# Tutorial: Create a Studio notebook in Managed Service for Apache Flink The following tutorial demonstrates how to create a Studio notebook that reads data from a Kinesis data stream or an Amazon MSK cluster. **Topics** + [ ## Complete the prerequisites ](#example-notebook-setup) + [ ## Create an AWS Glue database ](#example-notebook-glue) + [ ## Next steps: Create a Studio notebook with Kinesis Data Streams or Amazon MSK ](#examples-notebook-nextsteps) + [ # Create a Studio notebook with Kinesis Data Streams ](example-notebook-streams.md) + [ # Create a Studio notebook with Amazon MSK ](example-notebook-msk.md) + [ # Clean up your application and dependent resources ](example-notebook-cleanup.md) ## Complete the prerequisites Make sure that your AWS CLI is version 2 or later. To install the latest AWS CLI, see [ Installing, updating, and uninstalling the AWS CLI version 2](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html). ## Create an AWS Glue database Your Studio notebook uses an [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) database for metadata about your Amazon MSK data source. **Create an AWS Glue Database** 1. Open the AWS Glue console at [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/). 1. Choose **Add database**. In the **Add database** window, enter **default** for **Database name**. Choose **Create**. ## Next steps: Create a Studio notebook with Kinesis Data Streams or Amazon MSK With this tutorial, you can create a Studio notebook that uses either Kinesis Data Streams or Amazon MSK: + [Create a Studio notebook with Kinesis Data Streams](example-notebook-streams.md) : With Kinesis Data Streams, you quickly create an application that uses a Kinesis data stream as a source. You only need to create a Kinesis data stream as a dependent resource. + [Create a Studio notebook with Amazon MSK](example-notebook-msk.md) : With Amazon MSK, you create an application that uses a Amazon MSK cluster as a source. You need to create an Amazon VPC, an Amazon EC2 client instance, and an Amazon MSK cluster as dependent resources. # Create a Studio notebook with Kinesis Data Streams This tutorial describes how to create a Studio notebook that uses a Kinesis data stream as a source. **Topics** + [ ## Complete the prerequisites ](#example-notebook-streams-setup) + [ ## Create an AWS Glue table ](#example-notebook-streams-glue) + [ ## Create a Studio notebook with Kinesis Data Streams ](#example-notebook-streams-create) + [ ## Send data to your Kinesis data stream ](#example-notebook-streams-send) + [ ## Test your Studio notebook ](#example-notebook-streams-test) ## Complete the prerequisites Before you create a Studio notebook, create a Kinesis data stream (`ExampleInputStream`). Your application uses this stream for the application source. You can create this stream using either the Amazon Kinesis console or the following AWS CLI command. For console instructions, see [Creating and Updating Data Streams](https://docs.aws.amazon.com/kinesis/latest/dev/amazon-kinesis-streams.html) in the *Amazon Kinesis Data Streams Developer Guide*. Name the stream **ExampleInputStream** and set the **Number of open shards** to **1**. To create the stream (`ExampleInputStream`) using the AWS CLI, use the following Amazon Kinesis `create-stream` AWS CLI command. ``` $ aws kinesis create-stream \ --stream-name ExampleInputStream \ --shard-count 1 \ --region us-east-1 \ --profile adminuser ``` ## Create an AWS Glue table Your Studio notebook uses an [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) database for metadata about your Kinesis Data Streams data source. **Note** You can either manually create the database first or you can let Managed Service for Apache Flink create it for you when you create the notebook. Similarly, you can either manually create the table as described in this section, or you can use the create table connector code for Managed Service for Apache Flink in your notebook within Apache Zeppelin to create your table via a DDL statement. You can then check in AWS Glue to make sure the table was correctly created. **Create a Table** 1. Sign in to the AWS Management Console and open the AWS Glue console at [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/). 