DataHub Data Catalog Alternative: Open Source DataHub Lineage and Pricing

What DataHub is, and what the Acryl rename changed

DataHub started at LinkedIn and is now maintained by the company behind it, which used to be called Acryl Data. In May 2025, after a $35 million Series B, Acryl renamed itself DataHub, so the company and the project now share a name. That rename is still working its way through the internet, and it creates three small traps worth knowing before you research this tool.

First, acryldata.io now redirects to datahub.com, so older links resolve but older articles talking about "Acryl" and "DataHub" as two things are describing one company. Second, the Python package did not follow the rename: you still pip install acryl-datahub, currently at version 1.7.0.4, which trips people who go looking for a package called datahub. Third, and most annoying, datahub.com and datahub.io are unrelated. datahub.io belongs to the Open Knowledge Foundation and has nothing to do with this product. If you land on a page about open data packages, you are on the wrong site.

The project itself is in good health, which is not something you can say about every open-source catalog. As of August 2026 the repository carries roughly 12,500 stars and 3,700 forks, release v1.7.0 shipped on 4 August 2026 with a v1.6.0.1 patch following on 13 August, and there were commits today. Compare that with Apache Atlas or Amundsen, where the release cadence tells a very different story. DataHub has also repositioned: the repository now describes itself as "The Context Platform for your Data and AI Stack" rather than a data catalog, which is the same language shift OpenMetadata made.

DataHub column-level lineage really is free, and that is rare

This is the strongest reason to look at DataHub and we are not going to soften it. Column-level lineage is in the free Apache 2.0 edition. It is not a chargeable add-on, it is not fenced behind an enterprise tier, and it is not the table-level approximation that DataHub is sometimes unfairly accused of shipping.

That matters because of what the commercial field looks like. Alation documents column-level lineage as a per-connector enablement, and for Snowflake specifically it requires a parser add-on you have to buy. Collibra has it, but does not generate it for tables created by SQL statements unless you feed the SQL through a folder connection. AWS Glue Data Catalog has no lineage UI or API at all. Against that backdrop, "free, column-level, sixty-plus sources" is a genuinely strong position.

The coverage numbers are real. DataHub's own lineage support matrix lists more than sixty ingestion sources with both table and column-level lineage, including Snowflake, BigQuery, Databricks, Redshift, dbt, Looker, Tableau, Power BI, Postgres, MySQL, Oracle, SQL Server, Trino and Athena. A smaller set is table-level only, and it is worth checking whether yours is on it: Airbyte, Informatica, Kafka Connect, Metabase, NiFi, Preset, Redash, SageMaker, Salesforce, Superset, Flink, Feast, Fabric Data Factory, Vertex AI, Snowplow, SAP Analytics Cloud and Amazon Kinesis Data Streams. The lineage itself comes from a SQL parser built on sqlglot, which DataHub says "generates lineage with 97-99% accuracy and outperforms other SQL parsers by a wide margin".

The part most DataHub comparisons miss: Core has no monitoring

Here is the fact that changes shortlists, and almost no comparison page states it plainly. DataHub Core, the free edition, does not monitor your data. It catalogs it and it maps lineage across it. It does not tell you when a table stopped refreshing.

Every reliability capability is listed in DataHub's own documentation as DataHub Cloud only. That includes "Freshness, Volume, Schema & Column Monitoring, Custom SQL Checks", "AI Anomaly Detection", the "Data Health Dashboard", "Notifications for Data Assertions", "Monitoring Rules", "Pipeline Circuit Breakers (API)" and the "Data Observability Agent". So does most of the AI story people are excited about, including the "DataHub Hosted MCP Server", the "Ask DataHub AI Agent + Plugins" and AI documentation generation, plus the enterprise controls: a 99.5% uptime SLA, fine-grained access control, AWS PrivateLink support and IP address restrictions.

None of that is a criticism. It is a completely normal open-core split and DataHub is transparent about it in the docs. But it means the sentence "we will just use DataHub, it is free" quietly resolves to one of two very different projects. Either you are adopting a free catalog and lineage graph and you still have no answer to data reliability, or you are starting a procurement conversation for DataHub Cloud, which brings us to the price.

