merging in Power Query is equivalent to doing a JOIN between two tables in a database, or a lookup (like VLOOKUP) in Excel. It allows you to combine two datasets based on a common field. This is super useful in scenarios like a Sales table and a Products table, and you want to bring product names from the Products table into the Sales table (matching by Product ID). Or, say you have a trade transactions list and want to merge it with a table of exchange rates to add a currency conversion — any time you need to pull in columns from another table based on matching keys, merging is your friend.

In Power Query, you need to have both tables/data sources loaded as queries (they can be from different sources, e.g. one from a CSV, another from a database – doesn’t matter). Then you perform a Merge operation:

1. Select the primary query (e.g. Sales) in the Power Query Editor. On the Home tab, click Merge Queries (or Merge Queries as New if you prefer to create a new combined query without altering the originals).

2. The Merge dialog will pop up. Choose the second query (e.g., Products) to merge with.

3. Select the matching column in each table. For example, click the ProductID column in the Sales table preview (top part of the dialog) and the ProductID column in the Products table (bottom part of dialog).

4. Select the Join Kind (type of join). The default is Left Outer (keep all rows from the first table and only match ones from the second), like Excel’s VLOOKUP, which holds all sales and brings product info where available. Depending on your scenario, you can choose other join types (Right, Inner, Full, Anti joins).

5. Click OK. Power Query will add a new column to the Sales query—a structured column containing the matching rows from the Products table. (If there was a match, you’ll see a record or table; if no match, it might show null, depending on the join type.)

6. To bring in the actual columns from Products, click the expand icon (the small double-arrow icon at the top of the new column). Select which fields from the Products table you want to add (e.g. ProductName, Category, etc.), and uncheck “Use original column name as prefix” for cleaner column names. Press OK. Now those columns are expanded into the Sales table.

7. Rename the new columns if needed. You’ve successfully merged the two queries into one! The Sales query now has additional columns from the Products lookup.

The Merge dialog in Power Query joins a Sales table (top) with a Countries table (bottom) on a matching CountryID field. The join kind selected is Left Outer, which will keep all rows from Sales and bring in data from Countries when IDs match. The preview at the bottom shows a green check indicating that all four rows in Sales found a match in Countries.

After the merge, Power Query generates a couple of steps in the M code. First is a Table.NestedJoin step that performs the join, and then a Table.ExpandTableColumn step to expand the results. Here’s what the M code might look like for a merge example (merging a Sales table with a Country table by CountryID):

// Assume we already have Source (Sales table) and Countries table loaded

#"Merged Queries" = Table.NestedJoin(Source, {"CountryID"}, Countries, {"CountryID"}, "CountryData", JoinKind.LeftOuter),

#"Expanded CountryData" = Table.ExpandTableColumn(#"Merged Queries", "CountryData", {"CountryName", "Region"}, {"CountryName", "Region"})

Let’s break that down: Table.NestedJoin(Source, {"CountryID"}, Countries, {"CountryID"}, "CountryData", JoinKind.LeftOuter) takes the Source (our Sales table), and joins it with Countries matching on the CountryID column from each. It creates a new column called "CountryData" with matching rows from the Countries query—following, Table.ExpandTableColumn(... "CountryData", {"CountryName", "Region"}, {...}) expands that "CountryData" column, pulling out the CountryName and Region fields from it into the main table. After this, our Sales data has those two new columns (CountryName, Region).

From a user perspective, you didn’t have to write any of that code – you just went through the dialog and selected columns. But it’s great to see how Power Query formalizes it, especially if you want to troubleshoot or do more advanced tweaks.

Real-world example (Sales/Trade data): Imagine you have a Sales Transactions CSV with a column for CountryCode, and a separate Country Info table mapping country codes to country names and regions. Using Merge in Power Query, you can get the country names into the transactions table in minutes. Or for trade data, perhaps you merge a Shipments table with a Tariff Rates table by a product category to calculate fees. Combining data from different sources in a repeatable query is extremely powerful.

Pivoting and Unpivoting Data

Have you ever had data oriented the “wrong” way for your analysis? For example, you might receive a table where each column is a month (Jan, Feb, Mar, …) and each row is a product – but you need to analyze it in a normalized way (e.g. a row per transaction or a row per month). Or vice versa: you have a very detailed transactional table (with a row for every product * every month) and you want to create a summary table where each product is one row and you have separate columns for Jan, Feb, Mar sales, etc. This is where Pivoting and Unpivoting come into play.

• Unpivot (also called flattening or melting data) takes columns and turns them into rows. Use this when you have data spread across columns that represent variable values. For example, if you have columns for Jan, Feb, and Mar, unpivoting them would create a new Month column and a corresponding Value column, turning your data into a row for each month. This is great for normalizing cross-tab reports into a tabular format.