1. If you don't already have a AWS Glue database, choose **Databases** from the left navigation bar. Choose **Add Database**. In the **Add database** window, enter **default** for **Database name**. Choose **Create**. 1. In the left navigation bar, choose **Tables**. In the **Tables** page, choose **Add tables**, **Add table manually**. 1. In the **Set up your table's properties** page, enter **stock** for the **Table name**. Make sure you select the database you created previously. Choose **Next**. 1. In the **Add a data store** page, choose **Kinesis**. For the **Stream name**, enter **ExampleInputStream**. For **Kinesis source URL**, choose enter **https://kinesis.us-east-1.amazonaws.com**. If you copy and paste the **Kinesis source URL**, be sure to delete any leading or trailing spaces. Choose **Next**. 1. In the **Classification** page, choose **JSON**. Choose **Next**. 1. In the **Define a Schema** page, choose Add Column to add a column. Add columns with the following properties: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/managed-flink/latest/java/example-notebook-streams.html) Choose **Next**. 1. On the next page, verify your settings, and choose **Finish**. 1. Choose your newly created table from the list of tables. 1. Choose **Edit table** and add a property with the key `managed-flink.proctime` and the value `proctime`. 1. Choose **Apply**. ## Create a Studio notebook with Kinesis Data Streams Now that you have created the resources your application uses, you create your Studio notebook. **Topics** + [ ### Create a Studio notebook using the AWS Management Console ](#example-notebook-create-streams-console) + [ ### Create a Studio notebook using the AWS CLI ](#example-notebook-msk-create-api) ### Create a Studio notebook using the AWS Management Console 1. Open the Managed Service for Apache Flink console at [ https://console.aws.amazon.com/managed-flink/home?region=us-east-1\$1/applications/dashboard](https://console.aws.amazon.com/managed-flink/home?region=us-east-1#/applications/dashboard). 1. In the **Managed Service for Apache Flink applications** page, choose the **Studio** tab. Choose **Create Studio notebook**. **Note** You can also create a Studio notebook from the Amazon MSK or Kinesis Data Streams consoles by selecting your input Amazon MSK cluster or Kinesis data stream, and choosing **Process data in real time**. 1. In the **Create Studio notebook** page, provide the following information: + Enter **MyNotebook** for the name of the notebook. + Choose **default** for **AWS Glue database**. Choose **Create Studio notebook**. 1. In the **MyNotebook** page, choose **Run**. Wait for the **Status** to show **Running**. Charges apply when the notebook is running. ### Create a Studio notebook using the AWS CLI To create your Studio notebook using the AWS CLI, do the following: 1. Verify your account ID. You need this value to create your application. 1. Create the role `arn:aws:iam::AccountID:role/ZeppelinRole` and add the following permissions to the auto-created role by console. `"kinesis:GetShardIterator",` `"kinesis:GetRecords",` `"kinesis:ListShards"` 1. Create a file called `create.json` with the following contents. Replace the placeholder values with your information. ``` { "ApplicationName": "MyNotebook", "RuntimeEnvironment": "ZEPPELIN-FLINK-3_0", "ApplicationMode": "INTERACTIVE", "ServiceExecutionRole": "arn:aws:iam::AccountID:role/ZeppelinRole", "ApplicationConfiguration": { "ApplicationSnapshotConfiguration": { "SnapshotsEnabled": false }, "ZeppelinApplicationConfiguration": { "CatalogConfiguration": { "GlueDataCatalogConfiguration": { "DatabaseARN": "arn:aws:glue:us-east-1:AccountID:database/default" } } } } } ``` 1. Run the following command to create your application: ``` aws kinesisanalyticsv2 create-application --cli-input-json file://create.json ``` 1. When the command completes, you see output that shows the details for your new Studio notebook. The following is an example of the