DataIndy automates data analysis, migration, documentation, onboarding, and integration through intelligent profiling, modeling, and semantic metadata generation, accelerating data projects and preparing data for analytics and AI.
% Time Saved
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x Faster Delivery
% Consistency Gain
Transform raw datasets into reliable, actionable information through comprehensive data profiling, validation, and quality assessment. DataIndy enables you to explore the structure, completeness, consistency, and accuracy of your data, helping you identify anomalies, missing values, duplicates, and inconsistencies before they impact downstream processes.
With customizable analyses tailored to your specific business requirements, you can leverage your own APIs alongside more than 200 already-developed APIs to automate validation rules, analyse datasets, and streamline data preparation workflows.
By combining flexible analysis capabilities with reusable API integrations, DataIndy helps reduce manual effort, improve data reliability, and establish a stronger foundation for analytics, integration, and data-driven decision-making.
Transform raw data into structured, reliable, and
warehouse-ready schemas. DataIndy helps organise data
consistently, establishing a solid foundation for
integration, reporting, and analytics.
Generate schemas across 10 database technologies
using three established modelling approaches:
Third Normal Form (3NF) to reduce redundancy,
Data Vault to integrate data from multiple sources
and preserve historical changes, and Star Schema
to simplify analytical queries and reporting.
By automating schema generation and supporting different
modelling methodologies, DataIndy reduces manual effort
and helps create maintainable data structures that adapt
to evolving business requirements.
Establish consistent data standards across teams and projects.
DataIndy helps define shared conventions for naming, casing,
data domains, load strategies, and data products, ensuring
that data structures remain consistent across different
environments.
By centralising these standards, organisations can reduce
inconsistencies, simplify collaboration, and improve the
maintainability of their data assets. Teams can work
independently while following common rules and practices.
DataIndy promotes a more reliable and scalable approach to
data governance, helping teams align their workflows and
maintain high-quality, well-organised data as business needs
evolve.
Make your data easier to find, understand, and use.
DataIndy automatically generates searchable semantic metadata
that describes your data assets, their structure, and their
meaning, helping teams discover relevant information more
efficiently.
By enriching data with consistent descriptions and business
context, DataIndy supports data governance, improves
transparency, and helps users understand which datasets are
available and how they can be used.
This semantic foundation also helps AI agents interpret and
navigate data assets more effectively, enabling better
discovery and supporting more reliable, context-aware
data-driven workflows.
Protect sensitive information throughout data processing.
DataIndy is designed to process data in memory without
unnecessary persistence, helping minimise the exposure
of confidential information.
By reducing the need to store intermediate data, DataIndy
supports privacy-conscious workflows and helps organisations
identify sensitive datasets and fields while limiting unnecessary
data retention.
This approach helps strengthen data protection and supports
more secure data workflows, allowing teams to work with
sensitive information while keeping privacy in focus.
Navigate the evolving regulatory landscape with greater
clarity. DataIndy helps assess AI systems against relevant
EU AI Act requirements, supporting the identification of
potential risks, applicable obligations, and compliance gaps.
By analysing key aspects of AI systems, organisations can
better understand their responsibilities, including
requirements related to transparency, documentation,
risk management, and human oversight, where applicable.
DataIndy helps streamline compliance assessments and
highlight areas requiring attention, supporting a more
structured approach to AI governance and regulatory
readiness as requirements evolve.
Watch a 2-Minute Product Introduction:
PROFESSIONAL FEEDBACK
Independent perspectives on making complex data landscapes easier to understand and manage.
DataIndy accelerates data onboarding by identifying dataset structures, relationships, and early data quality signals. Its deterministic, reusable approach to data modelling and API extensibility makes it a promising solution for complex, fragmented data environments and federated architectures.
DataIndy simplifies the management of fragmented data landscapes through intuitive dashboards and faster data assessment. It offers potential efficiency gains and cost savings while complementing existing data architectures and BI tools without requiring their replacement.
DataIndy bridges the first mile of data and modern data platforms, turning trusted source data into actionable business value.
It analyzes your datasets (CSV, JSON files, or database tables), automatically detects relationships, and generates an interactive Entity-Relationship Diagram (ERD). You also get AI-powered descriptions and multiple export options.
With one click, it produces a complete data analysis and generates the DDL scripts needed to build your data warehouse.
It suggests how to normalize your tables or files, across multiple database dialects.
It automatically detects outliers and duplicates, helping you clean your datasets before analysis.
It identifies PII/PHI data using machine learning and recommends AI models per dataset column, benchmarking performance automatically.
All these functions — and many more — are powered by deterministic algorithms with AI assistance, ensuring reliable results while minimizing the risk of hallucinations.
No. DataIndy does not store your data in the cloud. Your datasets (CSV, JSON files, or database tables) are processed online in-memory as dataframes and discarded once processing is complete.
This approach ensures full data residency and control, while still enabling advanced capabilities such as automated relationship detection, ERD generation, normalization recommendations, and data quality analysis.
Indy operates across multiple database dialects, automatically detects outliers and duplicates, identifies PII/PHI using machine learning, and generates DDL scripts and AI-powered insights — all without persisting your data.
Yes. DataIndy is GDPR-ready by design and follows key GDPR principles such as data minimization, security, and privacy by design.
Indy does not store or persist customer datasets. All data is processed ephemerally in-memory as dataframes and discarded after execution. The only customer data stored consists of connection credentials for test databases, which are securely encrypted at rest.
DataIndy's AI capabilities are hosted within the same cloud instance where Indy is deployed. AI processing does not rely on external AI providers or third-party API calls, ensuring that customer data remains within the DataIndy environment and is not transferred outside the controlled infrastructure during analysis.
