Organizations are collecting a colossal amount of Structured and Unstructured Data but are struggling to enhance the quality of information for better decision-making. One of the primary reasons companies fail to obtain quality data is the lack of automation.
Often companies rely on manually writing code to perform validation, cleaning, and filtering of data. While dated practices can help businesses deal with less data, working with Big Data requires automation for improving data quality.
To expedite the process of Data Cleansing, Data Integration, Data Exploration, etc., companies are leveraging Open-Source Data Profiling Tools. Over the years, Data Profiling has proved to be one of the crucial requirements before consuming datasets for any project. This method is vital for Data Conversion and Migration, Data Warehousing, and Business Intelligence projects.
What is Data Profiling?
Data profiling is the process of examining source data to gain an understanding of its structure, content, and inter-relationships between data objects. This process helps in creating comprehensive summaries of data which can identify data quality issues, overall data trends and risks.
Understanding the Types of Data Profiling
Data Profiling encompasses a vast array of methodologies to examine various datasets as well as produce relevant metadata. It also has the capability to protect organizations from costly errors that reside in a database without anyone’s notice.
Some of the crucial types of Data Profiling are as follows:
- Structure Discovery or Structure Analysis: The Structure Discovery or Structure Analysis examines the complete rows and columns of data to determine whether a specific data is consistent in nature or not. Some of the typical structure discovery techniques include pattern matching, validation with metadata, among others.
- Content Discovery: Focusing mainly on the quality of data, Content Discovery takes a closer look at the data and helps users in detecting the issues in specific rows and columns of datasets. Content Discovery Data Profiling works by leveraging techniques like outlier detection, uniformity, frequency counts, etc.
- Relationship Discovery: Relationship Discovery is used to detect the interaction between one data source to another. Relationship Discovery Data Profiling is used to establish links within the data in disparate applications as well as databases.
Data Profiling Steps
Ralph Kimball, renowned for data warehouse architecture, proposes a four-step data profiling process:
- Project Start Profiling: Utilize data profiling early to determine if data is suitable for analysis, enabling a “go/no-go” decision on the project’s feasibility.
- Source Data Quality Check: Identify and rectify data quality issues in the source data preemptively, before transferring it to the target database.
- ETL Enhancement: Use data profiling to uncover data quality issues during the Extract-Transform-Load (ETL) process, facilitating necessary corrections and adjustments.
- Business Rule Identification: Unearth unanticipated business rules, hierarchical structures, and relationships (e.g., foreign key/private key), refining the ETL process accordingly.
Understanding the Need for Data Profiling Tools
There are several benefits of Data Profiling tools, some of which are mentioned below:
- Users can improve the quality of data using Data Profiling Tools.
- Businesses can identify factors that are having a significant impact on quality issues using Data Profiling Tools.
- Data Profiling Tools can determine patterns and data relationships for better data consolidation.
- Data Profiling Tools provide a clear picture of data structure, content, and rules.
- Data Profiling Tools can improve users’ understanding of the gathered data.
If you are exploring Data Profiling tools, then you also must be looking for a way to consolidate your data.
Hevo is an ELT platform that helps move data from 150+ data sources into your desired data warehouse!
Hevo also can identify anomalies, missing values, inconsistencies, and other issues that affect data reliability. All this, without writing a single line of code!
This ensures you have clean and consistent data to power your use case! Explore Hevo for free and see for yourself!
14-Day Full Feature Free Trial
The 8 best Open-Source Data Profiling tools available are as follows:
- Talend Open Studio
- Quadient DataCleaner
- Open Source Data Quality and Profiling
- OpenRefine
- DataMatch Enterprise
- Ataccama
- Apache Griffin
- Data Profiling Tools: Power MatchMaker
1) Talend Open Profiler
G2 Rating: 4.3
Talend Open Studio is one of the most popular Open-Source Data Integration and Data Profiling Tools. It executes simple ETL and data integration tasks in batch or real-time.
Some of the features of this tool include cleansing and managing data, analyzing the characteristics of text fields, integrating data instantly from any source, and others. One of the unique value propositions of this tool is the ability to advance matching with time-series data. The Open Profiler also provides an intuitive user interface that presents a series of graphs and tables, displaying the results of the profiling for each data element.
Although the Talend Open Studio is free to all users, other paid versions of this tool come with advanced features and have a price between $1000 – $1170 per month.
Key Features
- Self-service interface for broad user access.
- Provides summary statistics, visualizations, and anomaly detection.
- Talend Trust Score prioritizes data cleansing.
- Suggests data types based on content.
- Profiles data from databases, cloud storage, and flat files.
2) Quadient DataCleaner
G2 Rating: 2.6
Quadient DataCleaner is one of the Open-Source, plug-and-play Data Profiling Tools that help users run comprehensive quality checks across the entire database. Widely used with data gap analysis, completeness analysis, and data wrangling, Quadient DataCleaner is one of the popular Data Profiling Tools.
With Quadient DataCleaner, users can also perform Data Enrichment and carry out regular cleansing for ensuring extended data quality. Besides quality checks, the tool visualizes the results through convenient reports and dashboards.
The community version of this tool is free to all users. However, the price of paid versions with advanced functionalities is disclosed on request depending on your use case and business requirements.
Key Features
- Analyzes data basics: data types, distributions, missing values, duplicates.
- Checks completeness: identifies missing entries in important fields.
- Summarizes data: provides minimum, maximum, mean, and standard deviation.
- Visualizes findings: uses charts and graphs for data understanding.
- Free option available: open-source version for basic needs.
Check out their GitHub link, to know more about them.