• Pivot (the opposite of unpivot) takes rows and turns them into columns, aggregating along the way. Use this when you want to summarize or cross-tab your data. For example, if you have a table of [Country, Date, Sales] where each row is a daily sales figure, you could pivot by Date to create columns for each date, resulting in one row per Country with total sales for that date in each column.

In Power Query, these transformations are straightforward:

• To unpivot columns: select the columns that are value columns and choose Unpivot Other Columns (or select the ones to unpivot and choose Unpivot Only Selected). The result will be two new columns (Attribute and Value by default) where Attribute contains the former column header (e.g. Month) and Value contains that column’s value for each row.

• To pivot columns: select the column with the values you want to become new columns. Click Pivot Column on the Transform tab. In the dialogue, choose the Value column that contains the values to fill into the pivoted columns, and select an aggregation if there are multiple rows for each pivot key (e.g. sum, count, etc.). Power Query will create new columns for each unique value in the pivot key column and place the aggregated values accordingly.

For example, consider a small dataset of Trade Volume that looks like this (before pivot):

Country	Month	Volume
USA	Jan	500
USA	Feb	450
USA	Mar	567
Canada	Jan	300
Canada	Feb	420
Canada	Mar	254
Panama	Jan	100
Panama	Feb	40
Panama	Mar	80

This data is “normalized” (each row is one country-month). If we pivot it to have one column per Month, we’d get a table with one row per Country and columns for Jan, Feb, Mar volumes:

Country	Jan	Feb	Mar
Canada	300	420	254
Panama	100	40	80
USA	500	450	567

We achieved that by pivoting the Month column, using Volume as the values and Sum as the aggregation (in this case, each country-month was unique, so the sum is trivial). In Power Query’s M, the pivot step might look like:

#"Pivoted Table" = Table.Pivot(#"PreviousStep", List.Distinct(#"PreviousStep"[Month]), "Month", "Volume", List.Sum)

Here, Table.Pivot took the table from the previous step, List.Distinct(...[Month]) provided the list of unique month values to turn into columns, "Month" is the pivot key column, "Volume" is the values to aggregate, and List.Sum tells it how to aggregate if there are multiple entries (summing in this case). After this step, our data is pivoted as shown above.

Likewise, an unpivot operation in M would use Table.UnpivotOtherColumns or Table.Unpivot functions. For instance, if we wanted to unpivot that pivoted table back into the three-row-per-country format, we could unpivot the month columns:

#"Unpivoted Columns" = Table.UnpivotOtherColumns(PivotedTable, {"Country"}, "Month", "Volume")

This says to take the PivotedTable, leave the Country column as-is, and unpivot all other columns into two columns named Month (holding the old column name) and Volume (having the value). We’d then get back the original list of country-month-volume rows.

When to use pivot vs unpivot? If your data is too “wide” (lots of columns that represent values, like survey responses or months or categories), you often unpivot to make it easier to analyze (especially in PivotTables or Power BI visuals). If your data is too “tall” and granular, you might pivot to create a summary view (though you could also use a PivotTable or DAX for summarizing, pivoting in Power Query reshapes the data itself).

Power Query makes these transforms easy and repeatable. For example, a technical data scenario: you have sensor readings with separate columns for each sensor – you could unpivot to feed a modeling tool that expects a “Sensor Name” and “Reading” column. Or conversely, if you have logs with entries and want a matrix of counts by category, you could pivot the category column to get a wide summary.

Using Parameters for Dynamic Queries

Parameters in Power Query are like user-defined variables that make your queries dynamic and reusable. By creating a parameter, you can easily change specific values (like a filter criterion, a file path, or a threshold) without editing the query steps manually. This is especially handy when you want to run the same data transformation for different cases – for example, import sales data for different regions based on a selected parameter, or filter a report for a specific date range without duplicating the query.

Creating a parameter: In the Power Query Editor (Excel or Power BI), you can create parameters via Manage Parameters -> New Parameter on the Home ribbon. You give it a name, data type, and a current value (you can also provide a list of allowed values or a range if you want). For instance, you might create a parameter called RegionChoice of type Text, and set its current value to "Europe".

Once you have a parameter, it will appear in the Queries pane (with a special parameter icon). You can then use it in your other queries. How? Pretty much anywhere a literal value could be used in M code, you can substitute your parameter. The UI also often allows you to reference parameters: for example, if you apply a Filter Rows step on a column, instead of typing a number or text to filter by, you can choose “Parameter” and pick your defined parameter as the filter value.

