Quick Answer: What is a data profiling tool? #
A data profiling tool helps you analyze the structure, content, and quality of your data by scanning datasets and generating summary statistics. These tools surface insights like data types, value distributions, null counts, outliers, and inconsistencies — helping you quickly assess whether the data is complete, accurate, and fit for your intended use.
Data profiling is often a foundational step in data quality, governance, and analytics workflows. It gives teams an empirical view of the data at hand and helps spot inconsistencies early before they cause downstream issues.
Up next, let’s explore the key differences between data profiling and data quality, followed by an overview of the most popular data profiling tools, both open-source and proprietary, in the market. We’ll also look at the role of a metadata control plane in enabling data profiling for your entire data estate.
Table of contents #
- Data profiling tools explained
- What are some of the popular data profiling tools?
- Why do most data profiling tools fall short?
- How can a metadata control plane like Atlan help with data profiling?
- Data profiling tools: Summing up
- Data profiling tools: Frequently asked questions (FAQs)
Data profiling tools explained #
Data profiling tools generate a snapshot of your data by profiling columns, detecting data types, frequency distributions, and outliers. Data profiling tools are essential for assessing whether data meets the standards required for business, analytics, or regulatory use.
Gartner explains that data profiling tools provide data statistics, such as degree of duplication and ratios of attribute values, both in tabular and graphical formats. They profile data by analyzing one or multiple data sources and collecting metadata that shows the condition of the data, enabling the data steward to investigate the origin of data errors.
As mentioned earlier, data profiling is a vital part of data quality. Gartner’s Magic Quadrant for Augmented Data Quality Solutions highlights data profiling as a core capability, as it gives business users insight into data quality and helps them spot data quality issues.
So, before looking at the most popular data profiling tools, let’s briefly recap a few core concepts and understand the difference between data profiling and data quality.
What is data profiling? #
Data profiling is the process of gathering basic structural and characteristic information about data in a data asset. Gartner defines it as a technology for ‘discovering and investigating data quality issues.’
In the database world, a DESCRIBE keyword will give you the structural information.
Meanwhile, in the world of Pandas data analysis, you can get the statistical information, such as mean, standard deviation, maximum value, minimum value, percentiles, among other things, by using the describe() or df.info() function about the column values in a data asset.
If you’re running Spark SQL, you can run the statement ANALYZE TABLE table_name COMPUTE STATISTICS FOR ALL COLUMNS followed by a SQL query that uses built-in functions to generate a similar output. Other databases have similar methods to profile data from data assets.
Lately, the term ‘data profiling’ has become an overloaded term that encompasses more than just profiling, including concepts such as data quality, lineage, relationships, governance, and others.
Read more → What is data profiling?
How is data profiling different from data quality? #
Expanding on the fundamental definition, data profiling is a process that helps you understand the structure and characteristics of data, while also allowing you to discover issues and anomalies with data quality and integrity. The process of data profiling typically yields a detailed statistical report and summary of the data.
Data quality, on the other hand, focuses on testing and validating predefined rules. While some tools and libraries focus solely on data profiling, an increasing number of data quality tools, data catalogs, and other tools now feature native data profiling capabilities.
What are the benefits of data profiling? #
Data profiling is an activity that yields an output that supports a wide range of use cases across an organization, from business users to data scientists. Let’s take a look at the things data profiling helps with:
- For data scientists and machine learning engineers, it helps with exploratory data analysis, which uses descriptive statistics, univariate, and multivariate profiling, among other things.
- For data engineers and analysts, it helps with data asset discovery and data quality monitoring and assessment. Data profiling can also help with proactive anomaly detection based on time-based drift in profiling statistics.
- For business users, data profile statistics become an important part of the discovery, semantics, and overall understanding of the data assets.
With that in mind, let’s take a look at some of the popular data profiling tools.
What are some of the popular data profiling tools? #
The most popular data profiling tools include:
- Monte Carlo
- DataCleaner
- YData Profiling
- GX Cloud
- Data Ladder
- Soda Cloud
- Lakehouse Monitoring
Let’s get a brief overview on each data profiling tool.
Monte Carlo #
Monte Carlo is a data quality monitoring and observability platform that offers data profiling features. It supports profiling for columns with categorical, boolean, numeric, time, and list data structures.
You can run profiles based on a sample set of data, or you can apply a time-based filter. The profile summary includes statistical metrics such as count, percentage of unique values, percentage of null values, and other relevant information.
For more advanced profiling use cases, you can also create regex-based profiles.
