Features
Open-source data profiling tools help engineers automatically scan datasets to uncover statistics, data types, anomalies, and data-quality issues before those datasets are used downstream.
Data profiling is the systematic analysis of a dataset to extract metadata—such as column statistics, data types, distributions, outliers, and rule violations—that reveals the dataset’s overall health and fitness for downstream use.
Commercial data-quality platforms provide rich profiling capabilities, but they introduce vendor lock-in, opaque pricing, and limited extensibility. Open-source profilers, by contrast, deliver transparency, active community support, and the freedom to integrate directly into your data pipelines—whether they run in Airflow, dbt, or a CI/CD workflow.
At minimum, a profiler should compute counts, null percentages, distinct-value ratios, min/max/mean, percentiles, and basic regex or type checks.
Advanced tools allow you to set expectations—such as email must match regex—and flag rows or columns that break them.
Look for APIs (Python, CLI, REST) and the ability to plug into orchestration tools so profiling runs automatically after each load.
HTML reports, Markdown summaries, or in-database tables make it easier for analysts, data scientists, and execs to consume results.
Language: Python
Stars: 9k+
Great Expectations combines profiling with testing. The suite scaffold command scans a dataset, infers expectations, and then validates future loads against them. Its strengths are deep plugin support, YAML-based config, and Jupyter workflow integration.
Language: Java/C++
Stars: 2k+
While not a traditional profiler, DataSketches offers streaming sketch algorithms (HLL, KLL) to calculate cardinality, quantiles, and frequency on massive data without full scans—ideal for petabyte-scale profiling in systems like Druid or ClickHouse.
Language: Scala, Java, Python wrapper
Stars: 2.9k+
Built on Apache Spark, Amazon’s Deequ lets you define constraints (e.g., hasUnique("user_id")) and produces metrics & success/failure reports over large datasets. Perfect for distributed environments.
Language: Python
Stars: 12k+
Generates a single, interactive HTML report that details every column’s type, statistics, correlations, and warnings. Great for quick exploration of CSVs or SQL extracts up to tens of millions of rows.
Language: Java/Python
Part of the wider Open-Metadata project, this profiler connects to more than 200 data sources, runs column-level profiling inside the source system (via SQL), and pushes results into a central catalog—helpful for governance programs.
Language: Python
Stars: 3k+
Soda Core uses YAML checks to define data-quality rules. It profiles datasets in Snowflake, BigQuery, Postgres, and more using push-down SQL so that you don’t extract data.
Treat profiling like unit tests: run suites on every pull request that touches data models. Fail the build if critical metrics deviate.
Persist profiling results in a warehouse table so you can track data-quality trends and anticipate issues proactively.
Pipe failures into Slack or PagerDuty; developers fix issues faster when alerts include both the expectation and offending values.
Technical stats (null counts) matter, but encode domain logic too: status in {"active","inactive"}, signup_date <= today.
You receive a orders.csv dump and need to check its consistency before loading it into Postgres.
pip install ydata-profilingfrom ydata_profiling import ProfileReport
import pandas as pd
df = pd.read_csv("orders.csv")
profile = ProfileReport(df, title="Orders Profiling Report", explorative=True)
profile.to_file("orders_report.html")
orders_report.html to view null heatmaps, correlations, and warnings (e.g., High cardinality in order_id).While Galaxy is primarily a SQL editor, you can leverage it as the launchpad for SQL-based profiling runs. For instance, copy the SQL Suite generated by Open-Metadata into Galaxy, run it against Snowflake, and share the results in a Collection so teammates can endorse trusted quality checks.
Mistake: Profiling only the first full load.
Fix: Schedule profilers to run on every incremental batch; even append-only tables can drift.
Mistake: Scanning 1% of rows and assuming stats generalize.
Fix: Use stratified samples or sketches (DataSketches) to keep error bounds explicit.
Mistake: Generating HTML reports that no one reads.
Fix: Integrate alerts into existing dev workflows and surface critical indicators in tools like Galaxy or BI dashboards.
Whether you choose Great Expectations for its rich validation engine, pandas-profiling for quick one-offs, or Deequ for Spark-scale workloads, open-source profiling is now mature enough to serve as the backbone of a robust data-quality program. Embed it early, automate everything, and let your downstream analytics—and your stakeholders—reap the benefits.
Data teams regularly ingest new sources whose quality is unknown. If bad data flows downstream, dashboards mislead stakeholders and machine-learning models make flawed predictions. Automated, open-source data profiling surfaces null spikes, schema drift, and rule violations early—saving engineering hours and protecting business trust.
If you’re working in a Python-first stack or want tight Jupyter integration, Great Expectations is the better fit. For Spark or Scala-heavy environments, Deequ’s native Spark support and scalability make it preferable.
Yes. Tools like Open-Metadata Profiler and Soda Core push down profiling queries into the warehouse, so no data leaves your database. You can also write custom SQL in Galaxy to calculate null counts or distinct ratios.
Galaxy’s SQL editor lets you execute profiling queries, share them in Collections, and endorse the ones that form your data-quality contract. While Galaxy isn’t a profiler itself, it’s an ideal front-end for SQL-based checks generated by open-source profilers.
It’s excellent for exploratory analysis or small-to-medium datasets. For automated, repeatable production checks, migrate to Great Expectations or Soda Core, which provide version-controlled expectation suites and CI/CD integrations.
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