Cloudera Data Lineage (Octopai).
Founded 2016 · Santa Clara, CA
Status · ● acquired
Verified · ● 2mo ago
SaaS lineage with 60+ connectors and a 24-hour deploy story — built for hybrid enterprise estates without IBM-stack baggage.
Where it fits — and where it doesn't.
Mid-market and enterprise data teams who want cross-system column-level lineage spanning legacy ETL, BI tools, and cloud warehouses, but who do not want to procure IBM-scale software.
The 24-hour deploy claim — no professional services required — is genuine for SaaS deployments, and the connector library covers the same hybrid estate Manta does at a generally lower price point. Especially defensible if you're already a Cloudera shop, where it ships as a fabric component, but the standalone SaaS purchase via AWS or Azure Marketplace is a real path for non-Cloudera buyers too.
You need OpenLineage interoperability — Cloudera Data Lineage does not consume or produce OpenLineage events as of 2026, which makes it a poor substrate under modern Airflow / dbt / Spark stacks.
Avoid also if your estate is purely modern (Snowflake plus dbt plus Looker), where the connector-depth advantage matters less and an OpenLineage-native catalog (Atlan, DataHub) gives you more. Pricing is sales-led and undisclosed.
The honest scorecard.
- 60+ native connectors covering ETL (Informatica, SSIS, Talend), BI (Tableau, Power BI, Cognos, MicroStrategy), and modern warehouses
- SaaS-only deployment with a credible 24-hour time-to-value claim — no professional services bundled in
- Available on AWS Marketplace and Azure Marketplace as a standalone purchase, even outside Cloudera deployments
- Cross-system column-level lineage with reverse impact analysis — comparable scanner depth to Manta
- ML-driven metadata stitching across heterogeneous sources where naming and joins differ
- No OpenLineage support as of 2026 — does not consume or produce OL events, isolating it from the modern lineage standard
- No dbt integration of any depth — a meaningful gap for analytics engineering teams
- Pricing is undisclosed and sales-led; capacity model based on source-system count is not buyer-friendly for self-serve evaluation
- No public SDK or terraform provider — integration is API-driven only
- Strategic positioning as part of Cloudera's 'Unified Data Fabric' may distract roadmap from standalone-product polish over time
What Cloudera Data Lineage (Octopai) actually is.
What Cloudera Data Lineage actually is
Cloudera Data Lineage — still recognisable to most buyers as Octopai — is a SaaS product whose distinguishing fact is the breadth of its connector library and the speed of its deploy story. Sixty-plus native connectors cover legacy ETL (Informatica, SSIS, Talend, DataStage, Ab Initio, SAS DI), enterprise BI (Tableau, Power BI, Cognos, MicroStrategy, Qlik, SAP BusinessObjects), and modern cloud warehouses; a typical deployment claims 24 hours from contract to first lineage. Column-level lineage and reverse-impact analysis are first-class.
Since the November 2024 Cloudera acquisition, the product has continued to ship as a distinct SaaS offering, with the first major Cloudera-era release tagged 1.0.0 in October 2025 and a Spark Connector added in June 2025. AWS and Azure Marketplace listings give non-Cloudera buyers a clean procurement path.
Where it fits against the alternatives
The honest comparison is to ibm-manta. Both are scanner-driven, both target enterprise estates with legacy ETL and BI, both ship column-level cross-system lineage with reverse impact. The trade is procurement context and software-stack alignment: Manta if you’re already in IBM watsonx.data intelligence territory; Cloudera Octopai if you want a SaaS-only path without IBM Cloud Pak infrastructure, or you’re already a Cloudera shop, or you want to procure via AWS/Azure Marketplace.
Against modern OpenLineage-native catalogs (atlan, datahub, openmetadata), Cloudera Data Lineage is the right answer when the modern stack isn’t where your lineage problem lives. The lack of OpenLineage support is the trade — your modern Airflow and dbt emitters won’t reach Cloudera Data Lineage as of 2026.
On the OpenLineage gap
The single biggest strategic gap in Cloudera Data Lineage in 2026 is the absence of OpenLineage support — neither as consumer nor as producer. For organisations standardising on OpenLineage as the metadata interchange protocol, that is a real disqualifier; events from your Airflow DAGs and dbt runs won’t flow into this product. Cloudera has not publicly committed to closing this gap, which is informative. For organisations whose lineage problem is overwhelmingly in scanner-resolvable estates (legacy ETL, BI tools, warehouse SQL), the OpenLineage gap matters less, but it is the right question to ask in a sales conversation.
How to evaluate it
The honest test is whether the connector library actually covers your estate at the depth claimed, and whether the 24-hour deploy holds when integration realities (auth, network, source-system credentials) hit. Run a paid proof-of-concept on a meaningful subset — three legacy systems, two BI tools, your warehouse — and look at: did column-level lineage resolve through every source, was the cross-tool stitching accurate, and did the reverse-impact analysis answer the regulatory question your team actually has? If yes, the product earns its premium relative to running a Marquez backend with manual emitters; if no, the connector breadth is less valuable than the marketing implies.
All capabilities by cluster.
Catalog & discovery
Secondary · strength 2/3Lineage & metadata
Primary · strength 3/3Where it plugs in.
Native warehouse support
Orchestrators & pipeline tools
The honest pricing breakdown.
Sales-only tier All tiers — sales-led, scaled by source-system count
What it doesn't do.
Emits and consumes OpenLineage events as a first-class citizen rather than via a plugin or adapter. Signals commitment to interoperability with other metadata tooling — Marquez, OpenMetadata, Astronomer, and others can consume the same event stream. Increasingly the differentiator between "open" and "proprietary metadata model" observability platforms.
Pre-Merge Diffing →Compares the output of a model change against production before the pull request is merged — showing row-level and aggregate differences. Shifts data quality left into the development workflow. Datafold is the category-defining tool here; dbt's own cloud offering has added similar capabilities. Requires production-scale compute on a development branch, which has cost implications.
Data Contracts →Explicit, versioned agreements between data producers and consumers specifying schema, semantics, SLAs, and breaking-change policy. Enforced in CI for producers and at consumption time for consumers. Distinct from schema validation alone — a contract captures intent, not just structure. Implementations vary wildly; many tools claiming "data contracts" offer only schema checks.
Drill into one capability.
Other key features
If not Cloudera Data Lineage (Octopai), then what?
Common alternatives
Quick answers.
- Is Cloudera Data Lineage (Octopai) open source?
- No. Cloudera Data Lineage (Octopai) is a proprietary product.
- How much does Cloudera Data Lineage (Octopai) cost?
- Cloudera Data Lineage (Octopai) does not publish list pricing — it is sales-led, so you request a quote. There is no free tier.
- How is Cloudera Data Lineage (Octopai) deployed?
- Cloudera Data Lineage (Octopai) is a managed cloud (SaaS) product.
- Does Cloudera Data Lineage (Octopai) work with dbt and my warehouse?
- It has no dedicated dbt integration. Cloudera Data Lineage (Octopai) supports snowflake, redshift, bigquery, databricks, postgres, plus 2 more.
More lineage & metadata tools
Provenance.
Last verified 2026·05·08 against vendor documentation and, where possible, hands-on trial. Spot something off? Send a correction →