Resources

Introduction

Application Performance Management (APM) is crucial for monitoring and managing the performance and availability of software applications. Pinpoint is an open-source APM tool that offers comprehensive insights into the performance and reliability of applications. It is designed to monitor large-scale distributed systems, providing real-time performance metrics, tracing, and detailed visualizations.

This guide provides a step-by-step approach to implementing Pinpoint APM for a Node.js application, including setting up the server, installing Docker, deploying Pinpoint, and integrating it with the Node.js application.

About Pinpoint

Pinpoint is a powerful APM tool that helps understand the application's performance and track down issues. It supports a variety of technologies and provides functionalities like:

• Real-time application monitoring
• Distributed tracing
• Visualization of application topology
• Alerts and notifications
• Detailed transaction analysis

Setup a server:

We have to launch a new server with a minimum of 2vCPU and 4GB RAM.

Install Docker Engine

sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io

Install Docker Compose

Download the Docker Compose binary into the /usr/local/bin directory:

sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

Apply executable permissions to the binary

sudo chmod +x /usr/local/bin/docker-compose

Verify the installation:

docker --version

docker-compose --version

‍
Deploy Pinpoint Using Docker

Clone the git repository:

git clone https://github.com/pinpoint-apm/pinpoint-docker.git

cd pinpoint-docker

sudo docker-compose pull && docker-compose up -d
‍

NOTE:
If the above docker-compose.yml won’t work please use the following yml file to up the docker.

version: "3.6"

services:

  pinpoint-hbase:

    build:

      context: ./pinpoint-hbase/

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_HBASE_NAME}"

    image: "pinpointdocker/pinpoint-hbase:${PINPOINT_VERSION}"

    networks:

      - pinpoint

    environment:

      - AGENTINFO_TTL=${AGENTINFO_TTL}

      - AGENTSTATV2_TTL=${AGENTSTATV2_TTL}

      - APPSTATAGGRE_TTL=${APPSTATAGGRE_TTL}

      - APPINDEX_TTL=${APPINDEX_TTL}

      - AGENTLIFECYCLE_TTL=${AGENTLIFECYCLE_TTL}

      - AGENTEVENT_TTL=${AGENTEVENT_TTL}

      - STRINGMETADATA_TTL=${STRINGMETADATA_TTL}

      - APIMETADATA_TTL=${APIMETADATA_TTL}

      - SQLMETADATA_TTL=${SQLMETADATA_TTL}

      - TRACEV2_TTL=${TRACEV2_TTL}

      - APPTRACEINDEX_TTL=${APPTRACEINDEX_TTL}

      - APPMAPSTATCALLERV2_TTL=${APPMAPSTATCALLERV2_TTL}

      - APPMAPSTATCALLEV2_TTL=${APPMAPSTATCALLEV2_TTL}

      - APPMAPSTATSELFV2_TTL=${APPMAPSTATSELFV2_TTL}

      - HOSTAPPMAPV2_TTL=${HOSTAPPMAPV2_TTL}

    volumes:

      - hbase_data:/home/pinpoint/hbase

      - /home/pinpoint/zookeeper

    expose:

      # HBase Master API port

      - "60000"

      # HBase Master Web UI

      - "16010"

      # Regionserver API port

      - "60020"

      # HBase Regionserver web UI

      - "16030"

    ports:

      - "60000:60000"

      - "16010:16010"

      - "60020:60020"

      - "16030:16030"

    restart: always

    depends_on:

      - zoo1

‍

  pinpoint-mysql:

    container_name: pinpoint-mysql

    image: mysql:8.0

    restart: "no"

    hostname: pinpoint-mysql

    entrypoint: > 

      sh -c "

      curl -SL "https://raw.githubusercontent.com/ga-ram/pinpoint/latest/web/src/main/resources/sql/CreateTableStatement-mysql.sql" -o /docker-entrypoint-initdb.d/CreateTableStatement-mysql.sql &&

      curl -SL "https://raw.githubusercontent.com/ga-ram/pinpoint/latest/web/src/main/resources/sql/SpringBatchJobRepositorySchema-mysql.sql" -o /docker-entrypoint-initdb.d/SpringBatchJobRepositorySchema-mysql.sql &&

      sed -i '/^--/d' /docker-entrypoint-initdb.d/CreateTableStatement-mysql.sql &&

      sed -i '/^--/d' /docker-entrypoint-initdb.d/SpringBatchJobRepositorySchema-mysql.sql &&

      docker-entrypoint.sh mysqld

      "

    ports:

      - "3306:3306"

    environment:

      - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}

      - MYSQL_USER=${MYSQL_USER}

      - MYSQL_PASSWORD=${MYSQL_PASSWORD}

      - MYSQL_DATABASE=${MYSQL_DATABASE}

    volumes:

      - mysql_data:/var/lib/mysql

    networks:

      - pinpoint

‍

  pinpoint-web:

    build:

      context: ./pinpoint-web/

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_WEB_NAME}"

    image: "pinpointdocker/pinpoint-web:${PINPOINT_VERSION}"

    depends_on:

      - pinpoint-hbase

      - pinpoint-mysql

      - zoo1

      - redis

    restart: always

    expose:

      - "9997"

    ports:

      - "9997:9997"

      - "${WEB_SERVER_PORT:-8080}:8080"

    environment:

      - WEB_SERVER_PORT=${WEB_SERVER_PORT}

      - SPRING_PROFILES_ACTIVE=${SPRING_PROFILES}

      - PINPOINT_ZOOKEEPER_ADDRESS=${PINPOINT_ZOOKEEPER_ADDRESS}

      - CLUSTER_ENABLE=${CLUSTER_ENABLE}

      - ADMIN_PASSWORD=${ADMIN_PASSWORD}

      - CONFIG_SENDUSAGE=${CONFIG_SENDUSAGE}

      - LOGGING_LEVEL_ROOT=${WEB_LOGGING_LEVEL_ROOT}

      - CONFIG_SHOW_APPLICATIONSTAT=${CONFIG_SHOW_APPLICATIONSTAT}

      - JDBC_DRIVERCLASSNAME=${JDBC_DRIVERCLASSNAME}

      - JDBC_URL=${SPRING_DATASOURCE_HIKARI_JDBCURL}

      - JDBC_USERNAME=${SPRING_DATASOURCE_HIKARI_USERNAME}

      - JDBC_PASSWORD=${SPRING_DATASOURCE_HIKARI_PASSWORD}

      - SPRING_DATASOURCE_HIKARI_JDBCURL=${SPRING_DATASOURCE_HIKARI_JDBCURL}

      - SPRING_DATASOURCE_HIKARI_USERNAME=${SPRING_DATASOURCE_HIKARI_USERNAME}

      - SPRING_DATASOURCE_HIKARI_PASSWORD=${SPRING_DATASOURCE_HIKARI_PASSWORD}

      - SPRING_METADATASOURCE_HIKARI_JDBCURL=${SPRING_METADATASOURCE_HIKARI_JDBCURL}

      - SPRING_METADATASOURCE_HIKARI_USERNAME=${SPRING_METADATASOURCE_HIKARI_USERNAME}

      - SPRING_METADATASOURCE_HIKARI_PASSWORD=${SPRING_METADATASOURCE_HIKARI_PASSWORD}

      - SPRING_DATA_REDIS_HOST=${SPRING_DATA_REDIS_HOST}

      - SPRING_DATA_REDIS_PORT=${SPRING_DATA_REDIS_PORT}

      - SPRING_DATA_REDIS_USERNAME=${SPRING_DATA_REDIS_USERNAME}

      - SPRING_DATA_REDIS_PASSWORD=${SPRING_DATA_REDIS_PASSWORD}

    links:

      - "pinpoint-mysql:pinpoint-mysql"

    networks:

      - pinpoint

  pinpoint-collector:

    build:

      context: ./pinpoint-collector/

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_COLLECTOR_NAME}"

    image: "pinpointdocker/pinpoint-collector:${PINPOINT_VERSION}"

    depends_on:

      - pinpoint-hbase

      - zoo1

      - redis

    restart: always

    expose:

      - "9991"

      - "9992"

      - "9993"

      - "9994"

      - "9995"

      - "9996"

    ports:

      - "${COLLECTOR_RECEIVER_GRPC_AGENT_PORT:-9991}:9991/tcp"

      - "${COLLECTOR_RECEIVER_GRPC_STAT_PORT:-9992}:9992/tcp"

      - "${COLLECTOR_RECEIVER_GRPC_SPAN_PORT:-9993}:9993/tcp"