To profile all the data assets across your organization’s data estate, Monte Carlo integrates with Atlan. With this integration, you can see trust signals from data profiles and other data quality metrics within Atlan’s user interface.
Check out what Atlan crawls from Monte Carlo to understand how you use this integration with your data stack.
DataCleaner #
DataCleaner is an open-source data profiling and cleansing tool that assists with data quality analysis by providing automatic exploratory data analysis capabilities.
With DataCleaner, you can connect to various data source systems and create a profile, which will show you metrics and statistics around the number of records analyzed (sample data). This will also show statistics on completeness, uniqueness, distribution, and missing values, among other things, for every column of the data asset.
Although DataCleaner is still actively maintained, there has been only one release in the last three years. It also uses a now-retired Apache project called MetaModel as a gateway to connect to various data stores. This can be challenging if you are using a data stack that has some of the latest data sources that are not supported by Apache MetaModel.
YData Profiling #
YData Profiling, earlier known as Pandas Profiling, is a Python library that you can use with Pandas and Spark.
The library is designed to serve the needs of data science and ML-type development and workloads, where data analysts and scientists require advanced profiling of data assets. These activities include univariate profiling, multivariate profiling, identifying missing data, and detecting outliers, among others.
YData Profiling is known for its ease of use, as it requires only a single line of code to be injected into your Python code. Some of the higher-order features it provides as a result of the profiling are data asset comparison, time-series data profiling, PII (personally identifiable information) handling, and sensitive data management.
Take a look at a few of the examples of the profiling report YData generates to better understand its applications.
GX Cloud #
GX Cloud (Great Expectations) is an Expectation-based end-to-end data quality solution for your data estate that runs in the cloud.
When you connect your data sources to GX Cloud for writing data tests, performing data quality checks, and validating data assets, GX Cloud automatically fetches the structural information about the data assets.
It also allows you to opt in to an easy-to-use one-click approach to fetch the data asset profile, which includes detailed descriptive statistics and statistical summaries of the data asset. GX Cloud then uses the output of the data asset profile to make suggestions on which Expectations you should create for a given data asset.
GX Cloud is powered by a Python library called GX Core, which you can use to define expressive and customized unit tests for your data.
Data Ladder #
Data Ladder has a product called DataMatch Enterprise (DME) that enables you to perform data integration, linking, profiling, cleansing, and matching on your data.
DME’s data profiling feature enables you to identify missing data, assess data structures, determine uniqueness and completeness, and display extensive descriptive statistics for every column in the data asset. DME also offers a range of advanced data profiling features, including pattern recognition, anomaly detection, and others.
DME is a holistic enterprise-grade solution for data matching, deduplication, and master data management. It might not be suitable for you if you only have lightweight data profiling requirements and you’re using other tools for data cleansing and data quality management. Check out more about DME’s latest releases here.
Soda Cloud #
Soda is a data quality monitoring tool that has data profiling capabilities.
Soda allows you to automatically profile columns when you first add data assets to it. The profile information includes descriptive statistics, such as mean, maximum, and minimum values, as well as statistics on missing data for the columns in the data asset. This profiling information intends to help you define the data quality checks you want to write in Soda.
You can get profiling information for numeric, text, and date-time columns. On top of giving you the most common descriptive statistics like variance, standard deviation, etc., Soda also gives you the five smallest, largest, and most frequent values, wherever it makes sense.
To provide you with a more comprehensive picture of the profiling information across your data estate, Soda Cloud also integrates with Atlan. You can leverage Atlan Playbooks for automating data profiling. Learn more about the Soda + Atlan integration here.
Lakehouse Monitoring #
Lakehouse Monitoring is an extension to the Databricks platform, which, among other things, allows you to get quantitative measures to track the quality and consistency of your data.
The profiling information provides a statistical distribution and structural information of the columns in a data asset. Lakehouse Monitoring also helps detect any anomalies or outliers by automatically establishing a baseline. The profiling features of Lakehouse Monitoring are geared more towards data science and ML engineers.
The profile information is stored in a location that you specify. The information is accessible using a SQL query on top of the report generated by Lakehouse Monitoring. One of the tables created contains the profile metrics, while another captures drift metrics, which help compare aggregates and statistics for the same data asset across a time window. This helps assess if something has changed in terms of data quality and integrity from the source.
Check out the Lakehouse Monitoring documentation to learn more.
Why do most data profiling tools fall short? #
These are some of the popular data profiling tools that can work with parts of your data stack. However, most of them lack visibility or access to all of your data estate, which is why it is challenging to obtain a comprehensive data profiling picture of your organization’s data assets.
