bg gradient

10 Best Data Profiling Tools For 2023: Every Developer Must Know

Data quality assessment is vital for data-driven organizations. By understanding the dataset, analysts and developers can draw valuable insights that help improve business processes. Data profiling is one of the most important techniques for data quality assessment.

Data profiling involves inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, improving data quality, and supporting effective decision-making. Data profiling can be used to answer questions such as:

  • What is the overall structure of the dataset?
  • What are the data types for each field?
  • Is there any invalid or missing values?
  • How are the different fields related to each other?
  • What are the most common values for each field?
  • What are the outliers?

Introduction to Data Profiling

Data profiling is the process of examining data sources to determine their structure, content, and quality. It helps you understand your data by providing summary information about the data attributes, such as field name, data type, length, and pattern.

Data profiling can be used for both structured and unstructured data. There are many tools available for data profiling. In this article, we will introduce 10 of the most popular data profiling tools. But before we start with the tools let us first understand the types of data profiling and the need for data profiling.

Types of Data Profiling

A vast array of data profiling activities can be performed, depending on the goals of the data profiling exercise. Data profiling involves understanding both the structure and content of data. The main types of data profiling are:

Structure discovery or structure analysis: 

This type of data profiling investigates the structure of data, such as length, data type, and pattern. It also looks for duplicate values and missing values. It helps in examining the overall data quality.

Content discovery or content analysis: 

Content analysis looks at the actual data values and tries to understand the meaning of the data. It also looks for patterns, trends, and relationships. This type of profiling is helpful in identifying business rules that can be used to improve data quality. It works by using statistical techniques to find patterns in data.

Relationship discovery: 

This type of data profiling looks at the relationship between different fields in a dataset. Relationship analysis is helpful in understanding the dependencies between different fields. It can also be used to find anomalies and inconsistencies in data.

Need for Data Profiling

Data profiling is an essential part of data quality assessment. By understanding the structure and content of data, analysts and developers can draw valuable insights that help improve business processes. Some of the benefits of data profiling are:

  • It helps in understanding the overall structure of the dataset.
  • It helps in identifying invalid or missing values.
  • It helps in finding relationships between different fields.
  • It can be used to generate business rules that can be used to improve data quality.
  • It helps in identifying patterns, trends, and anomalies in data.

These are just some of the reasons why data profiling is an essential part of data quality assessment.

Various approaches to Data Profiling

There are many different approaches to data profiling, but most of them can be classified into two broad categories: statistical analysis and data mining. Statistical analysis is a process of exploring, modeling, and analyzing data to extract useful information. Data mining is a process of automatically discovering patterns and relationships in data.

Both statistical analysis and data mining can be used to answer the questions listed above, but they have different strengths and weaknesses. For example, statistical analysis is good at finding relationships between variables, but it can be slow and computationally intensive. 

Data mining, on the other hand, is good at finding patterns in data, but it can be difficult to interpret the results. The best approach to data profiling depends on the specific needs of the organization. In most cases, a combination of both statistical analysis and data mining will yield the best results.

Common Data Profiling Tools

There are many different tools available for data profiling. Some of the most common ones are listed below.

1. Boltic

Boltic enables you to quickly understand your data. It is designed to help you profile both structured and unstructured data. Boltic can be used to profile data from a variety of sources, including databases, files, and web services. It supports various file formats, including CSV, JSON, XML, and more. With Boltic, you can easily assess the quality of your data, and make corrections where necessary. 

Our platform was designed with data quality in mind, and our algorithms are constantly being updated to ensure that you always have the most accurate data possible. Using  Boltic can help you improve the quality of your data, and make better decisions about how to use it. We are constantly adding new features and capabilities, so be sure to check back often!

Our goal is to make data profiling easy and accessible to everyone. Whether you’re a small business owner trying to understand your customer base, or a large corporation looking to improve your data quality, we can help.

2. Aggregate Profiler

Aggregate Profiler is an open-source data quality and data profiling tool that can be used to carry out data profiling and analysis. You can use it to check the quality of your data, make corrections, and profile your data. It supports various file formats, including RDBMS, flat files, XML, and XLS. 