      - "${COLLECTOR_RECEIVER_BASE_PORT:-9994}:9994"

      - "${COLLECTOR_RECEIVER_STAT_UDP_PORT:-9995}:9995/tcp"

      - "${COLLECTOR_RECEIVER_SPAN_UDP_PORT:-9996}:9996/tcp"

      - "${COLLECTOR_RECEIVER_STAT_UDP_PORT:-9995}:9995/udp"

      - "${COLLECTOR_RECEIVER_SPAN_UDP_PORT:-9996}:9996/udp"

    networks:

      pinpoint:

        ipv4_address: ${COLLECTOR_FIXED_IP}

    environment:

      - SPRING_PROFILES_ACTIVE=${SPRING_PROFILES}

      - PINPOINT_ZOOKEEPER_ADDRESS=${PINPOINT_ZOOKEEPER_ADDRESS}

      - CLUSTER_ENABLE=${CLUSTER_ENABLE}

      - LOGGING_LEVEL_ROOT=${COLLECTOR_LOGGING_LEVEL_ROOT}

      - FLINK_CLUSTER_ENABLE=${FLINK_CLUSTER_ENABLE}

      - FLINK_CLUSTER_ZOOKEEPER_ADDRESS=${FLINK_CLUSTER_ZOOKEEPER_ADDRESS}

      - SPRING_DATA_REDIS_HOST=${SPRING_DATA_REDIS_HOST}

      - SPRING_DATA_REDIS_PORT=${SPRING_DATA_REDIS_PORT}

      - SPRING_DATA_REDIS_USERNAME=${SPRING_DATA_REDIS_USERNAME}

      - SPRING_DATA_REDIS_PASSWORD=${SPRING_DATA_REDIS_PASSWORD}

  pinpoint-quickstart:

    build:

      context: ./pinpoint-quickstart/

      dockerfile: Dockerfile

    container_name: "pinpoint-quickstart"

    image: "pinpointdocker/pinpoint-quickstart"

    ports:

      - "${APP_PORT:-8085}:8080"

    volumes:

      - data-volume:/pinpoint-agent

    environment:

      JAVA_OPTS: "-javaagent:/pinpoint-agent/pinpoint-bootstrap.jar -Dpinpoint.agentId=${AGENT_ID} -Dpinpoint.applicationName=${APP_NAME} -Dpinpoint.profiler.profiles.active=${SPRING_PROFILES}"

    networks:

      - pinpoint

    depends_on:

      - pinpoint-agent

  pinpoint-batch:

    build:

      context: ./pinpoint-batch/

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_BATCH_NAME}"

    image: "pinpointdocker/pinpoint-batch:${PINPOINT_VERSION}"

    depends_on:

      - pinpoint-hbase

      - pinpoint-mysql

      - zoo1

    restart: always

    environment:

      - BATCH_SERVER_PORT=${BATCH_SERVER_PORT}

      - SPRING_PROFILES_ACTIVE=${SPRING_PROFILES}

      - PINPOINT_ZOOKEEPER_ADDRESS=${PINPOINT_ZOOKEEPER_ADDRESS}

      - CLUSTER_ENABLE=${CLUSTER_ENABLE}

      - ADMIN_PASSWORD=${ADMIN_PASSWORD}

      - CONFIG_SENDUSAGE=${CONFIG_SENDUSAGE}

      - LOGGING_LEVEL_ROOT=${BATCH_LOGGING_LEVEL_ROOT}

      - CONFIG_SHOW_APPLICATIONSTAT=${CONFIG_SHOW_APPLICATIONSTAT}

      - BATCH_FLINK_SERVER=${BATCH_FLINK_SERVER}

      - JDBC_DRIVERCLASSNAME=${JDBC_DRIVERCLASSNAME}

      - JDBC_URL=${SPRING_DATASOURCE_HIKARI_JDBCURL}

      - JDBC_USERNAME=${SPRING_DATASOURCE_HIKARI_USERNAME}

      - JDBC_PASSWORD=${SPRING_DATASOURCE_HIKARI_PASSWORD}

      - SPRING_DATASOURCE_HIKARI_JDBCURL=${SPRING_DATASOURCE_HIKARI_JDBCURL}

      - SPRING_DATASOURCE_HIKARI_USERNAME=${SPRING_DATASOURCE_HIKARI_USERNAME}

      - SPRING_DATASOURCE_HIKARI_PASSWORD=${SPRING_DATASOURCE_HIKARI_PASSWORD}

      - SPRING_METADATASOURCE_HIKARI_JDBCURL=${SPRING_METADATASOURCE_HIKARI_JDBCURL}

      - SPRING_METADATASOURCE_HIKARI_USERNAME=${SPRING_METADATASOURCE_HIKARI_USERNAME}

      - SPRING_METADATASOURCE_HIKARI_PASSWORD=${SPRING_METADATASOURCE_HIKARI_PASSWORD}

      - ALARM_MAIL_SERVER_URL=${ALARM_MAIL_SERVER_URL}

      - ALARM_MAIL_SERVER_PORT=${ALARM_MAIL_SERVER_PORT}

      - ALARM_MAIL_SERVER_USERNAME=${ALARM_MAIL_SERVER_USERNAME}

      - ALARM_MAIL_SERVER_PASSWORD=${ALARM_MAIL_SERVER_PASSWORD}

      - ALARM_MAIL_SENDER_ADDRESS=${ALARM_MAIL_SENDER_ADDRESS}

      - ALARM_MAIL_TRANSPORT_PROTOCOL=${ALARM_MAIL_TRANSPORT_PROTOCOL}

      - ALARM_MAIL_SMTP_PORT=${ALARM_MAIL_SMTP_PORT}

      - ALARM_MAIL_SMTP_AUTH=${ALARM_MAIL_SMTP_AUTH}

      - ALARM_MAIL_SMTP_STARTTLS_ENABLE=${ALARM_MAIL_SMTP_STARTTLS_ENABLE}

      - ALARM_MAIL_SMTP_STARTTLS_REQUIRED=${ALARM_MAIL_SMTP_STARTTLS_REQUIRED}

      - ALARM_MAIL_DEBUG=${ALARM_MAIL_DEBUG}

    links:

      - "pinpoint-mysql:pinpoint-mysql"

    networks:

      - pinpoint

  pinpoint-agent:

    build:

      context: ./pinpoint-agent/

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_AGENT_NAME}"

    image: "pinpointdocker/pinpoint-agent:${PINPOINT_VERSION}"

    restart: unless-stopped

    networks:

      - pinpoint

    volumes:

      - data-volume:/pinpoint-agent

    environment:

      - SPRING_PROFILES=${SPRING_PROFILES}

      - COLLECTOR_IP=${COLLECTOR_IP}

      - PROFILER_TRANSPORT_AGENT_COLLECTOR_PORT=${PROFILER_TRANSPORT_AGENT_COLLECTOR_PORT}

      - PROFILER_TRANSPORT_METADATA_COLLECTOR_PORT=${PROFILER_TRANSPORT_METADATA_COLLECTOR_PORT}

      - PROFILER_TRANSPORT_STAT_COLLECTOR_PORT=${PROFILER_TRANSPORT_STAT_COLLECTOR_PORT}

      - PROFILER_TRANSPORT_SPAN_COLLECTOR_PORT=${PROFILER_TRANSPORT_SPAN_COLLECTOR_PORT}

      - PROFILER_SAMPLING_TYPE=${PROFILER_SAMPLING_TYPE}

      - PROFILER_SAMPLING_COUNTING_SAMPLING_RATE=${PROFILER_SAMPLING_COUNTING_SAMPLING_RATE}

      - PROFILER_SAMPLING_PERCENT_SAMPLING_RATE=${PROFILER_SAMPLING_PERCENT_SAMPLING_RATE}

      - PROFILER_SAMPLING_NEW_THROUGHPUT=${PROFILER_SAMPLING_NEW_THROUGHPUT}

      - PROFILER_SAMPLING_CONTINUE_THROUGHPUT=${PROFILER_SAMPLING_CONTINUE_THROUGHPUT}

      - DEBUG_LEVEL=${AGENT_DEBUG_LEVEL}

      - PROFILER_TRANSPORT_MODULE=${PROFILER_TRANSPORT_MODULE}

    depends_on:

      - pinpoint-collector

  #zookeepers

  zoo1:

    image: zookeeper:3.4.13

    restart: always

    hostname: zoo1

    expose:

      - "2181"

