feat(integration/fivetran): Fivetran connector integration

datahub-web-react/src/app/ingest/source/builder/constants.ts

@@ -29,6 +29,7 @@ import databricksLogo from '../../../../images/databrickslogo.png';
 import verticaLogo from '../../../../images/verticalogo.png';
 import mlflowLogo from '../../../../images/mlflowlogo.png';
 import dynamodbLogo from '../../../../images/dynamodblogo.png';
+import fivetranLogo from '../../../../images/fivetranlogo.png';
 
 export const ATHENA = 'athena';
 export const ATHENA_URN = `urn:li:dataPlatform:${ATHENA}`;
@@ -105,6 +106,8 @@ export const DBT_CLOUD = 'dbt-cloud';
 export const DBT_CLOUD_URN = `urn:li:dataPlatform:dbt`;
 export const VERTICA = 'vertica';
 export const VERTICA_URN = `urn:li:dataPlatform:${VERTICA}`;
+export const FIVETRAN = 'fivetran';
+export const FIVETRAN_URN = `urn:li:dataPlatform:${FIVETRAN}`;
 
 export const PLATFORM_URN_TO_LOGO = {
     [ATHENA_URN]: athenaLogo,
@@ -138,6 +141,7 @@ export const PLATFORM_URN_TO_LOGO = {
     [SUPERSET_URN]: supersetLogo,
     [UNITY_CATALOG_URN]: databricksLogo,
     [VERTICA_URN]: verticaLogo,
+    [FIVETRAN_URN]: fivetranLogo,
 };
 
 export const SOURCE_TO_PLATFORM_URN = {

datahub-web-react/src/app/ingest/source/builder/sources.json

@@ -216,6 +216,13 @@
         "docsUrl": "https://datahubproject.io/docs/generated/ingestion/sources/vertica/",
         "recipe": "source:\n    type: vertica\n    config:\n        # Coordinates\n        host_port: localhost:5433\n        # The name of the vertica database\n        database: Database_Name\n        # Credentials\n        username: Vertica_User\n        password: Vertica_Password\n\n        include_tables: true\n        include_views: true\n        include_projections: true\n        include_models: true\n        include_view_lineage: true\n        include_projection_lineage: true\n        profiling:\n            enabled: false\n        stateful_ingestion:\n            enabled: true  "
     },
+    {
+        "urn": "urn:li:dataPlatform:fivetran",
+        "name": "fivetran",
+        "displayName": "Fivetran",
+        "docsUrl": "https://datahubproject.io/docs/generated/ingestion/sources/fivetran/",
+        "recipe": "source:\n    type: fivetran\n    config:\n        # Fivetran log connector destination server configurations\n        fivetran_log_config:\n            destination_platform: snowflake\n            destination_config:\n                # Coordinates\n                account_id: snowflake_account_id\n                warehouse: warehouse_name\n                database: snowflake_db\n                log_schema: fivetran_log_schema\n\n                # Credentials\n                username: ${SNOWFLAKE_USER}\n                password: ${SNOWFLAKE_PASS}\n                role: snowflake_role\n\n        # Optional - filter for certain connector names instead of ingesting everything.\n        # connector_patterns:\n        #     allow:\n        #         - connector_name\n\n        # Optional -- This mapping is optional and only required to configure platform-instance for source\n        # A mapping of Fivetran connector id to data platform instance\n        # sources_to_platform_instance:\n        #     calendar_elected:\n        #         platform_instance: cloud_postgres_instance\n        #         env: DEV\n\n        # Optional -- This mapping is optional and only required to configure platform-instance for destination.\n        # A mapping of Fivetran destination id to data platform instance\n        # destination_to_platform_instance:\n        #     calendar_elected:\n        #         platform_instance: cloud_postgres_instance\n        #         env: DEV"
+    },
     {
         "urn": "urn:li:dataPlatform:custom",
         "name": "custom",