Let’s say we have a query SalesData that contains a column Region. We want to make the region filter dynamic using our RegionChoice parameter. We would filter the Region column by RegionChoice (which currently is "Europe"). In the Advanced Editor, that step might look like:

#"Filtered by Region" = Table.SelectRows(SalesData, each [Region] = RegionChoice)

The query returns only European sales because RegionChoice is a parameter (value "Europe"). If we change the parameter’s current value to "Asia" and refresh, the query will return Asian sales without us having to edit the filter step. This makes it super easy to reuse the same logic for different inputs.

Parameters can be used for more than just filters:

• Dynamic data source: e.g. a parameter for file path or URL, which you use in the Source step of a query. Changing the parameter can point your query at a different file or API endpoint. (This sometimes requires permissions for each new source, but is doable.)

• Thresholds: e.g. a parameter to control an outlier cutoff (maybe filter out values above a certain number), or to switch between different modes in a formula.

• What-if analysis: In Power BI, parameters (especially with the Dynamic M query parameters feature) can be tied to slicers, allowing end-users of a report to change the parameter via a slicer selection. This can trigger direct query changes. (That’s a more advanced topic, but worth mentioning for Power BI pros – it requires the data source to support query folding and DirectQuery mode.)

In Excel, one cool trick is that you can even link a Power Query parameter to a cell value. For instance, you could have a cell where a user types a region, and your Power Query uses that cell’s value as a parameter to fetch data for that region. This effectively lets you create a simple interactive report without writing any VBA.

Technical example: Suppose you maintain a log file of server events, and you have a query that processes this log (splits columns, filters errors, etc.). You could create a parameter for "Environment" (like Dev vs Prod) and use it to dynamically choose which log file (dev server log vs prod server log) to process or filter the log records by environment. Then, the same transformation logic applies to either environment’s data by toggling the parameter.

Overall, parameters add a layer of flexibility to Power Query. They make your queries more adaptable and easier to maintain. Instead of duplicating a query for each scenario, you parameterise one query and feed it different values.

Practical Examples: Sales, Trade, and Technical Data

Let’s quickly recap with a few real-world examples tying everything together. These examples demonstrate how Power Query can be applied to different domains of data:

• Sales Data Automation: Imagine you get monthly sales files for each region. With Power Query, you can connect to a folder of all those CSV files and append them into one big table (using Append Queries). You then apply transformations: clean up product names, merge in a Products lookup to get product categories, filter out any test or dummy sales records, and pivot the data to summarize sales by region and month. Once set up, this query can be refreshed each month — drop the new file into the folder and hit Refresh, and it will automatically pick it up. No more manually consolidating Excel files! It’s all done using the query (with easy-to-trace and audit) steps. The result could feed into a nice Excel dashboard or a Power BI report for the sales team.

• Trade & Finance Data: Think of trade data or financial transactions, often involving multiple systems. For example, you might pull trading data from System A and reference data from System B. Using Power Query, you can import both, merge them (maybe join on a trade ID or a date), and create a unified view. If you have exchange rates or currency conversion to apply, you could use a parameter to select the currency or even automatically fetch the latest rates via a web connector. You could also pivot data to analyze totals by trade type, or unpivot some report data to feed into a downstream calculation. All these transformations become refreshable processes. A specific case: a commodity trading report that combines shipment volumes (perhaps pivoted by commodity) with price data to calculate a profitability report – Power Query can gather, integrate, and prep all the necessary data so that your final Excel or Power BI does a simple calculation on the cleaned data.

• Technical Data & Logs: Suppose you’re an IT professional dealing with server logs or performance metrics. These often come in semi-structured text files or need noise filtering. With Power Query, you can connect to a log file (even multiple log files via Folder connector), use text transformations to parse the logs (split by spaces, extract date/time, etc.), filter down to only the relevant events (e.g. errors or specific event types), and maybe add columns to calculate time differences or categorize entries. If you have a parameter for environment or date, you can easily switch the analysis. The cleaned log data could then be loaded into Excel for an audit or into Power BI for visualization of system performance. Next time you get new log files, you replace the source or update the parameter and refresh – voila, the transformation pipeline runs again. This beats writing a one-off script each time.

These scenarios show how Power Query isn’t tied to a single type of data – whether it’s sales figures in Excel, trade data in databases, or text-based logs, the workflow of connecting, transforming, and loading remains consistent. You define what to do with the data, and Power Query handles how to do it repeatedly and efficiently.

Tips and Best Practices

Before we wrap up, here are some tips and best practices to help you get the most out of Power Query:

• Plan and Name Your Steps: Give your queries and steps meaningful names. Instead of “Changed Type” or “Custom1”, double-click and rename steps to things like “Filtered out Nulls” or “Added Year Column”. This makes the Applied Steps easier to understand when you or someone else reviews the query later.

