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Great Expectations Data Quality in Python, and an Honest Alternative
Great Expectations is an open-source Python framework for data quality, and it is good. To be clear about who is talking: we are not Great Expectations. We build Dataobservability, a different product from a different company, and this is a comparison rather than a takedown. GX Core is Apache-2.0, free, and actively developed, with v1.19.0 released on 2026-07-13, and Fivetran took over stewardship of the project in May 2026. The hosted GX Cloud is gone: FICO acquired it and folded it into the FICO platform, and it stopped being publicly available on June 1, 2026. So GX today means the open-source Core library. The real trade is not quality, it is who does the work: you write and maintain expectations in Python, or you let monitors generate themselves from warehouse metadata.
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Short answer
Great Expectations (GX) is an open-source Python framework for data quality. You install the library, create a Data Context, write expectations such as expect_column_values_to_not_be_null, and run them as checkpoints inside a pipeline. GX Core is Apache-2.0 and free, it is actively maintained (v1.19.0 released 2026-07-13), and Fivetran became steward of the open-source project in May 2026. The hosted GX Cloud is no longer a self-serve option: FICO acquired it and folded it into the FICO platform, and it was retired from public availability on June 1, 2026. Checked August 2026. Dataobservability is a separate product from a separate company: it connects read-only to Snowflake, BigQuery, Databricks, or Redshift, generates freshness, volume, schema, and distribution monitors itself, and publishes its price at 99 dollars a month.
Last updated August 2026
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Side by side
Dataobservability vs Great Expectations
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| Capability | Dataobservability | Great Expectations |
|---|---|---|
| Apache-2.0 open source core | ||
| Free to run yourself, no license fee | ||
| Expectations as code, reviewed in pull requests | ||
| Validates data inside a Python pipeline | ||
| Runs entirely in your own infrastructure | ||
| Coverage without writing a rule per table | ||
| Auto-generated monitors from warehouse metadata | ||
| Learned baselines and ML anomaly detection | ||
| Column-level lineage | ||
| Always-on monitoring with no scheduler to run | ||
| Incident grouping, Slack and PagerDuty routing | Partial | |
| Published price | 99, 299, 799 dollars a month | Core free (Apache-2.0). Hosted GX Cloud retired June 2026 |
Comparison reflects general product positioning and is provided in good faith. Verify current capabilities with each vendor.
What Great Expectations actually is, and the Data Context nobody mentions
GX is a Python library. You pip install it, create a Data Context (the config that holds your data sources, expectation suites, checkpoints, and validation results), point it at data, and write expectations: expect_column_values_to_not_be_null, expect_column_values_to_be_between, expect_table_row_count_to_be_between, and a few hundred more. Then you run a checkpoint, usually from Airflow, dbt, a Databricks job, or CI. That design is why GX is powerful and also why it is work. The Data Context has to live somewhere, be versioned, and be reachable by whatever runs the validation. On a laptop that takes five minutes. In production, across three environments and a scheduler, it is a small internal project with an owner and a backlog. It is worth naming that honestly, because nobody puts it on the slide. The upside of the same design: GX Core is Apache-2.0 and the code is on GitHub, so you can read exactly what it does before you commit, which is more than most vendors in this category will let you do.
The real trade: rules you write versus baselines that generate themselves
An expectation encodes something you already know. expect_column_values_to_be_between(0, 100) catches the day a percentage column ships 340. It does not catch the day the table simply stops loading, because nobody writes expect_this_table_to_still_be_arriving. It does not catch a row count halving unless a human guessed the right band and remembered to widen it after the last growth spurt. It does not catch a distribution sliding a few percent a week. Those are the failures that hurt, because the numbers still look like numbers and the dashboard still renders. Continuous monitoring comes at it from the other end: profile the table, learn what normal looks like for that table at that hour of that weekday, and page when reality departs from it. No rule required, which also means no rule to forget. The cost of our approach is the mirror image. We will never know that an order total must equal the sum of its line items. That is a business rule only a human can state, and GX states it well. The two tools are not competitors.
When you should just use Great Expectations
Plainly, so nobody has to read between the lines: if you have a small number of critical tables, a Python-fluent team, and rules that come from the business rather than from statistics, install GX Core and stop shopping. It costs nothing, it is Apache-2.0, so no acquisition or repricing can take it away from you, and v1.19.0 landed on 2026-07-13, so it is not abandonware. If your validations need to run inside the pipeline and block a bad batch from landing, GX is built for that and we are not: we watch tables after they land, we do not stop a load. If your team already maintains suites it trusts, throwing them away to buy a monitoring product is a bad trade. Keep them. The case for adding something like us starts when coverage stops scaling. Somewhere past a couple of hundred tables, the suites you have cover the tables somebody had time for, and the incidents keep coming from the ones nobody did.