In addition to data quality checks, Aggregate Profiler can also be used for tasks such as metadata discovery, anomaly detection, basket analytics, similarity checks, and more. When you choose Aggregate Profiler, you can be sure that you are getting a tool that is constantly being updated and improved. 

They take data quality seriously, and their team is always working to make sure that their tool is the best it can be.  You can rest assured that your data is in good hands with Aggregate Profiler.

3. Atlan

Atlan is an autonomous data profiling tool that provides a modern solution for data profiling. You can use Atlan for data profiling from a variety of different sources and connect with the software you choose. Atlan is one of the most accessible data profiling tools to get the data quality corrected in the required context and format while facilitating BI software integrations, native Excel plugins, and more. 

With an auto-generated profile, crowdsourced metadata, data dictionary, and README editor, Atlan offers a solution for every need. Using Atlan can help you improve the quality of your data, and make it easier to connect with different software applications. All your data profiling needs can be met with Atlan. Their software is constantly being updated to ensure that you always have the most accurate data possible.

4. IBM InfoSphere Information Analyser

The IBM InfoSphere Information Analyser is a powerful data profiling tool that can help you identify issues with your data quality, content, and structure. The working principle of the tool is based on column analysis, primary key analysis, natural key analysis, cross-domain analysis, and more. All your big data, business intelligence, data warehousing, and data management needs can be effectively met by deploying this tool. 

Some of the best features of the IBM InfoSphere Information Analyser include its machine-learning capabilities for auto-tagging data and identifying potential issues, as well as more than 200 built-in data quality rules for controlling bad data ingestion. IBM is one of the most well-renowned and trusted companies in the data management domain, and its products are used by some of the biggest names in the industry.

5. Informatica Data Explorer

Informatica Data Explorer comes in two modes, standard and advanced. It can analyze large data sets for anomalies and hidden relationships. The tool also has pre-built rules that can be applied to the data for profiling. Informatica Data Explorer supports all types of structured and unstructured data. Developers can use this tool to quickly and thoroughly profile data in the repository.

Your business can use Informatica Data Explorer to establish benchmarks, identify issues, and cleanse your data. It had been in the industry for more than two decades and has been constantly updating its features to ensure that it meets the needs of businesses. You won't have to worry about the quality of your data when you use Informatica Data Explorer. They have a reputation for being one of the best in the business.

6. Melissa Data Profiler

You can rely on Melissa Data Profiler for data profiling, data enrichment, data matching, and data verification tasks. This easy-to-use tool can format, check content, and analyze all kinds of data sets quickly and efficiently. The profiling capabilities help ensure that the data arriving in your warehouse is consistent and of high quality. 

Maintaining data standards, enhancing data governance, and managing data conveniently all become much easier with this tool. You will be able to easily identify and extract data, monitor the quality process, create a metadata repository, and more when you use Melissa Data Profiler. Investing in Melissa could be a great decision for your business.

7. Microsoft DOCS

Microsoft is a famous name in the industry, you know that you are in good hands with their Data Profiling task. Broad data types are no problem for this software and it will quickly become one of your most valuable tools in terms of quality assurance and data analysis. 

Get ahead of the game, and avoid any future data quality issues by implementing Microsoft DOCS into your workflow. You can use the software for data cleansing, data discovery, data transformation, data mining, and much more.

8. SAP BODS

Your best bet for data profiling and analysis is Business Objects Data Services (BODS). It's a comprehensive package that includes data quality monitoring, metadata management, and data profiling. With BODS, you can check for things like redundancy, sparseness, pattern distribution, cross-system data dependencies, and more. 

Standard profiling gives you an understanding of the unique values in each column, while relationship profiling provides detailed insights. Either way, BODS is an essential tool for ensuring the quality of your data. It will save you time and money in the long run by helping you avoid bad data.