output. ``` { "ApplicationDetail": { "ApplicationARN": "arn:aws:kinesisanalyticsus-east-1:012345678901:application/MyNotebook", "ApplicationName": "MyNotebook", "RuntimeEnvironment": "ZEPPELIN-FLINK-3_0", "ApplicationMode": "INTERACTIVE", "ServiceExecutionRole": "arn:aws:iam::012345678901:role/ZeppelinRole", ... ``` 1. Run the following command to start your application. Replace the sample value with your account ID. ``` aws kinesisanalyticsv2 start-application --application-arn arn:aws:kinesisanalyticsus-east-1:012345678901:application/MyNotebook\ ``` ## Send data to your Kinesis data stream To send test data to your Kinesis data stream, do the following: 1. Open the [ Kinesis Data Generator](https://awslabs.github.io/amazon-kinesis-data-generator/web/help.html). 1. Choose **Create a Cognito User with CloudFormation**. 1. The CloudFormation console opens with the Kinesis Data Generator template. Choose **Next**. 1. In the **Specify stack details** page, enter a username and password for your Cognito user. Choose **Next**. 1. In the **Configure stack options** page, choose **Next**. 1. In the **Review Kinesis-Data-Generator-Cognito-User** page, choose the **I acknowledge that AWS CloudFormation might create IAM resources.** checkbox. Choose **Create Stack**. 1. Wait for the CloudFormation stack to finish being created. After the stack is complete, open the **Kinesis-Data-Generator-Cognito-User** stack in the CloudFormation console, and choose the **Outputs** tab. Open the URL listed for the **KinesisDataGeneratorUrl** output value. 1. In the **Amazon Kinesis Data Generator** page, log in with the credentials you created in step 4. 1. On the next page, provide the following values: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/managed-flink/latest/java/example-notebook-streams.html) For **Record Template**, paste the following code: ``` { "ticker": "{{random.arrayElement( ["AMZN","MSFT","GOOG"] )}}", "price": {{random.number( { "min":10, "max":150 } )}} } ``` 1. Choose **Send data**. 1. The generator will send data to your Kinesis data stream. Leave the generator running while you complete the next section. ## Test your Studio notebook In this section, you use your Studio notebook to query data from your Kinesis data stream. 1. Open the Managed Service for Apache Flink console at [ https://console.aws.amazon.com/managed-flink/home?region=us-east-1\$1/applications/dashboard](https://console.aws.amazon.com/managed-flink/home?region=us-east-1#/applications/dashboard). 1. On the **Managed Service for Apache Flink applications** page, choose the **Studio notebook** tab. Choose **MyNotebook**. 1. In the **MyNotebook** page, choose **Open in Apache Zeppelin**. The Apache Zeppelin interface opens in a new tab. 1. In the **Welcome to Zeppelin\$1** page, choose **Zeppelin Note**. 1. In the **Zeppelin Note** page, enter the following query into a new note: ``` %flink.ssql(type=update) select * from stock ``` Choose the run icon. After a short time, the note displays data from the Kinesis data stream. To open the Apache Flink Dashboard for your application to view operational aspects, choose **FLINK JOB**. For more information about the Flink Dashboard, see [Apache Flink Dashboard](https://docs.aws.amazon.com/managed-flink/latest/java/how-dashboard.html) in the [Managed Service for Apache Flink Developer Guide](https://docs.aws.amazon.com/). For more examples of Flink Streaming SQL queries, see [Queries](https://nightlies.apache.org/flink/flink-docs-release-1.15/dev/table/sql/queries.html) in the [Apache Flink documentation](https://nightlies.apache.org/flink/flink-docs-release-1.15/). # Create a Studio notebook with Amazon MSK This tutorial describes how to create a Studio notebook that uses an Amazon MSK cluster as a source. **Topics** + [ ## Set up an Amazon MSK cluster ](#example-notebook-msk-setup) + [ ## Add a NAT gateway to your VPC ](#example-notebook-msk-nat) + [ ## Create an AWS Glue connection and table ](#example-notebook-msk-glue) + [ ## Create a Studio notebook with Amazon MSK ](#example-notebook-msk-create) + [ ## Send data to your Amazon MSK cluster ](#example-notebook-msk-send) + [ ## Test your Studio notebook ](#example-notebook-msk-test) ## Set up an Amazon MSK cluster For this tutorial, you