Start adding java ETL examples, starting with kafka etl. (#1805)

Start adding java ETL examples, starting with kafka etl.

We've had a few requests to start providing Java examples rather than Python due to type safety.

I've also started to add these to metadata-ingestion-examples to make it clearer these are *examples*. They can be used directly or as a basis for other things.

As we port to Java we'll move examples to contrib.

README.md

@@ -46,7 +46,7 @@ Please follow the [DataHub Quickstart Guide](docs/quickstart.md) to get a copy o
 * [Frontend](datahub-frontend)
 * [Web App](datahub-web)
 * [Generalized Metadata Service](gms)
-* [Metadata Ingestion](metadata-ingestion)
+* [Metadata Ingestion](metadata-ingestion-examples)
 * [Metadata Processing Jobs](metadata-jobs)
 
 ## Releases

build.gradle

@@ -44,7 +44,8 @@ project.ext.externalDependency = [
     'httpClient': 'org.apache.httpcomponents:httpclient:4.5.9',
     'jacksonCore': 'com.fasterxml.jackson.core:jackson-core:2.9.7',
     'jacksonDataBind': 'com.fasterxml.jackson.core:jackson-databind:2.9.7',
-    "javatuples": "org.javatuples:javatuples:1.2",
+    'javatuples': 'org.javatuples:javatuples:1.2',
+    'javaxInject' : 'javax.inject:javax.inject:1',
     'jerseyCore': 'org.glassfish.jersey.core:jersey-client:2.25.1',
     'jerseyGuava': 'org.glassfish.jersey.bundles.repackaged:jersey-guava:2.25.1',
     'jsonSimple': 'com.googlecode.json-simple:json-simple:1.1.1',
@@ -57,8 +58,8 @@ project.ext.externalDependency = [
     'mariadbConnector': 'org.mariadb.jdbc:mariadb-java-client:2.6.0',
     'mockito': 'org.mockito:mockito-core:3.0.0',
     'mysqlConnector': 'mysql:mysql-connector-java:5.1.47',
-    "neo4jHarness": "org.neo4j.test:neo4j-harness:3.4.11",
-    "neo4jJavaDriver": "org.neo4j.driver:neo4j-java-driver:4.0.0",
+    'neo4jHarness': 'org.neo4j.test:neo4j-harness:3.4.11',
+    'neo4jJavaDriver': 'org.neo4j.driver:neo4j-java-driver:4.0.0',
     'parseqTest': 'com.linkedin.parseq:parseq:3.0.7:test',
     'playDocs': 'com.typesafe.play:play-docs_2.11:2.6.18',
     'playGuice': 'com.typesafe.play:play-guice_2.11:2.6.18',
@@ -66,7 +67,7 @@ project.ext.externalDependency = [
     'playTest': 'com.typesafe.play:play-test_2.11:2.6.18',
     'postgresql': 'org.postgresql:postgresql:42.2.14',
     'reflections': 'org.reflections:reflections:0.9.11',
-    "rythmEngine": "org.rythmengine:rythm-engine:1.3.0",
+    'rythmEngine': 'org.rythmengine:rythm-engine:1.3.0',
     'servletApi': 'javax.servlet:javax.servlet-api:3.1.0',
     'springBeans': 'org.springframework:spring-beans:5.2.3.RELEASE',
     'springContext': 'org.springframework:spring-context:5.2.3.RELEASE',

contrib/metadata-ingestion/python/README.md

@@ -0,0 +1,23 @@
+# Python ETL examples
+
+ETL scripts written in Python.
+
+## Prerequisites
+
+1. Before running any python metadata ingestion job, you should make sure that DataHub backend services are all running.
+The easiest way to do that is through [Docker images](../../docker).
+2. You also need to build the `mxe-schemas` module as below.
+   ```
+   ./gradlew :metadata-events:mxe-schemas:build
+   ```
+   This is needed to generate `MetadataChangeEvent.avsc` which is the schema for `MetadataChangeEvent` Kafka topic.
+3. All the scripts are written using Python 3 and most likely won't work with Python 2.x interpreters.
+   You can verify the version of your Python using the following command.
+   ```
+   python --version
+   ```
+   We recommend using [pyenv](https://github.com/pyenv/pyenv) to install and manage your Python environment.
+4. Before launching each ETL ingestion pipeline, you can install/verify the library versions as below.
+   ```
+   pip install --user -r requirements.txt
+   ```