metadata-ingestion/docs/sources/fivetran/fivetran_pre.md

@@ -0,0 +1,86 @@
+## Integration Details
+
+This source extracts the following:
+
+- Connectors in fivetran as Data Pipelines and Data Jobs to represent data lineage information between source and destination.
+- Connector sources - DataJob input Datasets.
+- Connector destination - DataJob output Datasets.
+- Connector runs - DataProcessInstances as DataJob runs.
+
+## Configuration Notes
+
+1. Fivetran supports the fivetran platform connector to dump the log events and connectors, destinations, users and roles metadata in your destination.
+2. You need to setup and start the initial sync of the fivetran platform connector before using this source. Refer [link](https://fivetran.com/docs/logs/fivetran-platform/setup-guide).
+3. Once initial sync up of your fivetran platform connector is done, you need to provide the fivetran platform connector's destination platform and its configuration in the recipe.
+
+## Concept mapping 
+
+| Fivetran		   | Datahub												    |
+|--------------------------|--------------------------------------------------------------------------------------------------------|
+| `Connector`              | [DataJob](https://datahubproject.io/docs/generated/metamodel/entities/datajob/)       	            |
+| `Source`                 | [Dataset](https://datahubproject.io/docs/generated/metamodel/entities/dataset/)                        |
+| `Destination`            | [Dataset](https://datahubproject.io/docs/generated/metamodel/entities/dataset/)                        |
+| `Connector Run`          | [DataProcessInstance](https://datahubproject.io/docs/generated/metamodel/entities/dataprocessinstance) |
+
+Source and destination are mapped to Dataset as an Input and Output of Connector.
+
+## Current limitations
+
+Works only for Snowflake destination for now.
+
+## Snowflake destination Configuration Guide
+1. If your fivetran platform connector destination is snowflake, you need to provide user details and its role with correct privileges in order to fetch metadata.
+2. Snowflake system admin can follow this guide to create a fivetran_datahub role, assign it the required privileges, and assign it to a user by executing the following Snowflake commands from a user with the ACCOUNTADMIN role or MANAGE GRANTS privilege.
+
+```sql
+create or replace role fivetran_datahub;
+
+// Grant access to a warehouse to run queries to view metadata
+grant operate, usage on warehouse "<your-warehouse>" to role fivetran_datahub;
+
+// Grant access to view database and schema in which your log and metadata tables exist
+grant usage on DATABASE "<fivetran-log-database>" to role fivetran_datahub;
+grant usage on SCHEMA "<fivetran-log-database>"."<fivetran-log-schema>" to role fivetran_datahub;
+
+// Grant access to execute select query on schema in which your log and metadata tables exist
+grant select on all tables in SCHEMA "<fivetran-log-database>"."<fivetran-log-schema>" to role fivetran_datahub;
+
+// Grant the fivetran_datahub to the snowflake user.
+grant role fivetran_datahub to user snowflake_user;
+```
+
+## Advanced Configurations
+
+### Working with Platform Instances
+If you've multiple instances of source/destination systems that are referred in your `fivetran` setup, you'd need to configure platform instance for these systems in `fivetran` recipe to generate correct lineage edges. Refer the document [Working with Platform Instances](https://datahubproject.io/docs/platform-instances) to understand more about this.
+
+While configuration of platform instance for source system you need to provide connector id as key and for destination system provide destination id as key.
+
+#### Example - Multiple Postgres Source Connectors each reading from different postgres instance
+```yml
+    # Map of connector source to platform instance
+    sources_to_platform_instance:
+      postgres_connector_id1: 
+        platform_instance: cloud_postgres_instance
+        env: PROD
+
+      postgres_connector_id2:
+        platform_instance: local_postgres_instance
+        env: DEV
+```
+
+#### Example - Multiple Snowflake Destinations each writing to different snowflake instance
+```yml
+    # Map of destination to platform instance
+    destination_to_platform_instance:
+      snowflake_destination_id1: 
+        platform_instance: prod_snowflake_instance
+        env: PROD
+
+      snowflake_destination_id2:
+        platform_instance: dev_snowflake_instance
+        env: PROD
+```
+
+
+

metadata-ingestion/docs/sources/fivetran/fivetran_recipe.yml

@@ -0,0 +1,43 @@
+source:
+  type: fivetran
+  config:
+    # Fivetran log connector destination server configurations
+    fivetran_log_config:
+      destination_platform: snowflake
+      destination_config:
+        # Coordinates
+        account_id: "abc48144"
+        warehouse: "COMPUTE_WH"
+        database: "MY_SNOWFLAKE_DB"
+        log_schema: "FIVETRAN_LOG"
+
+        # Credentials
+        username: "${SNOWFLAKE_USER}"
+        password: "${SNOWFLAKE_PASS}"
+        role: "snowflake_role"
+
+    # Optional - filter for certain connector names instead of ingesting everything.
+    # connector_patterns:
+    #   allow:
+    #     - connector_name
+
+    # Optional -- A mapping of the connector's all sources to its database.
+    # sources_to_database:
+    #   connector_id: source_db
+
+    # Optional -- This mapping is optional and only required to configure platform-instance for source
+    # A mapping of Fivetran connector id to data platform instance
+    # sources_to_platform_instance:
+    #   connector_id:
+    #     platform_instance: cloud_instance
+    #     env: DEV
+
+    # Optional -- This mapping is optional and only required to configure platform-instance for destination.
+    # A mapping of Fivetran destination id to data platform instance
+    # destination_to_platform_instance:
+    #   destination_id:
+    #     platform_instance: cloud_instance
+    #     env: DEV
+
+sink:
+  # sink configs