What happened to GX Cloud, and what it means for you
The hosted product changed hands in 2026. FICO acquired GX Cloud and folded it into the FICO platform as the base for its data quality layer, and GX Cloud stopped being publicly available on June 1, 2026. At the same time Fivetran stepped in as steward of the open-source community and the GX Core project, so the library itself keeps shipping. The practical upshot: if you wanted a self-serve, hosted Great Expectations you could sign up for, that option is gone. GX today is the Apache-2.0 Core library you host and schedule yourself, and a managed version now means an enterprise FICO conversation. If a hosted service with a published price and a read-only setup is what you were after, that is the gap we fill: 99 dollars a month for Starter, 299 for Team, 799 for Scale, 14 days free, no credit card. Checked August 2026.
Honest verdict
Which one should you buy?
Pick Great Expectations when
Choose Great Expectations if you have a Python-fluent team, a manageable number of critical tables, and rules that come from the business rather than from statistics. GX Core costs nothing, it is Apache-2.0 so nobody can reprice it out from under you, it runs entirely inside your own infrastructure, and it can fail a pipeline before bad data lands. If your validations need to block a load, GX is the right shape and we are not. If your team already trusts a set of expectation suites, keep them.
Pick Dataobservability when
Choose Dataobservability if the tables that break are the ones nobody had time to write expectations for. We profile every table you connect, learn its normal, and page you when reality departs from it, with column-level lineage showing what downstream is affected. No expectation suites, no Data Context to host, no scheduler to babysit, and a price on the page: 99, 299, or 799 dollars a month, 14-day trial, no credit card.
Questions buyers ask
Great Expectations alternative FAQ
What is Great Expectations in data quality?
Great Expectations (GX) is an open-source Python framework for validating data. You define expectations, which are declarative assertions about a dataset (this column is never null, this column sits between 0 and 100, this table holds between 10,000 and 50,000 rows), group them into expectation suites, and run them as checkpoints from Airflow, dbt, a Databricks job, or CI. When data violates an expectation, the validation fails and you get a structured result you can act on. GX Core is Apache-2.0 and free. Dataobservability, the product behind this page, is a different tool from a different company: it monitors warehouse tables continuously and generates its own baselines instead of asking you to write the assertions.
Is Great Expectations open source and free?
Yes. GX Core is licensed Apache-2.0, the source is public on GitHub, and it is actively developed rather than parked: v1.19.0 was released on 2026-07-13, and Fivetran took over stewardship of the open-source project in May 2026. Free means free of license fees, not free of work. Somebody still writes the expectations, hosts the Data Context, schedules the runs, and keeps all of it green as tables change. The hosted GX Cloud that used to sit on top is gone: FICO acquired it and it was retired from public availability on June 1, 2026. Checked August 2026.
How much does Great Expectations cost?
GX Core costs nothing: Apache-2.0, self-hosted, no license fee, now stewarded by Fivetran. There is no longer a self-serve hosted tier to price. GX Cloud, the old hosted layer, was acquired by FICO and folded into the FICO platform, and it stopped being publicly available on June 1, 2026, so a managed GX now means an enterprise FICO conversation. Dataobservability publishes its price: 99 dollars a month for Starter, 299 for Team, 799 for Scale, 14-day trial, no credit card. Checked August 2026.
What is the difference between Great Expectations and data observability?
GX checks the rules you wrote. Data observability watches for changes nobody predicted. An expectation catches the day a percentage column ships 340, because a human anticipated that. It does not catch the day the table quietly stops refreshing, or the day the row count halves, or a distribution drifting over three weeks, because nobody writes an assertion for the failure they did not see coming. Observability profiles each table, learns its normal, and alerts on the departure. GX is a testing framework, observability is a monitoring system, and most mature teams run one of each.
Does Great Expectations work with Databricks?
GX Core is a Python library, so it runs wherever your Python runs, which includes a Databricks notebook or job, and it reaches data through its data source layer rather than a fixed vendor list. Support for specific engines does move between releases, so read the data source docs for the version you plan to pin before you build around it. Dataobservability connects to Databricks directly with a read-only connection, alongside Snowflake, BigQuery, and Redshift, and sets up with a read-only warehouse connection.
How do you write data quality rules in Great Expectations?
A rule in GX is an expectation. You write it in Python (or generate a first draft with a profiler), collect expectations into a suite, save the suite in your Data Context, and run the suite with a checkpoint from your scheduler or CI. The syntax is readable, which is one of the reasons GX has lasted. The part to plan for is maintenance: every expectation is a small promise about a table, and tables change. Thresholds go stale, columns get renamed, and a suite that nobody has updated in a year fails for reasons that have nothing to do with data quality. Auto-generated monitors invert that: nothing to write, nothing to update, at the cost of never knowing your business logic.
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