need an Amazon MSK cluster that allows plaintext access. If you don't have an Amazon MSK cluster set up already, follow the [Getting Started Using Amazon MSK](https://docs.aws.amazon.com/msk/latest/developerguide/getting-started.html) tutorial to create an Amazon VPC, an Amazon MSK cluster, a topic, and an Amazon EC2 client instance. When following the tutorial, do the following: + In [Step 3: Create an Amazon MSK Cluster](https://docs.aws.amazon.com/msk/latest/developerguide/create-cluster.html), on step 4, change the `ClientBroker` value from `TLS` to **PLAINTEXT**. ## Add a NAT gateway to your VPC If you created an Amazon MSK cluster by following the [Getting Started Using Amazon MSK](https://docs.aws.amazon.com/msk/latest/developerguide/getting-started.html) tutorial, or if your existing Amazon VPC does not already have a NAT gateway for its private subnets, you must add a NAT Gateway to your Amazon VPC. The following diagram shows the architecture. ![\[AWS VPC architecture with public and private subnets, NAT gateway, and Glue Data Catalog integration.\]](http://docs.aws.amazon.com/managed-flink/latest/java/images/vpc_05.png) To create a NAT gateway for your Amazon VPC, do the following: 1. Open the Amazon VPC console at [https://console.aws.amazon.com/vpc/](https://console.aws.amazon.com/vpc/). 1. Choose **NAT Gateways** from the left navigation bar. 1. On the **NAT Gateways** page, choose **Create NAT Gateway**. 1. On the **Create NAT Gateway** page, provide the following values: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/managed-flink/latest/java/example-notebook-msk.html) Choose **Create NAT Gateway**. 1. On the left navigation bar, choose **Route Tables**. 1. Choose **Create Route Table**. 1. On the **Create route table** page, provide the following information: + **Name tag**: **ZeppelinRouteTable** + **VPC**: Choose your VPC (e.g. **AWSKafkaTutorialVPC**). Choose **Create**. 1. In the list of route tables, choose **ZeppelinRouteTable**. Choose the **Routes** tab, and choose **Edit routes**. 1. In the **Edit Routes** page, choose **Add route**. 1. In the ****For **Destination**, enter **0.0.0.0/0**. For **Target**, choose **NAT Gateway**, **ZeppelinGateway**. Choose **Save Routes**. Choose **Close**. 1. On the Route Tables page, with **ZeppelinRouteTable** selected, choose the **Subnet associations** tab. Choose **Edit subnet associations**. 1. In the **Edit subnet associations** page, choose **AWSKafkaTutorialSubnet2** and **AWSKafkaTutorialSubnet3**. Choose **Save**. ## Create an AWS Glue connection and table Your Studio notebook uses an [AWS Glue](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html) database for metadata about your Amazon MSK data source. In this section, you create an AWS Glue connection that describes how to access your Amazon MSK cluster, and an AWS Glue table that describes how to present the data in your data source to clients such as your Studio notebook. **Create a Connection** 1. Sign in to the AWS Management Console and open the AWS Glue console at [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/). 1. If you don't already have a AWS Glue database, choose **Databases** from the left navigation bar. Choose **Add Database**. In the **Add database** window, enter **default** for **Database name**. Choose **Create**. 1. Choose **Connections** from the left navigation bar. Choose **Add Connection**. 1. In the **Add Connection** window, provide the following values: + For **Connection name**, enter **ZeppelinConnection**. + For **Connection type**, choose **Kafka**. + For **Kafka bootstrap server URLs**, provide the bootstrap broker string for your cluster. You can get the bootstrap brokers from either the MSK console, or by entering the following CLI command: ``` aws kafka get-bootstrap-brokers --region us-east-1 --cluster-arn ClusterArn ``` + Uncheck the **Require SSL connection** checkbox. Choose **Next**. 1. In the **VPC** page, provide the following values: + For **VPC**, choose the name of your VPC (e.g. ** AWSKafkaTutorialVPC**.) + For **Subnet**, choose **AWSKafkaTutorialSubnet2**. + For **Security groups**, choose all available groups. Choose **Next**. 