DataHub pricing: there is no price

We checked this again in August 2026 rather than repeat what other pages say. datahub.com/pricing returns a 404, and there is no pricing link anywhere in the site navigation. DataHub Cloud is quoted, not listed.

That puts DataHub in the large majority of this category. Collibra, Alation, Atlan, Informatica, Ataccama, Immuta, Monte Carlo, Datafold and Acceldata all publish nothing. You will find third-party figures for DataHub Cloud on aggregator sites. We will not repeat them, because DataHub has not published them and the aggregators contradict each other badly enough that quoting one would be inventing a number with extra steps. Ask for the quote, and ask specifically what is metered: assets, seats, ingestion volume, or some combination.

The other cost is the one that does not appear on any quote. A production DataHub deployment is a real piece of infrastructure. Its Kubernetes guidance states that "more than 7GB of RAM is required to run DataHub and its dependencies", and those dependencies are four separate systems: Kafka, a relational database (MySQL, Postgres or MariaDB), a search index (Elasticsearch) and a graph index (Neo4j or Elasticsearch). On top of those sit four DataHub components: GMS, the MAE consumer, the MCE consumer and the frontend. That is an ongoing platform team commitment, not a weekend install, and it is the honest counterweight to the word "free". Teams comparing self-hosted total cost against a managed tool should price the engineer, not just the license. The same arithmetic applies to every open-source option in the data catalog field.

What DataHub says its own SQL parser cannot do

A 97-99% accuracy claim is strong, and it is also a claim about the queries the parser can read. DataHub documents the cases it cannot, and these are worth reading before you assume a lineage graph is complete, because a missing edge looks exactly like a table with no downstream consumers.

• Scalar UDFs and table-valued functions. For scalar UDFs, lineage points to the inputs but the parser cannot understand the function itself. Tabular UDFs are unsupported.
• json_extract and similar functions, which is a real gap if you land semi-structured payloads and unpack them downstream.
• UNNEST and structs, handled best-effort only.
• Multi-statement SQL and scripting, which covers a lot of stored procedure and orchestration logic.
• Stale schemas break column lineage. In DataHub's words, "in cases where the table schema information in DataHub is outdated or otherwise incorrect, we may not be able to generate accurate column-level lineage."
• Filter and join columns are not tracked. Columns used only in WHERE, GROUP BY, ORDER BY, JOIN or HAVING clauses do not produce lineage, so a column that silently controls which rows land downstream will not show as an upstream dependency.
• Dynamic identifiers are not supported; only static table references are analyzed.

Every SQL-parsing lineage tool has a version of this list, including ours. The difference is that DataHub publishes theirs, which is more than most vendors do. Use it as a checklist against your own warehouse patterns rather than as a reason to rule the tool out.

When DataHub is right, and when a lineage-first tool is

Buy DataHub if you have a platform team with capacity, you want a catalog the whole company uses, you need a business glossary and stewardship workflows, and your budget prefers engineering time to a subscription. It is the best free column-level lineage available and the community around it is active. If your requirement is genuinely "runs entirely inside our perimeter with no vendor", the open-source options are the honest answer and we are not one of them, and our maintenance check on open source data catalog tools compares DataHub against the rest of that field.

Look at Datatrail if the job is narrower and the constraint is time. We are built around the graph rather than the catalog: query history and the dbt manifest are parsed into column-level lineage, and everything else derives from it. Open a column and see downstream impact, every model, exposure and dashboard that reads it, before you merge rather than after a dashboard breaks. Freshness and schema change monitoring run on the same graph, in the same product, at the same price, with no Cloud tier to negotiate for them.

The connection is read-only and takes minutes, there is no Kafka cluster to run, and the price is on the pricing page: $99 a month at the Starter tier, $299 for Team, $799 for Scale. For a data team of a handful of engineers, that is usually less than the fully loaded cost of the week it takes to stand up four dependencies and keep them patched. If you want to see the whole field first, our comparison of data lineage tools and metadata management tools both cover DataHub alongside the commercial platforms.