contrib/metadata-ingestion/python/kafka-etl/README.md

@@ -0,0 +1,17 @@
+# Kafka ETL
+
+## Ingest metadata from Kafka to DataHub
+The kafka_etl provides you ETL channel to communicate with your kafka.
+```
+➜  Config your kafka environmental variable in the file.
+    ZOOKEEPER      # Your zookeeper host.
+    
+➜  Config your Kafka broker environmental variable in the file.
+    AVROLOADPATH   # Your model event in avro format.
+    KAFKATOPIC     # Your event topic.
+    BOOTSTRAP      # Kafka bootstrap server.
+    SCHEMAREGISTRY # Kafka schema registry host.
+
+➜  python kafka_etl.py
+```
+This will bootstrap DataHub with your metadata in the kafka as a dataset entity.

metadata-events/mxe-avro-1.7/build.gradle

@@ -12,19 +12,24 @@ dependencies {
   avsc project(':metadata-events:mxe-schemas')
 }
 
+def genDir = file("src/generated/java")
+
 task avroCodeGen(type: com.commercehub.gradle.plugin.avro.GenerateAvroJavaTask, dependsOn: configurations.avsc) {
   source("$rootDir/metadata-events/mxe-schemas/src/renamed/avro")
-  outputDir = file("src/generated/java")
+  outputDir = genDir
+  dependsOn(':metadata-events:mxe-schemas:renameNamespace')
 }
 
 compileJava.source(avroCodeGen.outputs)
-build.dependsOn avroCodeGen
 
-clean {
-  project.delete('src/generated')
+idea {
+  module {
+    sourceDirs += genDir
+    generatedSourceDirs += genDir
+  }
 }
 
-avroCodeGen.dependsOn(':metadata-events:mxe-schemas:renameNamespace')
+project.rootProject.tasks.idea.dependsOn(avroCodeGen)
 
 // Exclude classes from avro-schemas
 jar {

metadata-events/mxe-utils-avro-1.7/src/main/java/com/linkedin/metadata/EventUtils.java

@@ -53,7 +53,7 @@ public class EventUtils {
 
   @Nonnull
   private static Schema getAvroSchemaFromResource(@Nonnull String resourcePath) {
-    URL url = Resources.getResource(resourcePath);
+    URL url = EventUtils.class.getClassLoader().getResource(resourcePath);
     try {
       return Schema.parse(Resources.toString(url, Charsets.UTF_8));
     } catch (IOException e) {

metadata-ingestion-examples/README.md

@@ -0,0 +1,47 @@
+# Metadata Ingestion
+
+This directory contains example apps for ingesting data into DataHub.
+
+You are more than welcome to use these examples directly, or use them as a reference for you own jobs.
+
+See the READMEs of each example for more information on each.
+
+### Common themes
+
+All these examples ingest by firing MetadataChangeEvent Kafka events. They do not ingest directly into DataHub, though
+this is possible. Instead, the mce-consumer-job should be running, listening for these events, and perform the ingestion
+for us.
+
+### A note on languages
+
+We initially wrote these examples in Python (they still exist in `metadata-ingestion`; TODO to delete them once they're
+all ported). The idea was that these were very small example scripts, that should've been easy to use. However, upon
+reflection, not all developers are familiar with Python, and the lack of types can hinder development. So the decision
+was made to port the examples to Java.
+
+You're more than welcome to extrapolate these examples into whatever languages you like. At LinkedIn, we primarily use
+Java.
+
+### Ingestion at LinkedIn
+
+It is worth noting that we do not use any of these examples directly (in Java, Python, or anything else) at LinkedIn. We
+have several different pipelines for ingesting data; it all depends on the source.
+
+- Some pipelines are based off other Kafka events, where we'll transform some existing Kafka event to a metadata event.
+  - For example, we get Kafka events hive changes. We make MCEs out of those hive events to ingest hive data.
+- For others, we've directly instrumented existing pipelines / apps / jobs to also emit metadata events.
+  - For example, TODO? Gobblin?
+- For others still, we've created a series offline jobs to ingest data.
+  - For example, we have an Azkaban job to process our HDFS datasets.
+
+For some sources of data one of these example scripts may work fine. For others, it may make more sense to have some
+custom logic, like the above list. Namely, all these examples today are one-off (they run, fire events, and then stop),
+you may wish to build continuous ingestion pipelines instead.
+
+### "Real" Ingestion Applications
+
+We appreciate any contributions of apps you may wish to make to ingest data from other sources.
+
+TODO this section feels a little weird. Are our ingestion apps not really real apps? :p LDAP is real, as is kafka.
+Granted, these are just one off apps to ingest. Maybe we should provide a library for these, then expose the one off
+apps as examples?

