We profile, measure and cleanse your data. 7 quality dimensions with Great Expectations. Your data reliable, ready for real decisions.
$ datapulse profile sales.csv Rows: 12,847 Columns: 23 QUALITY SCORE: 94.2% Completeness: 98.7% ✓ Uniqueness: 96.3% ✓ Consistency: 91.4% ⚠ Accuracy: 95.1% ✓ Timeliness: 89.2% ⚠ Validity: 97.8% ✓ Integrity: 96.0% ✓ █ Report saved to ./reports/sales_dq.html
Our data quality SaaS platform. Automatic profiling, 7 DQ dimensions and reports ready for decision-making.
Upload your CSV or connect your database. DataPulse analyzes completeness, uniqueness, consistency, accuracy, timeliness, validity and integrity. It generates visual reports with actionable recommendations.
From profiling to business-ready dashboards. All with Python, AI and best practices.
Complete analysis of CSVs, tables and databases. Statistics, distributions, types, nulls and patterns.
CSV · SQL · APIsMeasurement of 7 dimensions with Great Expectations. Automated rules, alerts and continuous scoring.
7 dimensions · GXETL, normalization, deduplication and standardization. Data ready for analysis or migration.
ETL · Pandas · SQLVisualization with quality metrics. Automatic HTML, PDF reports or integrated into your system.
HTML · PDF · APIREST endpoints to consume clean and validated data. Direct integration with your application or BI.
FastAPI · REST · JSONData strategy for your company. We evaluate your maturity, recommend tools and define DQ metrics.
Strategy · DQ · AIEach dimension is measured with automated rules. The result is an actionable score.
No missing values in critical fields.
No duplicate records that distort results.
Coherent data across different sources.
Values correctly represent reality.
Data updated when needed.
Data meets format and business rules.
Consistent relationships between tables and data.
We process CSV, Excel (.xlsx), JSON, SQL databases (PostgreSQL, MySQL, SQL Server) and data from REST APIs. If you have another format, we evaluate it at no obligation.
It's not required. We can work with local files, on-premise databases or in the cloud (AWS, GCP). We adapt to your current infrastructure.
A basic profiling of a CSV or table takes minutes with DataPulse. A complete analysis of multiple sources can take 1-3 days depending on volume and complexity.
Yes. We connect to PostgreSQL, MySQL, SQL Server, MongoDB and more. We use secure connections (SSL/SSH) and never modify your original data.
Great Expectations is an open-source Python library for data validation and quality. It allows you to define rules (expectations) and run them automatically against your data to generate quality reports.
Tell us what you need. We respond in minutes.