      - "2888"

      - "3888"

    ports:

      - "2181"

    environment:

      ZOO_MY_ID: 1

      ZOO_SERVERS: server.1=0.0.0.0:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888

    networks:

      - pinpoint

  zoo2:

    image: zookeeper:3.4.13

    restart: always

    hostname: zoo2

    expose:

      - "2181"

      - "2888"

      - "3888"

    ports:

      - "2181"

    environment:

      ZOO_MY_ID: 2

      ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=0.0.0.0:2888:3888 server.3=zoo3:2888:3888

    networks:

      - pinpoint

  zoo3:

    image: zookeeper:3.4.13

    restart: always

    hostname: zoo3

    expose:

      - "2181"

      - "2888"

      - "3888"

    ports:

      - "2181"

    environment:

      ZOO_MY_ID: 3

      ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=0.0.0.0:2888:3888

    networks:

      - pinpoint

  ##flink

  jobmanager:

    build:

      context: pinpoint-flink

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_FLINK_NAME}-jobmanager"

    image: "pinpointdocker/pinpoint-flink:${PINPOINT_VERSION}"

    expose:

      - "6123"

    ports:

      - "${FLINK_WEB_PORT:-8081}:8081"

    command: standalone-job -p 1 pinpoint-flink-job.jar -spring.profiles.active release

    environment:

      - JOB_MANAGER_RPC_ADDRESS=jobmanager

      - PINPOINT_ZOOKEEPER_ADDRESS=${PINPOINT_ZOOKEEPER_ADDRESS}

    networks:

      - pinpoint

    depends_on:

      - zoo1

  taskmanager:

    build:

      context: pinpoint-flink

      dockerfile: Dockerfile

      args:

        - PINPOINT_VERSION=${PINPOINT_VERSION}

    container_name: "${PINPOINT_FLINK_NAME}-taskmanager"

    image: "pinpointdocker/pinpoint-flink:${PINPOINT_VERSION}"

    expose:

      - "6121"

      - "6122"

      - "19994"

    ports:

      - "6121:6121"

      - "6122:6122"

      - "19994:19994"

    depends_on:

      - zoo1

      - jobmanager

    command: taskmanager

    links:

      - "jobmanager:jobmanager"

    environment:

      - JOB_MANAGER_RPC_ADDRESS=jobmanager

    networks:

      - pinpoint

  redis:

    image: redis:7.0.14

    restart: always

    hostname: pinpoint-redis

    ports:

      - "6379:6379"

    networks:

      - pinpoint

volumes:

  data-volume:

  mysql_data:

  hbase_data:

networks:

  pinpoint:

    driver: bridge

    ipam:

      config:

        - subnet: ${PINPOINT_NETWORK_SUBNET}

The explanation of the components that we have used in the docker-compose yaml files
‍
1. Services

Services are the individual containers that make up the application. Each service runs in its container but can interact with other services defined in the same docker-compose.yml file.

a. pinpoint-hbase

• Purpose: Pinpoint uses HBase as its primary storage for storing tracing data.
• Build: The service is built from a Dockerfile located in the ./pinpoint-hbase/ directory.
• Environment Variables: These variables define various TTL (Time-to-Live) settings for different types of data stored in HBase.
• Volumes: Persistent storage for HBase data is mounted on the host to ensure data persistence across container restarts.
• Ports: The service exposes several ports for communication (60000, 16010, 60020, 16030).
• Depends_on: This ensures that zoo1 (Zookeeper) service starts before pinpoint-hbase.

b. pinpoint-mysql

• Purpose: MySQL is used to store application metadata and other relational data needed by Pinpoint.
• Image: A MySQL 8.0 image from Docker Hub is used.
• Environment Variables: These include MySQL credentials like root password, user, password, and database name.
• Volumes: Persistent storage for MySQL data is mounted on the host.
• Ports: The MySQL service is exposed on port 3306.

c. pinpoint-web

• Purpose: This is the web UI for Pinpoint, allowing users to visualize and analyze the tracing data.
• Build: The service is built from a Dockerfile located in the ./pinpoint-web/ directory.
• Depends_on: This ensures that the pinpoint-hbase, pinpoint-mysql, zoo1, and redis services are running before starting the web service.
• Environment Variables: These configure the web service, including database connections, logging levels, and other properties.
• Ports: The service exposes port 9997 for the web interface.