metadata-ingestion-examples/common/build.gradle

@@ -0,0 +1,20 @@
+plugins {
+  id 'java'
+}
+
+dependencies {
+  compile project(':metadata-dao-impl:kafka-producer')
+
+  compile externalDependency.javaxInject
+  compile externalDependency.kafkaAvroSerde
+  compile externalDependency.kafkaSerializers
+  compile externalDependency.lombok
+  compile externalDependency.springBeans
+  compile externalDependency.springBootAutoconfigure
+  compile externalDependency.springCore
+  compile externalDependency.springKafka
+
+  annotationProcessor externalDependency.lombok
+
+  runtime externalDependency.logbackClassic
+}

metadata-ingestion-examples/common/src/main/java/com/linkedin/metadata/examples/configs/KafkaConfig.java

@@ -0,0 +1,42 @@
+package com.linkedin.metadata.examples.configs;
+
+import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig;
+import io.confluent.kafka.serializers.KafkaAvroSerializer;
+import java.util.Arrays;
+import java.util.Map;
+import org.apache.avro.generic.IndexedRecord;
+import org.apache.kafka.clients.producer.KafkaProducer;
+import org.apache.kafka.clients.producer.Producer;
+import org.apache.kafka.common.serialization.StringSerializer;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.boot.autoconfigure.kafka.KafkaProperties;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+
+@Configuration
+public class KafkaConfig {
+  @Value("${KAFKA_BOOTSTRAP_SERVER:localhost:29092}")
+  private String kafkaBootstrapServers;
+
+  @Value("${KAFKA_SCHEMAREGISTRY_URL:http://localhost:8081}")
+  private String kafkaSchemaRegistryUrl;
+
+  @Bean(name = "kafkaEventProducer")
+  public Producer<String, IndexedRecord> kafkaListenerContainerFactory(KafkaProperties properties) {
+    KafkaProperties.Producer producerProps = properties.getProducer();
+
+    producerProps.setKeySerializer(StringSerializer.class);
+    producerProps.setValueSerializer(KafkaAvroSerializer.class);
+
+    // KAFKA_BOOTSTRAP_SERVER has precedence over SPRING_KAFKA_BOOTSTRAP_SERVERS
+    if (kafkaBootstrapServers != null && kafkaBootstrapServers.length() > 0) {
+      producerProps.setBootstrapServers(Arrays.asList(kafkaBootstrapServers.split(",")));
+    } // else we rely on KafkaProperties which defaults to localhost:9092
+
+    Map<String, Object> props = properties.buildProducerProperties();
+    props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, kafkaSchemaRegistryUrl);
+
+    return new KafkaProducer<>(props);
+  }
+}

metadata-ingestion-examples/common/src/main/java/com/linkedin/metadata/examples/configs/SchemaRegistryConfig.java

@@ -0,0 +1,19 @@
+package com.linkedin.metadata.examples.configs;
+
+import io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient;
+import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+
+@Configuration
+public class SchemaRegistryConfig {
+  @Value("${SCHEMAREGISTRY_URL:http://localhost:8081}")
+  private String schemaRegistryUrl;
+
+  @Bean(name = "schemaRegistryClient")
+  public SchemaRegistryClient schemaRegistryFactory() {
+    return new CachedSchemaRegistryClient(schemaRegistryUrl, 512);
+  }
+}

metadata-ingestion-examples/common/src/main/java/com/linkedin/metadata/examples/configs/ZooKeeperConfig.java

