11 Open Source Data Exploration Tools You Need to Know in 2023 11 Open Source Data Exploration Tools You Need to Know in 2023
There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like... 11 Open Source Data Exploration Tools You Need to Know in 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc. In this article, we’re going to cover 11 data exploration tools that are specifically designed for exploration and analysis.

Data Exploration/Exploratory Data Analysis

Data exploration is the initial act of getting to know your data and what you’re working with, often working with raw data to find any initial characteristics and patterns. Data visualization can help here by visualizing your datasets. It’s also part of the initial process of preparing your data, and may involve cleaning, transforming, and working with any anomalies. These tools will help make your initial data exploration process easy.


GitHub | Website

The primary goal of ydata-profiling is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as HTML and JSON.


YData Profiling Logo



GitHub | Website

Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.



Apache Superset

GitHub | Website

Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.  Features include an intuitive interface for visualizing datasets and building interactive dashboards. Performance is impressive, has an impressive integration library, and solid security and authentication. The no-code visualization builds are a handy feature. Apache Superset remains popular thanks to how well it gives you control over your data.

Explore (chart buider) UI



GitHub | Website

Algorithm Visualizer is an interactive online platform that visualizes algorithms from code. It offers visualization tools in various languages including JavaScript, Java, and C++. The project was inspired by a group of coders looking to visualize what they’re working on, thus creating a tool that can show algorithms and descriptions of algorithms in real time.


Data Quality

Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable. High-quality data is essential for making informed decisions, as well as for the effective operation of systems and processes that rely on it. Maintaining high-quality data is critical for organizations in order to avoid negative impacts on decision-making and business operations.



GitHub | Website

Cleanlab is focused on data-centric AI (DCAI), providing algorithms/interfaces to help companies (across all industries) improve the quality of their datasets and diagnose/fix various issues in them. This tool automatically detects problems in an ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels for robust training and flagging errors in your data.

Cleanlab’s Chief Scientist & Co-Founder, Jonas Mueller, will present more about the tool at ODSC East coming this May, in a session called “Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI.”


Great Expectations

GitHub | Website

Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing, documentation, and profiling. With Great Expectations, data teams can express what they “expect” from their data using simple assertions. Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality.

Sam Bail, technical lead at Superconductive (the core maintainers behind Great Expectations), delivered a talk about building a robust data pipeline during ODSC East 2021. You can watch it on demand here.




GitHub | Website

VisiData is a free, open-source tool that lets you quickly open, explore, summarize, and analyze datasets in your computer’s terminal. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility that can handle millions of rows with ease.

Screenshot of table in VisiData

Data Profiling and Data Analytics

Now that the data has been examined and some initial cleaning has taken place, it’s time to assess the quality of the characteristics of the dataset. This includes its structure, content, and relationships between variables. This step is important because it’s used to identify any issues or inconsistencies in the data. Data analysts can use these tools to examine the data and produce reports on key aspects, such as data types, ranges, distributions, and so on. To differentiate from data exploration, data profiling is focused on the quality of the data, whereas data exploration is meant to better understand the data.



GitHub | Website

Metabase is an easy-to-use data exploration tool that allows even non-technical users to ask questions and gain insights. This business intelligence and user experience tool allows you to build interactive dashboards, models for cleaning tables, and set up alerts to notify users when your data changes. You can even connect directly to 20+ data sources to work with data within minutes.

Metabase Product Screenshot



GitHub | Website

A popular open-source business intelligence tool, Lightdash is designed for dbt (data build tool), and allows data analysts and engineers to control all of their business intelligence tools in a single place, bridging the gap between the transformation and visualization layers. The tool is a full-stack BI platform, so analysts can write their metrics in-house, enabling the entire business to work with the data with ease.



GitHub | Website

Perspective is an interactive analytics and data visualization component, which is especially well-suited for large and/or streaming datasets. This tool allows users to create easily-configurable reports, dashboards, notebooks, and applications.


Apache Doris

GitHub | Website

Built on an MPP (massively parallel processing) architecture, this tool from Apache is a high-performance, real-time analytics database, known for speed and ease of use. Apache Doris can better meet the scenarios of report analysis, ad-hoc query, unified data warehouse, Data Lake Query Acceleration, etc. Users can build user behavior analysis, AB test platform, log retrieval analysis, user portrait analysis, order analysis, and other applications on top of this.

How to learn more about data exploration tools and uses

There are plenty of data exploration tools available and countless ways to use them. For those looking to get more out of their data, whether you’re new to data science or you’re a seasoned pro, getting hands-on training with these tools is the best way to learn how they work. At ODSC East 2023, we have a number of sessions related to data visualization and data exploration tools. By registering now for 60% off, you can see these sessions and more.

  • Graph Viz: Exploring, Analyzing and Visualizing Graphs and Networks: Tamilla Triantoro, PhD | Associate Professor of Computer Information Systems | Quinnipiac University
  • Beyond the Basics: Data Visualization in Python: Stefanie Molin | Software Engineer, Data Scientist, Chief Information Security Office, Author of Hands-On Data Analysis with Pandas | Bloomberg LP
  • Streamlining Your Streaming Analytics with Delta Lake & Rust: Gary Nakanelua | Managing Director, Innovation | Blueprint Technologies (BPCS)
  • Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI: Jonas Mueller | Chief Scientist and Co-Founder | Cleanlab
  • How to build stunning Data Science Web applications in Python – Taipy Tutorial: Florian Jacta and Albert Vu | Customer Success Managers | Taipy
  • Interactive Explainable AI: Meg Kurdziolek, PhD | Sr. UX Researcher | Google
  • Next-Level Data Visualization in Python: A Practical Guide to Upgrading Your Plots by Making the Most of Matplotlib and More: Melanie Veale, PhD | Data Solutions Architect | Anomalo


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