d. pinpoint-collector

• Purpose: The collector service gathers trace data from applications and stores it in HBase.
• Build: The service is built from a Dockerfile located in the ./pinpoint-collector/ directory.
• Depends_on: This ensures that pinpoint-hbase, zoo1, and redis services are running before starting the collector.
• Environment Variables: These configure the collector service, including its connection to HBase, Zookeeper, and logging levels.
• Ports: The collector exposes several ports (9991-9996) for various types of communication (gRPC, UDP, etc.).
• Networks: The collector service is part of the pinpoint network and uses a fixed IP address.

e. zoo1

• Purpose: Zookeeper is used to manage and coordinate the distributed components of Pinpoint.
• Image: A Zookeeper image (3.4.14) from Docker Hub is used.
• Environment Variables: These configure the Zookeeper instance.
• Ports: The service is exposed on port 2181 for Zookeeper communication.

f. redis

• Purpose: Redis is used as a caching layer for Pinpoint, helping to improve performance.
• Image: A Redis image (5.0.6) from Docker Hub is used.
• Ports: The Redis service is exposed on port 6379.

2. Networks

Networks allow the services to communicate with each other. In this docker-compose.yml, a custom bridge network named pinpoint is defined.

• pinpoint: This is a user-defined bridge network that allows all the services to communicate with each other on a private network. Each service can reach others using their service names.

3. Volumes

Volumes provide persistent storage that survives container restarts. They are used to store data generated by services (like databases).

• hbase_data: A volume for storing HBase data.
• mysql_data: A volume for storing MySQL data.

4. Environment Variables

Environment variables are used to configure the services at runtime. These can include database credentials, logging levels, ports, and other configuration details. Each service defines its own set of environment variables, tailored to its specific needs.

5. Ports

Ports are exposed to allow external access to the services. For example:

• 3306:3306 for MySQL
• 9997:9997 for the Pinpoint Web UI
• 6379:6379 for Redis

6. Restart Policies

Restart policies (restart: always) ensure that the containers are automatically restarted if they stop or crash. This helps maintain the high availability of the services.

7. Links

Links allow containers to communicate with each other using hostnames. In this docker-compose.yml, the pinpoint-web and pinpoint-collector services are linked to the pinpoint-mysql service to facilitate database communication.

8. Expose vs. Ports

• Expose: This allows containers to communicate with each other internally, without exposing the ports to the host machine.
• Ports: These map the container ports to the host machine, allowing external access to the services.

Then need to whitelist the following ports 8080, 80, and 443  in the security groups.

We can see the dashboard below.

Integrate Pinpoint to the Node Js application:

We have to import the pinpoint agent in the Nodejs application.
Commands to be run after import pinpoint agent:

Install with npm:

npm install --save pinpoint-node-agent

Install with yarn:

yarn add pinpoint-node-agent

Adding a code:

To run Pinpoint agent for applications, we need to make sure the prerequisites are in place first.

CommonJS

require('pinpoint-node-agent')

If we are using pm2, use node-args(CLI) or node_args(Ecosystem File).

module.exports = {

  apps : [{

    name: "app",

    script: "./app.js",

    'node_args': ['-r', 'pinpoint-node-agent']

  }]

}

Below is the example of we have attached,

Configure with environment variables and start the application

‍
Based on the pinpoint-config-default.json file in the server, only necessary parts are set as environment variables.

PINPOINT_AGENT_ID=${HOSTNAME} PINPOINT_APPLICATION_NAME=Test-Node-App PINPOINT_COLLECTOR_IP=<pinpoint server private-ip> PINPOINT_ENABLE=true pm2 start ~/application path/app.js

Once the application is running and then check the site. The output is attached below

Conclusion

By following these steps, we have successfully set up Pinpoint APM to monitor our Node.js application. With Pinpoint, we can gain deep insights into our application's performance, identify bottlenecks, and optimize our code to ensure a smooth and efficient user experience. Pinpoint's real-time monitoring and comprehensive tracing capabilities make it an invaluable tool for managing the performance of our applications.
‍

Reference

‍
https://github.com/pinpoint-apm

https://github.com/pinpoint-apm/pinpoint

https://github.com/pinpoint-apm/pinpoint-node-agent

https://www.baeldung.com/ops/pinpoint-intro 

Overview

Moving a virtual machine (VM) instance from Google Cloud Platform (GCP) to Amazon Web Services (AWS) can seem scary. But with the right tools and a step by step process it can be done. In this post we will walk you through the entire process and make the transition from GCP to AWS smooth. Here we are using AWS’s native tool, Application Migration Service, to move a VM instance from GCP to AWS.

