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.
In this article, you will get an understanding of what Data Profiling is, along with a list of the best Open-Source Data Profiling tools.
Introduction to Data Profiling
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Data Profiling can be defined as the process of examining and analyzing data to create valuable summaries of it. The process helps yield maximum quality from the gathered data and insights within a given dataset, which organizations can use to make effective business growth decisions. As data grows exponentially, keeping up the data quality complexities becomes strenuous for companies for maintaining productivity and efficiency with their data analytics initiatives.
According to Gartner’s research, the average financial impact of poor data quality on organizations is USD 9.7 million per year. As a result, Data Profiling is an essential process for organizations to garner data that can be instrumental in their analytics workflows. There are now a wide variety of Open-Source and Paid Data Profiling Tools available that can help businesses manage their data better.
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.
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.
Hevo Data, a No-code Data Pipeline, empowers you to perform Data Profiling on a multitude of data sources to streamline your data cleansing & transfer processes. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures high Data Quality and Data Governance for your work and lets you focus on other key business activities.
Hevo lends itself well for any data profiling, pre-processing, and transformations before loading them to your Data Warehouse. Hevo is fully managed and its pre-built integrations with 100+ data sources (including 40+ free sources), allow you to unify and profile your decentralized data in a single run. It provides a consistent & reliable cloud-based solution to manage data in real-time and always have analysis-ready data in your desired destination.
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Auto Schema Mapping: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema. This way, you can perform fast data profiling even for your unstructured data.
- Quick Setup: Hevo with its automated features, can be set up in minimal time using just 3-steps. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. This allows you to plan out, which data you need to profile during the ETL process and which data needs fixing at the source itself.
- Hevo Is Built To Scale: You can easily leverage Hevo to cope with your increasing data loads. As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous real-time data movement, cleanse your data seamlessly using Hevo’s automated data profiling tool and No-code interface. Try our 14-day full access free trial.
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The 8 best Open-Source Data Profiling tools available are as follows:
1) Talend Open Profiler
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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.
More information about Talend Open Studio can be found here.
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2) Quadient DataCleaner
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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.
More information about Quadient DataCleaner can be found here.
3) Open Source Data Quality and Profiling
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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, this tool also supports Hadoop to transfer files between Hadoop Grid for working seamlessly with a plethora of data.
More information about Open Source Data Quality and Profiling can be found here.
4) OpenRefine
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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.
More information about OpenRefine can be found here.
5) DataMatch Enterprise
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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.
More information about DataMatch Enterprise can be found here.
6) Ataccama
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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.
More information about Ataccama can be found here.
7) Apache Griffin
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Apache Griffin is an Open-Source Data Quality solution 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.
More information about Apache Griffin can be found here.
8) Power MatchMaker
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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.
More information about Power MatchMaker can be found here.
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.
Most businesses today use multiple platforms to carry out their day-to-day operations. As a result, all their data is spread across the databases of these platforms. Now, building an in-house data integration solution that can also take care of data cleansing and quality assurance, would be a complex task requiring a high volume of resources. Businesses can instead use existing automated No-code data integration platforms like Hevo.
Hevo is an all-in-one cloud-based Data Pipeline that automates your data cleansing, profiling, and transformation tasks before loading it to your desired Data Warehouse. Hevo’s native integration with 100+ sources(including 40+ free sources) ensures you can move your data without the need to write complex ETL scripts. Once you have chosen the required data sources, Hevo will take over and ensure a thorough data profiling of your chosen data without requiring any assistance from your side. With Hevo, you can go a step further and even derive insights from your profiled data in real-time. Hevo will make your life easier and make data profiling hassle-free.
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