@@ -0,0 +1,26 @@
+package com.linkedin.metadata.examples.configs;
+
+import java.io.IOException;
+import org.apache.zookeeper.Watcher;
+import org.apache.zookeeper.ZooKeeper;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+
+@Configuration
+public class ZooKeeperConfig {
+  @Value("${ZOOKEEPER:localhost:2181}")
+  private String zookeeper;
+
+  @Value("${ZOOKEEPER_TIMEOUT_MILLIS:3000}")
+  private int timeoutMillis;
+
+  @Bean(name = "zooKeeper")
+  public ZooKeeper zooKeeperFactory() throws IOException {
+    Watcher noopWatcher = event -> {
+    };
+
+    return new ZooKeeper(zookeeper, timeoutMillis, noopWatcher);
+  }
+}

metadata-ingestion-examples/kafka-etl/README.md

@@ -0,0 +1,40 @@
+# Kafka ETL
+
+A small application which reads existing Kafka topics from ZooKeeper, retrieves their schema from the schema registry,
+and then fires an MCE for each schema.
+
+## Running the Application
+
+First, ensure that services this depends on, like schema registry / zookeeper / mce-consumer-job / gms / etc, are all
+running.
+
+This application can be run via gradle:
+
+```
+./gradlew :metadata-ingestion-examples:kafka-etl:bootRun
+```
+
+Or by building and running the jar:
+
+```
+./gradlew :metadata-ingestion-examples:kafka-etl:build
+
+java -jar metadata-ingestion-examples/kafka-etl/build/libs/kafka-etl.jar 
+```
+
+### Environment Variables
+
+See the files under `src/main/java/com/linkedin/metadata/examples/kafka/config` for a list of customizable spring
+environment variables.
+
+### Common pitfalls
+
+For events to be fired correctly, schemas must exist in the schema registry. If a topic was newly created, but no schema
+has been registered for it yet, this application will fail to retrieve the schema for that topic. Check the output of
+the application to see if this happens. If you see a message like
+
+```
+io.confluent.kafka.schemaregistry.client.rest.exceptions.RestClientException: Subject not found.; error code: 40401
+```
+
+Then the odds are good that you need to register the schema for this topic.

metadata-ingestion-examples/kafka-etl/build.gradle

@@ -0,0 +1,29 @@
+plugins {
+  id 'org.springframework.boot'
+  id 'java'
+}
+
+dependencies {
+  compile project(':metadata-utils')
+  compile project(':metadata-builders')
+  compile project(':metadata-dao-impl:kafka-producer')
+  compile project(':metadata-events:mxe-schemas')
+  compile project(':metadata-ingestion-examples:common')
+
+  compile externalDependency.javaxInject
+  compile externalDependency.kafkaAvroSerde
+  compile externalDependency.kafkaSerializers
+  compile externalDependency.lombok
+  compile externalDependency.springBeans
+  compile externalDependency.springBootAutoconfigure
+  compile externalDependency.springCore
+  compile externalDependency.springKafka
+
+  annotationProcessor externalDependency.lombok
+
+  runtime externalDependency.logbackClassic
+}
+
+bootJar {
+  mainClassName = 'com.linkedin.metadata.examples.kafka.KafkaEtlApplication'
+}

metadata-ingestion-examples/kafka-etl/src/main/java/com/linkedin/metadata/examples/kafka/KafkaEtl.java