metadata-ingestion/setup.py

@@ -395,6 +395,7 @@
     "powerbi-report-server": powerbi_report_server,
     "vertica": sql_common | {"vertica-sqlalchemy-dialect[vertica-python]==0.0.8.1"},
     "unity-catalog": databricks | sqllineage_lib,
+    "fivetran": snowflake_common,
 }
 
 # This is mainly used to exclude plugins from the Docker image.
@@ -525,6 +526,7 @@
             "nifi",
             "vertica",
             "mode",
+            "fivetran",
             "kafka-connect",
         ]
         if plugin
@@ -629,6 +631,7 @@
         "unity-catalog = datahub.ingestion.source.unity.source:UnityCatalogSource",
         "gcs = datahub.ingestion.source.gcs.gcs_source:GCSSource",
         "sql-queries = datahub.ingestion.source.sql_queries:SqlQueriesSource",
+        "fivetran = datahub.ingestion.source.fivetran.fivetran:FivetranSource",
     ],
     "datahub.ingestion.transformer.plugins": [
         "simple_remove_dataset_ownership = datahub.ingestion.transformer.remove_dataset_ownership:SimpleRemoveDatasetOwnership",

metadata-ingestion/src/datahub/api/entities/datajob/datajob.py

@@ -100,7 +100,9 @@ def generate_tags_aspect(self) -> Iterable[GlobalTagsClass]:
         )
         return [tags]
 
-    def generate_mcp(self) -> Iterable[MetadataChangeProposalWrapper]:
+    def generate_mcp(
+        self, materialize_iolets: bool = True
+    ) -> Iterable[MetadataChangeProposalWrapper]:
         mcp = MetadataChangeProposalWrapper(
             entityUrn=str(self.urn),
             aspect=DataJobInfoClass(
@@ -113,7 +115,9 @@ def generate_mcp(self) -> Iterable[MetadataChangeProposalWrapper]:
         )
         yield mcp
 
-        yield from self.generate_data_input_output_mcp()
+        yield from self.generate_data_input_output_mcp(
+            materialize_iolets=materialize_iolets
+        )
 
         for owner in self.generate_ownership_aspect():
             mcp = MetadataChangeProposalWrapper(
@@ -144,7 +148,9 @@ def emit(
         for mcp in self.generate_mcp():
             emitter.emit(mcp, callback)
 
-    def generate_data_input_output_mcp(self) -> Iterable[MetadataChangeProposalWrapper]:
+    def generate_data_input_output_mcp(
+        self, materialize_iolets: bool
+    ) -> Iterable[MetadataChangeProposalWrapper]:
         mcp = MetadataChangeProposalWrapper(
             entityUrn=str(self.urn),
             aspect=DataJobInputOutputClass(
@@ -157,10 +163,9 @@ def generate_data_input_output_mcp(self) -> Iterable[MetadataChangeProposalWrapp
         yield mcp
 
         # Force entity materialization
-        for iolet in self.inlets + self.outlets:
-            mcp = MetadataChangeProposalWrapper(
-                entityUrn=str(iolet),
-                aspect=StatusClass(removed=False),
-            )
-
-            yield mcp
+        if materialize_iolets:
+            for iolet in self.inlets + self.outlets:
+                yield MetadataChangeProposalWrapper(
+                    entityUrn=str(iolet),
+                    aspect=StatusClass(removed=False),
+                )

metadata-ingestion/src/datahub/api/entities/dataprocess/dataprocess_instance.py

@@ -220,12 +220,10 @@ def emit_process_end(
             self._emit_mcp(mcp, emitter, callback)
 
     def generate_mcp(
-        self, created_ts_millis: Optional[int] = None
+        self, created_ts_millis: Optional[int] = None, materialize_iolets: bool = True
     ) -> Iterable[MetadataChangeProposalWrapper]:
-        """
-        Generates mcps from the object
-        :rtype: Iterable[MetadataChangeProposalWrapper]
-        """
+        """Generates mcps from the object"""
+
         mcp = MetadataChangeProposalWrapper(
             entityUrn=str(self.urn),
             aspect=DataProcessInstanceProperties(
@@ -253,7 +251,7 @@ def generate_mcp(
         )
         yield mcp
 
