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SQL vs. Power Query: Which Is the Better Database Tool?

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Power Query or SQL

Power Query or SQL

This article will compare two major database software tools: Power Query and SQL. We will focus on various use cases as well as the similarities and differences between the two. If you are trying to make sense of which one is worth using, you should give this comparison a read.

SQLPower Query

Learning Curve

Medium. Requires understanding of tables, queries and data.

Requires understanding of Power BI and its outcomes.


Requires database server and data-lake.

Requires external data sources.

User Base

Large developer community, database admin community.

Specific to the Power BI community.

Programming Support

SQL is the query language and also supported by other external languages.

It can be custom coded and extended with M language.

Data Operations

You can perform CREATE, READ, UPDATE and DELETE.

You can perform data transformations on already processed data.

Custom Code

You can custom code stored procedures and routines to automate.

You can write custom code using the power query to perform specific tasks.

Data Source

Database Server

Various data sources (files, services, database) etc.

External Dependency

SQL requires a lot of tools for you for reporting, visualization, query and storage.

Power query is specific to a tool so it is already dependent on single tool.


Every step of the way you would require permission to access the data.

You take permission only when you are working on the data.


Large and requires much understanding of data and database.

Limited to the tool.

Refresh Times

Initially slow, once cached lot easier.

Lot quicker as data is specifically queried by the tool.

Shared Datasets

You can share the data, reports and other queries using tools.

You share what you are using as a data source. You have limited access.

Power Query: Advantages and Disadvantages

Power Query is a data preparation engine run by the Power BI tool. This engine allows the tool to transform the data and also connect the data with the various data sources out there. e.g. MySQL, CSV Files, Snowflake, etc.

Power Query allows you to connect with various sources that are out there—for example, files, databases, services, feeds, data lakes, etc. With it you can connect your desktop and a cloud service with all of those available data sources.

Some of the data sources may require external connectors. These connectors understand what it takes to connect the available data source with the Power BI tool, which makes it easier to process the data further and transform it as required.

Once the data is taken into the tool, further manipulation using the query, transformation and the manipulation becomes easier. You set up a recurring process for accessing the data, transforming it, which is a part of the tool and the engine in general.


  • You can't use parameters passing while working with big data.
  • Limited cells that can be previewed.
  • Limited data size processed in buffer (tool limitation).
  • Performance issues

SQL: Advantages and Disadvantages

SQL is a standardized programming language that makes it easier for you to design, process and manipulate data from the database server. You can literally do anything you want with the data and also get much quicker and faster performance from your queries.

It's lot faster in terms of performance than Power Query. It's transformations are lot quicker than the Power Query.

Reporting with SQL can be done per database basis. Each of them have different tools and methods to accomplish the much better report performance. SQL is lot better in this regard—be it auto reporting or machine learning based reports.

SQL can be used with many other external tools, which makes it easy for you to do analysis. There are, however, some database engines that lack the tools specific to the analysis and the predictive nature of the data. For that you'd find third-party external tools, which makes the use of the Power BI more useful over traditional SQL query.


  • Steep learning curve of SQL language.
  • Simplicity of single tool like Power BI for use case.
  • Not suitable for less technically inclined users.
  • Requires external libraries and engines for predictive and prescriptive analysis.

SQL vs. PowerQuery: Which is Better?

SQL and Power Query both have their place when it comes to data science and business intelligence (BI). While SQL requires a lot of work in order to get results, its performance and control is unmatched for the Power BI.

Power Query is more useful only in the context of a less technically-inclined user wanting to analyze the data, numbers and get reports and conclusions quickly.

Try both and choose the tool most useful for you.

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

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