@@ -0,0 +1,115 @@
+package com.linkedin.metadata.examples.kafka;
+
+import com.linkedin.common.AuditStamp;
+import com.linkedin.common.FabricType;
+import com.linkedin.common.urn.CorpuserUrn;
+import com.linkedin.common.urn.DataPlatformUrn;
+import com.linkedin.common.urn.DatasetUrn;
+import com.linkedin.metadata.aspect.DatasetAspect;
+import com.linkedin.metadata.dao.producer.KafkaMetadataEventProducer;
+import com.linkedin.metadata.snapshot.DatasetSnapshot;
+import com.linkedin.mxe.MetadataChangeEvent;
+import com.linkedin.schema.KafkaSchema;
+import com.linkedin.schema.SchemaField;
+import com.linkedin.schema.SchemaFieldArray;
+import com.linkedin.schema.SchemaFieldDataType;
+import com.linkedin.schema.SchemaMetadata;
+import com.linkedin.schema.StringType;
+import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient;
+import java.util.List;
+import javax.inject.Inject;
+import javax.inject.Named;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.avro.generic.IndexedRecord;
+import org.apache.kafka.clients.producer.Producer;
+import org.apache.zookeeper.ZooKeeper;
+import org.springframework.boot.CommandLineRunner;
+import org.springframework.stereotype.Component;
+
+/**
+ * Gathers Kafka topics from the local zookeeper instance and schemas from the schema registry, and then fires
+ * MetadataChangeEvents for their schemas.
+ *
+ * <p>This should cause DataHub to be populated with this information, assuming it and the mce-consumer-job are running
+ * locally.
+ *
+ * <p>Can be run with {@code ./gradlew :metadata-ingestion-examples:java:kafka-etl:bootRun}.
+ */
+@Slf4j
+@Component
+public final class KafkaEtl implements CommandLineRunner {
+  private static final DataPlatformUrn KAFKA_URN = new DataPlatformUrn("kafka");
+
+  @Inject
+  @Named("kafkaEventProducer")
+  private Producer<String, IndexedRecord> _producer;
+
+  @Inject
+  @Named("zooKeeper")
+  private ZooKeeper _zooKeeper;
+
+  @Inject
+  @Named("schemaRegistryClient")
+  private SchemaRegistryClient _schemaRegistryClient;
+
+  private SchemaMetadata buildDatasetSchema(String datasetName, String schema, int schemaVersion) {
+    final AuditStamp auditStamp = new AuditStamp();
+    auditStamp.setTime(System.currentTimeMillis());
+    auditStamp.setActor(new CorpuserUrn(System.getenv("USER")));
+    final SchemaMetadata.PlatformSchema platformSchema = new SchemaMetadata.PlatformSchema();
+    platformSchema.setKafkaSchema(new KafkaSchema().setDocumentSchema(schema));
+    return new SchemaMetadata().setSchemaName(datasetName)
+        .setPlatform(KAFKA_URN)
+        .setCreated(auditStamp)
+        .setLastModified(auditStamp)
+        .setVersion(schemaVersion)
+        .setHash("")
+        .setPlatformSchema(platformSchema)
+        .setFields(new SchemaFieldArray(new SchemaField().setFieldPath("")
+            .setDescription("")
+            .setNativeDataType("string")
+            .setType(new SchemaFieldDataType().setType(SchemaFieldDataType.Type.create(new StringType())))));
+  }
+
+  private void produceKafkaDatasetMce(SchemaMetadata schemaMetadata) {
+    MetadataChangeEvent.class.getClassLoader().getResource("avro/com/linkedin/mxe/MetadataChangeEvent.avsc");
+
+    // Kafka topics are considered datasets in the current DataHub metadata ecosystem.
+    final KafkaMetadataEventProducer<DatasetSnapshot, DatasetAspect, DatasetUrn> eventProducer =
+        new KafkaMetadataEventProducer<>(DatasetSnapshot.class, DatasetAspect.class, _producer);
+    eventProducer.produceSnapshotBasedMetadataChangeEvent(
+        new DatasetUrn(KAFKA_URN, schemaMetadata.getSchemaName(), FabricType.PROD), schemaMetadata);
+    _producer.flush();
+  }
+
+  @Override
+  public void run(String... args) throws Exception {
+    log.info("Starting up");
+
+    final List<String> topics = _zooKeeper.getChildren("/brokers/topics", false);
+    for (String datasetName : topics) {
+      if (datasetName.startsWith("_")) {
+        continue;
+      }
+
+      final String topic = datasetName + "-value";
+      io.confluent.kafka.schemaregistry.client.SchemaMetadata schemaMetadata;
+      try {
+        schemaMetadata = _schemaRegistryClient.getLatestSchemaMetadata(topic);
+      } catch (Throwable t) {
+        log.error("Failed to get schema for topic " + datasetName, t);
+        log.error("Common failure: does this event schema exist in the schema registry?");
+        continue;
+      }
+
+      if (schemaMetadata == null) {
+        log.warn(String.format("Skipping topic without schema: %s", topic));
+        continue;
+      }
+      log.trace(topic);
+
+      produceKafkaDatasetMce(buildDatasetSchema(datasetName, schemaMetadata.getSchema(), schemaMetadata.getVersion()));
+      log.info("Successfully fired MCE for " + datasetName);
+    }
+  }
+}

metadata-ingestion-examples/kafka-etl/src/main/java/com/linkedin/metadata/examples/kafka/KafkaEtlApplication.java