-        yield from self.generate_inlet_outlet_mcp()
+        yield from self.generate_inlet_outlet_mcp(materialize_iolets=materialize_iolets)
 
     @staticmethod
     def _emit_mcp(
@@ -329,7 +327,9 @@ def from_dataflow(dataflow: DataFlow, id: str) -> "DataProcessInstance":
         dpi._template_object = dataflow
         return dpi
 
-    def generate_inlet_outlet_mcp(self) -> Iterable[MetadataChangeProposalWrapper]:
+    def generate_inlet_outlet_mcp(
+        self, materialize_iolets: bool
+    ) -> Iterable[MetadataChangeProposalWrapper]:
         if self.inlets:
             mcp = MetadataChangeProposalWrapper(
                 entityUrn=str(self.urn),
@@ -349,10 +349,9 @@ def generate_inlet_outlet_mcp(self) -> Iterable[MetadataChangeProposalWrapper]:
             yield mcp
 
         # Force entity materialization
-        for iolet in self.inlets + self.outlets:
-            mcp = MetadataChangeProposalWrapper(
-                entityUrn=str(iolet),
-                aspect=StatusClass(removed=False),
-            )
-
-            yield mcp
+        if materialize_iolets:
+            for iolet in self.inlets + self.outlets:
+                yield MetadataChangeProposalWrapper(
+                    entityUrn=str(iolet),
+                    aspect=StatusClass(removed=False),
+                )

metadata-ingestion/src/datahub/emitter/mcp.py

@@ -240,7 +240,7 @@ def from_obj_require_wrapper(
         return mcp
 
     def as_workunit(
-        self, *, treat_errors_as_warnings: bool = False
+        self, *, treat_errors_as_warnings: bool = False, is_primary_source: bool = True
     ) -> "MetadataWorkUnit":
         from datahub.ingestion.api.workunit import MetadataWorkUnit
 
@@ -254,10 +254,12 @@ def as_workunit(
                 id=f"{self.entityUrn}-{self.aspectName}-{ts}",
                 mcp=self,
                 treat_errors_as_warnings=treat_errors_as_warnings,
+                is_primary_source=is_primary_source,
             )
 
         return MetadataWorkUnit(
             id=f"{self.entityUrn}-{self.aspectName}",
             mcp=self,
             treat_errors_as_warnings=treat_errors_as_warnings,
+            is_primary_source=is_primary_source,
         )

metadata-ingestion/src/datahub/ingestion/api/source_helpers.py

@@ -17,6 +17,7 @@
 from datahub.configuration.time_window_config import BaseTimeWindowConfig
 from datahub.emitter.mce_builder import make_dataplatform_instance_urn
 from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.mcp_builder import entity_supports_aspect
 from datahub.ingestion.api.workunit import MetadataWorkUnit
 from datahub.metadata.schema_classes import (
     BrowsePathEntryClass,
@@ -64,9 +65,9 @@ def auto_status_aspect(
     """
     For all entities that don't have a status aspect, add one with removed set to false.
     """
-
     all_urns: Set[str] = set()
     status_urns: Set[str] = set()
+    skip_urns: Set[str] = set()
     for wu in stream:
         urn = wu.get_urn()
         all_urns.add(urn)
@@ -89,9 +90,17 @@ def auto_status_aspect(
         else:
             raise ValueError(f"Unexpected type {type(wu.metadata)}")
 
+        if not isinstance(
+            wu.metadata, MetadataChangeEventClass
+        ) and not entity_supports_aspect(wu.metadata.entityType, StatusClass):
+            # If any entity does not support aspect 'status' then skip that entity from adding status aspect.
+            # Example like dataProcessInstance doesn't suppport status aspect.
+            # If not skipped gives error: java.lang.RuntimeException: Unknown aspect status for entity dataProcessInstance
+            skip_urns.add(urn)
+
         yield wu
 
-    for urn in sorted(all_urns - status_urns):
+    for urn in sorted(all_urns - status_urns - skip_urns):
         yield MetadataChangeProposalWrapper(
             entityUrn=urn,
             aspect=StatusClass(removed=False),