3) Open Source Data Quality and Profiling
G2 Rating: NA
Open Source Data Quality and Profiling is a Data Quality and Data Preparation solution. The tool provides a high-performance integrated data management platform that can perform Data Profiling, Data Preparation, Metadata Discovery, Anomaly Discovery, etc.
Started as a Data Quality and Preparation tool, it now houses features such as Data Governance, Data Enrichment alteration, Real-time Alerting, etc. Today, it’s one of the best open source data quality tools also supports Hadoop to transfer files between Hadoop Grid for working seamlessly with a plethora of data.
Key Features
- Fuzzy logic for similarity checks between data sources
- Analyzes data volume (Cardinality) across tables and files
- Scans entire databases
- Offers SQL interface for advanced users
- Provides data dictionary and schema comparison tool
4) OpenRefine
G2 Rating: 4.6
Previously known as Google Refine and Freebase Gridworks, OpenRefine is an Open-Source tool for working with messy data. Released in 2010, the active community of OpenRefine has strived to enhance the Data Profiling tool for users to keep it relevant in the changing requirements.
Available in more than 15 languages, OpenRefine is a Java-based tool that allows users to load, clean, reconcile, understand data. To ensure improved Data Profiling, it also augments the information from the web. And for strenuous data transforming, users can leverage General Refine Expression Language (GREL), Python, and Clojure.
Key Features
- Visually see data distribution
- Clean data while profiling
- Find duplicate entries
- Profile multiple columns at once
- Analyze data with custom expressions
5) DataMatch Enterprise
G2 Rating: 4.2
DataMatch Enterprise is a popular toolkit for Code-free Profiling, Cleansing, Matching, and Deduplication. It provides a highly visual data cleansing application specifically designed to resolve customer and contact data quality issues. The platform leverages multiple proprietary and standard algorithms to identify phonetic, fuzzy, miskeyed, abbreviated, and domain-specific variations.
DataMatch Enterprise (DME) is free to download, but other versions, such as DataMatch Enterprise Server (DMES), come with a certain price disclosed after booking a demo.
Key Features
- Data Analysis and Profiling
- Data Quality Monitoring and Rules
- Metadata Management and Cataloging
- Data Lineage and Impact Analysis
- Data Standardization and Formatting
6) Ataccama
G2 Rating: 4.2
Ataccama is an enterprise Data Quality Fabric solution that helps in building an agile, data-driven organization. Ataccama provides one of the free and Open-source Data Profiling tools that include features that give users the ability to profile data directly from the browser, advanced profiling metrics including foreign key analysis, perform transformations on any data, etc.
The platform also leverages Artificial Intelligence to detect anomalies during data load to notify the issues with data. Focused on several aspects of Data Profiling, the platform includes different modulus like Ataccama DQ Analyzer to simplify Data Profiling. The community is further working on improving Data Profiling with upcoming modules like Data Prep and Freemium Data Catalog.
Key Features
- Automate data profiling
- Get results fast with efficient processing
- Profile many tables at once
- Analyze data dependencies
- Validate against business rules
7) Apache Griffin
G2 Rating: NA
Apache Griffin is one of the best Data Quality tools open source which can be used for Big Data to unify the process for measuring data quality from different perspectives. It also supports both batch and streaming modes to cater to varying data analytics requirements. Griffin offers a set of pre-defined data quality domain models to address a broader range of data quality issues. This enables companies to expedite Data Profiling at scale.
Key Features
- Data Source Connectivity
- Data Profiling and Analysis
- Data Quality Rules Definition
- Metadata Management
- Data Lineage Tracking
- Data Visualization and Reporting
8) Power MatchMaker
G2 Rating: NA
Power MatchMaker is an Open-Source Java-based Data Cleansing tool created primarily for Data Warehouse and Customer Relationship Management (CRM) developers. The tool allows you to cleanse data, validate, identify, and remove duplicate records.
Highly used for addressing the challenges witnessed during Customer Relationship Management (CRM) and Data Warehouse integration, Power MatchMaker is a go-to solution for transforming key dimensions, merging duplicate data, and building cross-reference tables.
The Power MatchMaker tool is free to download and use, with production support and training available at reasonable prices.
Key Features
- Find duplicate records effectively
- Gain insights into data quality during deduplication
Data Profiling and Quality Best Practices
Basic Techniques
- Distinct Count and Percentage: Identify natural keys and distinct values per column, aiding in insert and update processing, particularly for tables lacking headers.
- Percentage of Null/Blank Values: Detect missing or unknown data, assisting ETL architects in setting appropriate default values.
- String Length Metrics: Determine minimum, maximum, and average string lengths, facilitating the selection of suitable data types and sizes in the target database, optimizing column widths for enhanced performance.
Advanced Techniques
- Key Integrity: Ensure key presence through zero/blank/null analysis, identifying orphan keys that can disrupt ETL and future analyses.
- Cardinality: Assess relationships (e.g., one-to-one, one-to-many, many-to-many) between related datasets, aiding BI tools in executing inner or outer joins accurately.
Pattern and Frequency Distributions: Validate data fields for correct formatting (e.g., email validity), crucial for outbound communications (e.g., emails, phone numbers, addresses), ensuring data integrity.
Additional Resources on Data Profiling Tools
Conclusion
Over the years, Data Profiling has emerged as a critical commodity tool that can be utilized for various tasks, including Data Quality Validation, Data Integration and Transformation processing, Data Quality assessment, etc. The article provided you with a comprehensive guide on popular Data Profiling Tools
Dharmendra Kumar is a specialist writer in the data industry, known for creating informative and engaging content on data science. He expertly blends his problem-solving skills with his writing, making complex topics accessible and captivating for his audience.