This is where the need arises for a central location where all your data assets are cataloged. Without unified metadata, it’s hard to connect profiling metrics with governance, lineage, or trust frameworks.
A metadata control plane solves this by consolidating metadata from your entire stack, providing context-aware profiling, lineage, classification, and policy enforcement in one place. In the next section, let’s see a metadata control plane in action.
How can a metadata control plane like Atlan help with data profiling? #
Data profiling is an activity that is all about metadata, as it aims to understand the structural and statistical information about a given data asset.
Atlan is a platform built on a metadata control plane foundation, providing access to metadata for all your data estate. Atlan activates this metadata by leveraging it for automation across a variety of use cases, such as data cataloging, discovery, governance, lineage, and, last but not least, data profiling.
Atlan has several built-in features for data profiling, and in addition, also integrates with various tools like Soda, Monte Carlo, dbt, and Anomalo, bringing all data quality monitoring and profiling information under one roof. Some of the data profiling metrics tracked by Atlan are distinct count, missing count, minimum and maximum values, standard deviation, variance, among others.
Using Atlan, you can automate data profiling using Atlan Playbooks across a range of data sources, including Databricks, Snowflake, PostgreSQL, MySQL, Trino, Redshift, and Athena, among others.
Learn more about Atlan’s data profiling and other capabilities in the official documentation.
Data profiling tools: Summing up #
Data profiling is a crucial activity that enhances data quality and facilitates data discovery within an organization. It enables data analysts, engineers, and scientists to understand the size, shape, distribution, and form of the data in any data asset.
To efficiently profile data, however, you need a wealth of metadata, which can be provided in a single place only by a unified control plane for metadata that connects to all of your organization’s data systems.
Moreover, you need a platform that can leverage this metadata to activate various use cases, not just data profiling, but also cataloging, governance, and discovery, among other things.
Atlan is a metadata activation platform that is built on the foundation of the metadata control plane. It comes with its built-in features, but also integrates with other tools that help with data profiling. By investing in a platform like Atlan that supports metadata activation and ecosystem integration, data leaders can create a shared language of trust across their organization.
Data profiling tools: Frequently asked questions (FAQs) #
1. What is data profiling? #
Data profiling is the process of gathering structural information and descriptive statistics about data assets to gain an understanding of the size, shape, and form of the data. Examining the data profile of a data asset helps identify potential issues with the data and determine what tests and quality checks are necessary to track and maintain data quality.
2. What are the use cases for data profiling? #
Data analysts and data scientists utilize data profiling at the outset of exploratory data analysis. Before delving into the data, it is essential to understand the basic structure, distribution, and descriptive statistics of the dataset.
For data engineers, the use case is slightly different. Data engineers use data profiling metrics to identify data quality tests and validations that should be implemented to ensure high-quality data for a data asset.
3. What is a data profiling tool and why does it matter? #
A data profiling tool scans your datasets and summarizes their structure, values, and quality. It helps you understand if your data is complete, consistent, and ready for analytics or AI. Profiling is often the first step in any data quality, governance, or ML workflow.
4. How is data profiling different from data quality checks? #
Data profiling helps you understand your data by surfacing statistics like null values, outliers, and duplicates. Data quality tools go a step further — they validate the data against predefined rules, alert on anomalies, and support remediation workflows.
5. What are some common data profiling metrics that data profiling tools should track? #
Some of the common profiling metrics count and percentages of the following: empty string values, null values, and unique values. Other metrics depend on the data type of the column being profiled.
For instance, for a datetime column, you might want to see the maximum date time and the minimum date time, or the date time range. Similarly, for a text field, you may want to determine the percentage of values that fall outside an enumerated or expected values list, indicating the data quality of that column.
6. How often should data be profiled? #
It depends on the use case. For static reference data, one-time profiling may suffice. For dynamic or high-value data (e.g., fraud features, healthcare dashboards), profiling should be continuous or triggered by data updates to catch drift and anomalies early.
7. How do I choose the right data profiling tool for my stack? #
Look for tools that integrate with your core platforms (like Snowflake, Databricks, or Postgres), support automated and continuous profiling, and surface metrics in a format accessible to both technical and business users. Metadata integration and role-based usability are also key.
8. What role does metadata play in data profiling? #
Metadata provides the context behind profiling results, such as where the data came from, who owns it, and what it powers. Tools that embed profiling within a metadata control plane let you trace anomalies to their source, track impact, and enforce quality more effectively.