1. In the **Connection properties** / **Connection access** page, choose **Finish**. **Create a Table** **Note** You can either manually create the table as described in the following steps, or you can use the create table connector code for Managed Service for Apache Flink in your notebook within Apache Zeppelin to create your table via a DDL statement. You can then check in AWS Glue to make sure the table was correctly created. 1. In the left navigation bar, choose **Tables**. In the **Tables** page, choose **Add tables**, **Add table manually**. 1. In the **Set up your table's properties** page, enter **stock** for the **Table name**. Make sure you select the database you created previously. Choose **Next**. 1. In the **Add a data store** page, choose **Kafka**. For the **Topic name**, enter your topic name (e.g. **AWSKafkaTutorialTopic**). For **Connection**, choose **ZeppelinConnection**. 1. In the **Classification** page, choose **JSON**. Choose **Next**. 1. In the **Define a Schema** page, choose Add Column to add a column. Add columns with the following properties: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/managed-flink/latest/java/example-notebook-msk.html) Choose **Next**. 1. On the next page, verify your settings, and choose **Finish**. 1. Choose your newly created table from the list of tables. 1. Choose **Edit table** and add the following properties: + key: `managed-flink.proctime`, value: `proctime` + key: `flink.properties.group.id`, value: `test-consumer-group` + key: `flink.properties.auto.offset.reset`, value: `latest` + key: `classification`, value: `json` Without these key/value pairs, the Flink notebook runs into an error. 1. Choose **Apply**. ## Create a Studio notebook with Amazon MSK Now that you have created the resources your application uses, you create your Studio notebook. **Topics** + [ ### Create a Studio notebook using the AWS Management Console ](#example-notebook-create-msk-console) + [ ### Create a Studio notebook using the AWS CLI ](#example-notebook-msk-create-api) **Note** You can also create a Studio notebook from the Amazon MSK console by choosing an existing cluster, then choosing **Process data in real time**. ### Create a Studio notebook using the AWS Management Console 1. Open the Managed Service for Apache Flink console at [ https://console.aws.amazon.com/managed-flink/home?region=us-east-1\$1/applications/dashboard](https://console.aws.amazon.com/managed-flink/home?region=us-east-1#/applications/dashboard). 1. In the **Managed Service for Apache Flink applications** page, choose the **Studio** tab. Choose **Create Studio notebook**. **Note** To create a Studio notebook from the Amazon MSK or Kinesis Data Streams consoles, select your input Amazon MSK cluster or Kinesis data stream, then choose **Process data in real time**. 1. In the **Create Studio notebook** page, provide the following information: + Enter **MyNotebook** for **Studio notebook Name**. + Choose **default** for **AWS Glue database**. Choose **Create Studio notebook**. 1. In the **MyNotebook** page, choose the **Configuration** tab. In the **Networking** section, choose **Edit**. 1. In the **Edit networking for MyNotebook** page, choose **VPC configuration based on Amazon MSK cluster**. Choose your Amazon MSK cluster for **Amazon MSK Cluster**. Choose **Save changes**. 1. In the **MyNotebook** page, choose **Run**. Wait for the **Status** to show **Running**. ### Create a Studio notebook using the AWS CLI To create your Studio notebook by using the AWS CLI, do the following: 1. Verify that you have the following information. You need these values to create your application. + Your account ID. + The subnet IDs and security group ID for the Amazon VPC that contains your Amazon MSK cluster. 