9. SAS DataFlux

The fact that DataFlux can quickly and securely extract, profile, standardize, and monitor data makes it an invaluable tool for ensuring high-quality data in every business process. 

By providing high-performance environments for creating and exploring data profiles, designing data standardization schemes, and more, DataFlux makes it easy to keep your data clean and accurate. SAS DataFlux allows you to improve your decision-making, business processes, and data quality while reducing costs.

10. Talend Open Studio

Talend offers deep visibility into an organization's data through its free, downloadable Open Studio tool. This flexible tool can help you assess the quality of different types of data fields, databases, and file types. It comes with a sophisticated framework that includes pre-built connectors and monitoring tools to help address data deduplication, validation, and standardization. 

Furthermore, Talend Open Studio can be used to quickly prototype data quality solutions and workflows. As your needs evolve, you can easily scale up your investment in Talend by purchasing additional modules and support services. This is one of the best free data profiling tools available today.

Conclusion

It's very clear that data profiling is crucial to maintaining high-quality data in your organization. They can help you avoid costly data quality issues down the road. Your business will thank you for taking the time to implement a data profiling tool. It can mean the difference between a successful data-driven organization and one that struggles with poor data quality.

Don't wait until it's too late, implement a data profiling tool today! Boltic's no-code, easy-to-use platform can help you quickly and efficiently profile your data. Our industry-leading tool can help you establish benchmarks, identify issues, and cleanse your data.

Contact us today to learn more about how Boltic can help you achieve your data profiling goals! Try Boltic for free today! We are sure you'll be happy you did.

FAQ

What is data profiling in ETL Testing?

Data profiling is the process of analyzing data to understand the nature, structure, and quality of that data. Data profiling can be used to assess the quality of data, to understand how data is distributed, or to find patterns in data. During an ETL testing project, data profiling can be used to understand the source data, to assess the quality of the data, or to find issues with the data.

What are the steps of data profiling?

There are four basic steps in data profiling: 1. Collect data: In order to profile data, you first need to collect it. This can be done manually or through automated means. 2. Analyze data: Once the data is collected, it needs to be analyzed. This can be done using statistical methods, visualizations, or other means. 3. Interpret data: The next step is to interpret the data. This involves understanding what the data means and how it can be used. 4. Communicate findings: Finally, the findings of the data profiling need to be communicated to those who will use it. This can be done through reports, presentations, or other means.

What is an example of profiling?

arrow down
An example of profiling is when law enforcement looks at the characteristics of a person who has committed a crime in order to try and find other people who may have committed similar crimes. Another example is when businesses profile their customers in order to better understand them and target their marketing efforts.

Why do we need data profiling?

Data profiling is important because it can help to understand the data, to assess the quality of the data, or to find issues with the data. Data profiling can also help to improve decision-making, business processes, and data quality while reducing costs.

What is a profiling technique?

A profiling technique is a method used to profile data. Some common techniques include statistical methods, visualizations, and machine learning. You will need to choose the right technique for your needs, depending on the type of data you are profiling and the goals you hope to achieve. Once, you have chosen a technique, you will need to collect data, analyze it, interpret it, and communicate your findings.

What is data profiling and data quality?

Data profiling is the process of analyzing data to understand the nature, structure, and quality of that data. Data quality is a measure of the correctness, completeness, and consistency of data. Data profiling can be used to assess the quality of data, to understand how data is distributed, or to find patterns in data. On the other hand, data quality assessment can be used to determine the overall quality of a dataset, to identify issues with data, or to develop plans for improving data quality.
Kickstart your journey with Boltic & make data operation easy
Try Now

Spend less time building pipelines and more time scaling your business

Manage Big Data operations with a free forever plan
No credit card required

Boltic is a Modern Enterprise Grade Data Platform for businesses of all sizes and industry. Built with the vision to simplify data exploration and make work life easier with automation.

Solve advanced data problems, automate ETL workflows, build and share reports at scale. Easily integrate data from multiple sources, transforming it, and sending it to desired destinations.

© 2024 Shopsense Retail Technologies  |  #MadeInIndia