• Filter Early: If you only need a subset of the data, apply filters as early as possible in your query. This reduces the data load and speeds up processing. For example, if you only need the last 12 months of data, filter the date in Power Query upfront. (Bonus: if your data source is a database or other capable source, Power Query might translate that filter into a query on the source — this is called query folding, which means only the needed data is pulled.)

• Remove Unused Columns: Remove any columns you don’t need as soon as possible. Less clutter and less data to carry through the pipeline. This can improve performance and clarity.

• Correct Data Types: Always check that your columns have the right data types (Power Query may assign types automatically, usually in a step called “Changed Type”). Correct types (number, date, etc.) ensure subsequent operations (like calculations or merges) work correctly. If a column is text but should be numeric, fix the type in Power Query rather than waiting to discover errors in your Excel formulas or visuals.

• Split Complex Logic into Steps: Don’t be afraid to break down transformations into multiple steps. Each step only transforms the data, but collectively, they achieve complex results. This stepwise approach makes debugging easier. You can always combine or simplify steps later if needed.

• Use Append vs. Merge Appropriately: Remember the difference—merge adds more columns by joining two tables, while Append adds more rows by stacking tables. Use merge for lookups (enriching data) and append for consolidating datasets (like union all). If you have many files (say, one per month), use Append to combine them.

• Beware of Excessive Steps on Large Data: While Power Query can handle relatively large datasets, try to perform only the necessary transformations, and be mindful of operations that might be expensive (like sorting huge data, merging on non-indexed fields, etc.). If working with extensive data, enable Data Load -> Enable Data Preview to Download in Background (or disable it) in options to control performance, and consider using filtering to preview a small sample while designing the query.

• Refresh and Test: Test the refresh with actual data updates after setting up a query. Ensure any new or unexpected values (e.g., new columns appearing or new categories) are handled (Power Query might throw errors if something changes in the source schema). You can add conditional handling or ensure your M code is robust for such changes.

• Document and Save: It’s a good practice to document tricky parts of your query. You can add a descriptive step (there’s a feature to insert a step that contains a note, or annotate in a separate document). Also, saving your work (Excel workbook or Power BI file) will save the queries. You can copy the M code (Advanced Editor contents) for backup or version control and save it as text.

• Learn the M Language (gradually): While you can do much without coding, learning some M can help in advanced scenarios. Knowing how to tweak a generated formula or write a simple if-else logic in a custom column can extend Power Query’s capabilities. The more you use the Query Editor, the more you’ll pick up M organically, since the formula bar shows each step’s code.

• Keep Queries Independent (if appropriate): If you create multiple queries, you can reference one query from another (for example, create a base query, then reference it in another to do additional transforms). This is fine, but remember that if you reference the base query, it might still load data even if you don’t use it directly. If it’s a staging query, consider disabling “Enable Load” for intermediate queries (right-click query -> uncheck Enable Load) so they don’t clutter your data model or workbook. This way, they act as helpers for other queries only.

By following these practices, you’ll make your Power Query solutions more efficient, maintainable, and less error-prone. Now you’re not just using Power Query – you’re mastering it!

Conclusion and Further Resources

Microsoft Power Query is a versatile tool that significantly simplifies data tasks. We started with connecting and cleaning data, then moved on to advanced transformations like merging and pivoting, all with a casual, hands-on perspective. By now, you should appreciate how Power Query can automate tedious workflows, whether you’re preparing sales reports, analyzing trade data, or parsing technical logs. The best way to learn is to try it out on your data. Start small, record those steps, and build up. You’ll find that what used to take hours of manual work can be refreshed in seconds with a well-crafted Power Query.

For further learning, check out the resources below. Happy querying!

• Official Documentation—Power Query Overview (Microsoft Learn)—Comprehensive docs on Power Query for Excel and Power BI, including how-to guides and concepts. It’s a great starting point for exploring specific topics (like connectors or dataflows).

• Microsoft Learn Module: Automate Data Cleaning with Power Query—This beginner-friendly online course teaches you how to use Power Query in Excel to connect, clean, and shape data, with hands-on labs and examples.

• Power BI Tutorial: Shape and Combine Data – A step-by-step tutorial on using Power Query in Power BI Desktop to import, transform, and combine data from multiple sources. Good for seeing practical examples of transformations (most techniques also apply to Excel).

• Power Query M Reference – Reference guide for the M formula language, listing all available functions (useful when writing or editing M code to do more advanced things that the UI might not cover directly).

• Community & Blogs – The Power Query tag in the Microsoft Tech Community and blogs by data experts (e.g., Ken Puls, Chris Webb) contain a wealth of tips, from beginner tricks to advanced hacks. These can be great for seeing how others solve real problems with Power Query.

  
  

You’ll quickly go from a Power Query novice to a data transformation ninja by leveraging these resources and practising regularly. Happy data crunching!