1. Create a file called `create.json` with the following contents. Replace the placeholder values with your information. ``` { "ApplicationName": "MyNotebook", "RuntimeEnvironment": "ZEPPELIN-FLINK-3_0", "ApplicationMode": "INTERACTIVE", "ServiceExecutionRole": "arn:aws:iam::AccountID:role/ZeppelinRole", "ApplicationConfiguration": { "ApplicationSnapshotConfiguration": { "SnapshotsEnabled": false }, "VpcConfigurations": [ { "SubnetIds": [ "SubnetID 1", "SubnetID 2", "SubnetID 3" ], "SecurityGroupIds": [ "VPC Security Group ID" ] } ], "ZeppelinApplicationConfiguration": { "CatalogConfiguration": { "GlueDataCatalogConfiguration": { "DatabaseARN": "arn:aws:glue:us-east-1:AccountID:database/default" } } } } } ``` 1. Run the following command to create your application: ``` aws kinesisanalyticsv2 create-application --cli-input-json file://create.json ``` 1. When the command completes, you should see output similar to the following, showing the details for your new Studio notebook: ``` { "ApplicationDetail": { "ApplicationARN": "arn:aws:kinesisanalyticsus-east-1:012345678901:application/MyNotebook", "ApplicationName": "MyNotebook", "RuntimeEnvironment": "ZEPPELIN-FLINK-3_0", "ApplicationMode": "INTERACTIVE", "ServiceExecutionRole": "arn:aws:iam::012345678901:role/ZeppelinRole", ... ``` 1. Run the following command to start your application. Replace the sample value with your account ID. ``` aws kinesisanalyticsv2 start-application --application-arn arn:aws:kinesisanalyticsus-east-1:012345678901:application/MyNotebook\ ``` ## Send data to your Amazon MSK cluster In this section, you run a Python script in your Amazon EC2 client to send data to your Amazon MSK data source. 1. Connect to your Amazon EC2 client. 1. Run the following commands to install Python version 3, Pip, and the Kafka for Python package, and confirm the actions: ``` sudo yum install python37 curl -O https://bootstrap.pypa.io/get-pip.py python3 get-pip.py --user pip install kafka-python ``` 1. Configure the AWS CLI on your client machine by entering the following command: ``` aws configure ``` Provide your account credentials, and **us-east-1** for the `region`. 1. Create a file called `stock.py` with the following contents. Replace the sample value with your Amazon MSK cluster's Bootstrap Brokers string, and update the topic name if your topic is not **AWSKafkaTutorialTopic**: ``` from kafka import KafkaProducer import json import random from datetime import datetime BROKERS = "<>" producer = KafkaProducer( bootstrap_servers=BROKERS, value_serializer=lambda v: json.dumps(v).encode('utf-8'), retry_backoff_ms=500, request_timeout_ms=20000, security_protocol='PLAINTEXT') def getStock(): data = {} now = datetime.now() str_now = now.strftime("%Y-%m-%d %H:%M:%S") data['event_time'] = str_now data['ticker'] = random.choice(['AAPL', 'AMZN', 'MSFT', 'INTC', 'TBV']) price = random.random() * 100 data['price'] = round(price, 2) return data while True: data =getStock() # print(data) try: future = producer.send("AWSKafkaTutorialTopic", value=data) producer.flush() record_metadata = future.get(timeout=10) print("sent event to Kafka! topic {} partition {} offset {}".format(record_metadata.topic, record_metadata.partition, record_metadata.offset)) except Exception as e: print(e.with_traceback()) ``` 1. Run the script with the following command: ``` $ python3 stock.py ``` 1. Leave the script running while you complete the following section. ## Test your Studio notebook In this section, you use your Studio notebook to query data from your Amazon MSK cluster. 1. Open the Managed Service for Apache Flink console at [ https://console.aws.amazon.com/managed-flink/home?region=us-east-1\$1/applications/dashboard](https://console.aws.amazon.com/managed-flink/home?region=us-east-1#/applications/dashboard). 1. On the **Managed Service for Apache Flink applications** page, choose the **Studio notebook** tab. Choose **MyNotebook**. 1. In the **MyNotebook** page, choose **Open in Apache Zeppelin**. The Apache Zeppelin interface opens in a new tab. 1. In the **Welcome to Zeppelin\$1** page, choose **Zeppelin new note**. 