OpenMetadata/openmetadata-docs-v1/content/v0.13.3/connectors/ingestion/lineage/index.md

---
title: Lineage Ingestion
slug: /connectors/ingestion/lineage
---

# Lineage Ingestion

A large subset of connectors distributed with OpenMetadata include support for lineage ingestion. Lineage ingestion processes
queries to determine upstream and downstream entities for data assets. Lineage is published to the OpenMetadata catalog when metadata is ingested.

Using the OpenMetadata user interface and API, you may trace the path of data across Tables, Pipelines, and Dashboards.

![gif](/images/v0.13.2/openmetadata/ingestion/lineage/lineage-ingestion.gif)

Lineage ingestion is specific to the type of the Entity that we are processing. We are going to explain
the ingestion process for the supported services.

The team is continuously working to increase the lineage coverage of the available services. Do not hesitate
to [reach out](https://slack.open-metadata.org/) if you have any questions, issues or requests!

## Database Services

Here we have 3 lineage sources, divided in different workflows, but mostly built around a **Query Parser**.

### View Lineage

During the Metadata Ingestion workflow we differentiate if a Table is a View. For those sources where we can
obtain the query that generates the View (e.g., Snowflake allows us to pick up the View query from the DDL).

After all Tables have been ingested in the workflow, it's time to [parse](https://sqllineage.readthedocs.io/en/latest/)
all the queries generating Views. During the query parsing, we will obtain the source and target tables, search if the
Tables exist in OpenMetadata, and finally create the lineage relationship between the involved Entities.

Let's go over this process with an example. Suppose have the following DDL:

```sql
CREATE OR REPLACE VIEW schema.my_view
AS SELECT ... FROM schema.table_a JOIN another_schema.table_b;
```

From this query we will extract the following information:

**1.** There are two `source` tables, represented by the string `schema.table_a` as `another_schema.table_b`
**2.** There is a `target` table `schema.my_view`.

In this case we suppose that the database connection requires us to write the table names as `<schema>.<table>`. However,
there are other possible options. Sometimes we can find just `<table>` in a query, or even `<database>.<schema>.<table>`.

The point here is that we have limited information that we can use to identify the Table Entity that represents the
table written down in SQL. To close this gap, we run a query against ElasticSearch using the Table FQN.

Once we have identified all the ingredients in OpenMetadata as Entities, we can run the Lineage API to add the
relationship between the nodes.

![query-parser](/images/v0.13.2/openmetadata/ingestion/lineage/query-parser.png)

What we just described is the core process of identifying and ingesting lineage, and it will be reused (or partially reused)
for the rest of the options as well.

### dbt

When configuring an Ingestion adding dbt information we can parse the nodes on the Manifest JSON to get the data model
lineage. Here we don't need to parse a query to obtain the source and target elements, but we still rely on querying ElasticSearch
to identify the graph nodes as OpenMetadata Entities.

Note that if a Model is not materialized, its data won't be ingested.

### Query Log

{% note %}

Up until 0.11, Query Log analysis for lineage happens during the Usage Workflow.

From 0.12 onwards, there is a separated Lineage Workflow that will take care of this process.

{% /note %}

#### How to run?

The main difference here is between those sources that provide internal access to query logs and those that do not. For
services such as:

- [BigQuery](/connectors/database/bigquery)
- [Snowflake](/connectors/database/snowflake)
- [MSSQL](/connectors/database/mssql)
- [Redshift](/connectors/database/redshift)
- [Clickhouse](/connectors/database/clickhouse)
- [Postgres](/connectors/database/postgres)
- [Databricks](/connectors/database/databricks)

There are specific workflows (Usage & Lineage) that will use the query log information. An alternative for sources not
listed here is to run the workflow by providing the Query Logs that you have previously exported and then running
the [workflow](/connectors/ingestion/workflows/usage/usage-workflow-query-logs).

#### Process

That being said, this process is the same as the one shown in the View Lineage above. By obtaining a set of queries to
parse, we will obtain the `source` and `target` information, use ElasticSearch to identify the Entities in OpenMetadata
and then send the lineage to the API.

{% note %}

When running any query from within OpenMetadata we add an information comment to the query text

```
{"app": "OpenMetadata", "version": <openmetadata-ingestion version>}
```

Note that queries with this text as well as the ones containing headers from dbt (which follow a similar structure),
will be filtered out when building the query log internally.

{% /note %}

#### Troubleshooting

Make sure that the tables that you are trying to add lineage for are present in OpenMetadata (and their upstream/downstream
as well). You might also need to validate if the query logs are available in the tables for each service.

You can check the queries being used here:

- [BigQuery](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L428)
- [Snowflake](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L197)
- [MSSQL](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L350)
- [Redshift](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L18)
- [Clickhouse](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L376)
- [Postgres](https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/src/metadata/utils/sql_queries.py#L467)

By default, we apply a result limit of 1000 records. You might also need to increase that for databases with big volumes
of queries.

## Dashboard Services

When configuring the Ingestion Workflow for Dashboard Services you can select which Database Services are hosting
the data feeding the Dashboards and Charts.

When ingesting the Dashboards metadata, the workflow will pick up the origin tables (or database, in the case of
PowerBI), and prepare the lineage information.
{% image
  src="/images/v0.13.2/openmetadata/ingestion/lineage/dashboard-ingestion-lineage.png"
  alt="Dashboard Lineage"
  caption="Dashboard Lineage"
 /%}

## Pipeline Services

The supported services here are [Airflow](/connectors/pipeline/airflow),
[Fivetran](/connectors/pipeline/fivetran), [Dagster](/connectors/pipeline/dagster)
and [Airbyte](/connectors/pipeline/airbyte).

All of them ingest the lineage information out of the box. The only special case is Airflow, where one needs to
setup `inlets` and `outlets`. You can find more information about it
[here](https://docs.open-metadata.org/connectors/pipeline/airflow/lineage-backend#adding-lineage).

## Manual Lineage

Sometimes there is information that is shared among people but not present in the sources. To enable capturing all
the possible knowledge, you can also add lineage manually with our UI editor.

{%inlineCalloutContainer%}

{%inlineCallout
  icon="celebration"
  bold="Manual Lineage"
  href="/connectors/ingestion/lineage/edit-lineage-manually"%}
Capture Lineage knowledge with the UI editor.
{%/inlineCallout%}

{%/inlineCalloutContainer%}