@@ -0,0 +1,16 @@
+package com.linkedin.metadata.examples.kafka;
+
+import org.springframework.boot.WebApplicationType;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+import org.springframework.boot.autoconfigure.elasticsearch.rest.RestClientAutoConfiguration;
+import org.springframework.boot.builder.SpringApplicationBuilder;
+
+
+@SuppressWarnings("checkstyle:HideUtilityClassConstructor")
+@SpringBootApplication(exclude = {RestClientAutoConfiguration.class}, scanBasePackages = {
+    "com.linkedin.metadata.examples.configs", "com.linkedin.metadata.examples.kafka"})
+public class KafkaEtlApplication {
+  public static void main(String[] args) {
+    new SpringApplicationBuilder(KafkaEtlApplication.class).web(WebApplicationType.NONE).run(args);
+  }
+}

metadata-ingestion-examples/kafka-etl/src/main/resources/logback.xml

@@ -0,0 +1,40 @@
+<configuration>
+    <property name="LOG_DIR" value="${LOG_DIR:- /tmp/datahub/logs}"/>
+
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder>
+            <pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
+        </encoder>
+    </appender>
+
+    <appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">
+        <file>${LOG_DIR}/kafka-etl-java.log</file>
+        <append>true</append>
+        <encoder>
+            <pattern>%d{HH:mm:ss} [%thread] %-5level %logger{36} - %msg%n</pattern>
+        </encoder>
+        <rollingPolicy class="ch.qos.logback.core.rolling.FixedWindowRollingPolicy">
+            <FileNamePattern>${LOG_DIR}/kafka-etl.%i.log</FileNamePattern>
+            <minIndex>1</minIndex>
+            <maxIndex>3</maxIndex>
+        </rollingPolicy>
+        <triggeringPolicy class="ch.qos.logback.core.rolling.SizeBasedTriggeringPolicy">
+            <MaxFileSize>100MB</MaxFileSize>
+        </triggeringPolicy>
+    </appender>
+
+    <logger name="org.apache.kafka.clients" level="warn" additivity="false">
+        <appender-ref ref="STDOUT" />
+        <appender-ref ref="FILE"/>
+    </logger>
+
+    <logger name="com.linkedin.metadata.examples.kafka" level="info" additivity="false">
+        <appender-ref ref="STDOUT" />
+        <appender-ref ref="FILE"/>
+    </logger>
+
+    <root level="warn">
+        <appender-ref ref="STDOUT" />
+        <appender-ref ref="FILE"/>
+    </root>
+</configuration>

metadata-ingestion/README.md

@@ -90,22 +90,6 @@ The ldap_etl provides you ETL channel to communicate with your LDAP server.
 ```
 This will bootstrap DataHub with your metadata in the LDAP server as an user entity.
 
-## Ingest metadata from Kafka to DataHub
-The kafka_etl provides you ETL channel to communicate with your kafka.
-```
-➜  Config your kafka environmental variable in the file.
-    ZOOKEEPER      # Your zookeeper host.
-    
-➜  Config your Kafka broker environmental variable in the file.
-    AVROLOADPATH   # Your model event in avro format.
-    KAFKATOPIC     # Your event topic.
-    BOOTSTRAP      # Kafka bootstrap server.
-    SCHEMAREGISTRY # Kafka schema registry host.
-
-➜  python kafka_etl.py
-```
-This will bootstrap DataHub with your metadata in the kafka as a dataset entity.
-
 ## Ingest metadata from MySQL to DataHub
 The mysql_etl provides you ETL channel to communicate with your MySQL.
 ```

settings.gradle

@@ -18,6 +18,8 @@ include 'metadata-events:mxe-avro-1.7'
 include 'metadata-events:mxe-registration'
 include 'metadata-events:mxe-schemas'
 include 'metadata-events:mxe-utils-avro-1.7'
+include 'metadata-ingestion-examples:common'
+include 'metadata-ingestion-examples:kafka-etl'
 include 'metadata-jobs:mae-consumer-job'
 include 'metadata-jobs:mce-consumer-job'
 include 'metadata-models'