1. In the **Zeppelin Note** page, enter the following query into a new note: ``` %flink.ssql(type=update) select * from stock ``` Choose the run icon. The application displays data from the Amazon MSK cluster. To open the Apache Flink Dashboard for your application to view operational aspects, choose **FLINK JOB**. For more information about the Flink Dashboard, see [Apache Flink Dashboard](https://docs.aws.amazon.com/managed-flink/latest/java/how-dashboard.html) in the [Managed Service for Apache Flink Developer Guide](https://docs.aws.amazon.com/). For more examples of Flink Streaming SQL queries, see [Queries](https://nightlies.apache.org/flink/flink-docs-release-1.15/dev/table/sql/queries.html) in the [Apache Flink documentation](https://nightlies.apache.org/flink/flink-docs-release-1.15/). # Clean up your application and dependent resources ## Delete your Studio notebook 1. Open the Managed Service for Apache Flink console. 1. Choose **MyNotebook**. 1. Choose **Actions**, then **Delete**. ## Delete your AWS Glue database and connection 1. Open the AWS Glue console at [https://console.aws.amazon.com/glue/](https://console.aws.amazon.com/glue/). 1. Choose **Databases** from the left navigation bar. Check the checkbox next to **Default** to select it. Choose **Action**, **Delete Database**. Confirm your selection. 1. Choose **Connections** from the left navigation bar. Check the checkbox next to **ZeppelinConnection** to select it. Choose **Action**, **Delete Connection**. Confirm your selection. ## Delete your IAM role and policy 1. Open the IAM console at [https://console.aws.amazon.com/iam/](https://console.aws.amazon.com/iam/). 1. Choose **Roles** from the left navigation bar. 1. Use the search bar to search for the **ZeppelinRole** role. 1. Choose the **ZeppelinRole** role. Choose **Delete Role**. Confirm the deletion. ## Delete your CloudWatch log group The console creates a CloudWatch Logs group and log stream for you when you create your application using the console. You do not have a log group and stream if you created your application using the AWS CLI. 1. Open the CloudWatch console at [https://console.aws.amazon.com/cloudwatch/](https://console.aws.amazon.com/cloudwatch/). 1. Choose **Log groups** from the left navigation bar. 1. Choose the **/AWS/KinesisAnalytics/MyNotebook** log group. 1. Choose **Actions**, **Delete log group(s)**. Confirm the deletion. ## Clean up Kinesis Data Streams resources To delete your Kinesis stream, open the Kinesis Data Streams console, select your Kinesis stream, and choose **Actions**, **Delete**. ## Clean up MSK resources Follow the steps in this section if you created an Amazon MSK cluster for this tutorial. This section has directions for cleaning up your Amazon EC2 client instance, Amazon VPC, and Amazon MSK cluster. ### Delete your Amazon MSK cluster Follow these steps if you created an Amazon MSK cluster for this tutorial. 1. Open the Amazon MSK console at [https://console.aws.amazon.com/msk/home?region=us-east-1\$1/home/](https://console.aws.amazon.com/msk/home?region=us-east-1#/home/). 1. Choose **AWSKafkaTutorialCluster**. Choose **Delete**. Enter **delete** in the window that appears, and confirm your selection. ### Terminate your client instance Follow these steps if you created an Amazon EC2 client instance for this tutorial. 1. Open the Amazon EC2 console at [https://console.aws.amazon.com/ec2/](https://console.aws.amazon.com/ec2/). 1. Choose **Instances** from the left navigation bar. 1. Choose the checkbox next to **ZeppelinClient** to select it. 1. Choose **Instance State**, **Terminate Instance**. ### Delete your Amazon VPC Follow these steps if you created an Amazon VPC for this tutorial. 1. Open the Amazon EC2 console at [https://console.aws.amazon.com/ec2/](https://console.aws.amazon.com/ec2/). 1. Choose **Network Interfaces** from the left navigation bar. 1. Enter your VPC ID in the search bar and press enter to search. 1. Select the checkbox in the table header to select all the displayed network interfaces. 1. Choose **Actions**, **Detach**. In the window that appears, choose **Enable** under **Force detachment**. Choose **Detach**, and wait for all of the network interfaces to reach the **Available** status. 1. Select the checkbox in the table header to select all the displayed network interfaces again. 1. Choose **Actions**, **Delete**. Confirm the action. 1. Open the Amazon VPC console at [https://console.aws.amazon.com/vpc/](https://console.aws.amazon.com/vpc/). 1. Select **AWSKafkaTutorialVPC**. Choose **Actions**, **Delete VPC**. Enter **delete** and confirm the deletion.