Customer data is never used to train AI models and remains isolated within the DataIndy execution environment.
The platform supports compliance efforts by automatically detecting PII/PHI, enforcing consistent data standards, and embedding governance controls across the data lifecycle, helping organizations meet GDPR obligations related to data protection, accountability, and risk reduction.
Its multi-tenant architecture supports data factory and data mesh initiatives for both SMBs and enterprises, enforcing standards and automating governance at scale.
Unlike traditional data modeling tools that require your data model to already exist in a database, DataIndy works directly with raw CSV files, JSON files, and existing database tables.
It uses heuristic algorithms combined with AI assistance to automatically discover joins, relationships, and data structures, making it ideal for data discovery and rapid prototyping.
With just a few clicks, DataIndy can generate a complete Data Warehouse script, eliminating the need to manually design structures and write scripts from scratch.
Data models can also be exported and imported into tools such as ERWin or other modeling platforms for further refinement, documentation, and generation of complete data model diagrams.
Designed for data analysts, data engineers, consultants, data scientists, students, and small teams who need fast, affordable solutions. Quickly generate ERDs, run data analysis, create DWH/Lakehouse scripts, and perform profiling, cleaning, normalization, and more. Automate end-to-end data integration without the cost and complexity of traditional tools.
Built for enterprise as well: the platform is multi-tenant and helps standardize data products, enforce naming conventions, and manage user access with RBAC. Managers can govern multiple tenants easily, supporting modern architectures such as data factories and data meshes.
The tool is especially useful in migration projects or post-merger integrations, where it can automatically identify relationships across databases and accelerate unification efforts.
DataIndy connects securely to customer test databases, CSV, and JSON files. The only stored customer data are encrypted connection credentials, ensuring maximum security.
Enterprise authentication features, including Single Sign-On (SSO), will be available soon, enabling organizations to integrate DataIndy with their existing identity providers and access management policies.
Data cleaning, normalization, PII/PHI detection, AI-powered analysis, and warehouse design are executed through a secure in-memory processing engine, allowing fast, safe, and efficient processing without touching disk storage.
Governance, GDPR compliance, and consistent data standards are embedded throughout the data discovery and onboarding lifecycle, without slowing down analytics or time-to-market.
DataIndy helps organizations establish trust in their data by automatically identifying PII/PHI, data quality issues, inconsistencies, and structural relationships, providing the visibility needed to apply governance policies early.
By discovering and documenting data characteristics before building pipelines, teams can create more reliable data models, enforce standards consistently, and reduce compliance risks from the beginning.
Yes! You can export your ER diagram as a PDF for documentation or as JSON to preserve node positions. AI-generated entity summaries are included to enrich your documentation automatically.
You can also generate and export DDL scripts, making it easy to recreate the data model directly in industry-standard tools such as ERwin, IDERA, and others.
Yes. File size and row limits are configurable and can be adjusted according to the available infrastructure and specific business requirements. For enterprise deployments, the platform can be configured with additional memory and resources to handle larger datasets.
The platform is designed primarily for data onboarding and discovery, not for large-scale data processing. Its purpose is to help users quickly understand unfamiliar datasets by identifying schemas, data types, relationships, candidate keys, value distributions, missing values, and common data quality issues.
The profiling engine uses in-memory processing to provide a fast and interactive experience while ensuring that customer data is not stored in the cloud. While it is technically possible to increase the infrastructure capacity to analyze larger datasets, loading an entire multi-terabyte dataset into memory is generally not the most efficient approach for data discovery.
In most real-world onboarding projects, a representative sample provides enough information to:
This approach reflects how data discovery has traditionally been performed. Before automated profiling tools existed, data architects did not scan every row of a multi-terabyte dataset to understand its structure. They used representative samples, metadata, business knowledge, and targeted analysis. Automation accelerates this process by making discovery faster and more systematic.
Full dataset scans are appropriate for specific scenarios such as detecting very rare outliers, calculating exact statistics, validating uniqueness across all records, or performing production-level data quality checks. These are data processing and validation activities, which are typically handled by ETL pipelines or distributed processing platforms.
The philosophy behind the platform is:
Understand first. Process later.
By understanding the data before processing it at scale, organizations can design better pipelines, reduce unnecessary processing costs, and identify potential issues earlier.
The platform is optimized to answer:
"What is my data?"
rather than:
"How can I process every record at maximum scale?"
These are different problems requiring different architectures. The platform can be scaled when required, while remaining focused on its core mission: reducing the time needed to understand and onboard new data sources.
The goal of the platform is not to replace ETL engines or distributed data processing platforms. Our goal is to reduce the time required to understand data—from days or weeks to minutes.
By providing automated data discovery, profiling, and relationship analysis before processing at scale, the platform helps organizations design better pipelines, reduce unnecessary processing costs, and accelerate data onboarding.
DataIndy offers flexible plans for both individual users and enterprise teams, depending on data volume, collaboration needs, and governance requirements.
For individual users and small teams, the Raider plan provides free access with limited functionality, allowing users to explore data profiling and discovery capabilities. Advanced features such as exports, data warehouse script generation, and advanced AI-assisted suggestions are available in premium plans.
Individual plans include:
For organizations and enterprise customers, DataIndy provides scalable packages designed around team size, data volume, and security requirements:
Enterprise plans can be configured according to specific requirements, including number of users and dataset size limits.
All subscriptions are flexible and can be upgraded as your needs grow. DataIndy is designed to scale from individual data discovery projects to enterprise-wide data onboarding initiatives.
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