Architecture Diagram

Step-by-Step Guide

Step 1: Setup on GCP

Launch a Test Windows VM Instance

Go to your GCP console and create a test Windows VM. We created a 51 GB boot disk for this example. This will be our source VM.

RDP into the Windows Server

Next RDP into your Windows server. Once connected you need to install the AWS Application Migration Service (AMS) agent on this server.

Install the AMS Agent

To install the AMS agent, download it using the following command:

https://aws-application-migration-service-us-east-1.s3.us-east-1.amazonaws.com/latest/windows/AwsReplicationWindowsInstaller.exe

For more details, refer to the AWS documentation: https://docs.aws.amazon.com/mgn/latest/ug/windows-agent.html

Step 2: Install the AMS Agent

Navigate to the Downloads folder and open the AWS agent with administrator privileges using the Command prompt.

 When installing you will be asked to choose the AWS region to replicate to. For this guide we chose N.V.

Step 3: Prepare the AWS Console

Create a User and Attach Permissions

In the AWS console create a new user and attach an AWS replication permission role to it. Generate access and secret keys for this user.

While creating keys choose the “third-party service” option for that key.

Enter the Keys into the GCP Windows Server

Enter the access key and secret key into the GCP Windows server. The AMS agent will ask which disks to replicate (e.g. C and D drives). For this example we just pressed enter to replicate all disks.

Once done the AMS agent will install and start replicating your data.

In our AWS account, one instance was created :

After installing the AMS agent on the source Windows server in GCP, a replication server was created in the AWS EC2 console. This instance was used to replicate all VM instance data from the GCP account to the AWS account.

Step 4: Monitor the Data Migration

 Go to the Application Migration Service in your AWS account. In the source servers column you should see your GCP VM instance listed.

The data migration will start and you can monitor it. Depending on the size of your boot disk and the amount of data this may take some time.

It took over half an hour to migrate the data from a 51 GB boot disk on a GCP VM instance to AWS. Once completed, it was ready for the testing stage.

Step 5: Create a Launch Template‍

After the data migration is done, create a launch template for your use case. This launch template should include instance type, key pair, VPC range, subnets, etc. The new EC2 instance will be launched from this template.

‍Step 6: Create a Replication Template

Similarly, create a replication template. This template will replicate your data to your new AWS environment.

‍Step 7: Launch an EC2 Test Instance

Once the templates are set up, launch an EC2 test instance from the boot disk of your source GCP VM instance. Take a snapshot of your instance to ensure data integrity. The test instance should launch successfully and match your original GCP VM. This is automated, no manual migration steps.

Once we launch a test EC2 instance, everything starts to happen automatically and the test EC2 instance is launched. Below is the automated process for launching the EC2 instance. See the screenshot.

Once the above is done, data is migrated from GCP to AWS using AWS Application Migration Service replication server. You can see the test EC2 instance in the AWS EC2 console as shown below.

Test EC2 instance configuration for your reference:

Step 8: Final cut-over stage

Once the cutover is complete and a new EC2 instance is launched, the test EC2 instance and replication server are terminated and we are left with the new EC2 instance with our custom configuration. See the screenshot below.

Step 9: Verify the EC2 Instance

Login to the new EC2 instance using RDP and verify all data is migrated. Verify all data is intact and accessible, check for any discrepancies. See our new EC2 instance below:

‍Step 10: Test Your Application

After verifying the data, test your application to see if it works as expected in the new AWS environment. We tested our sample web application and it worked.

Conclusion

Migrating a VM instance from GCP to AWS is a multi step process but with proper planning and execution it can be done smoothly. Follow this guide and your data will be migrated securely and your applications will run smoothly in the new environment.

At Ankercloud, our commitment to information security is reflected in our ISO 27001:2022 certification. Leveraging our expertise and advanced tools, we help other organizations achieve the same certification efficiently. With Vanta, we ensure a streamlined, automated, and effective compliance journey, showcasing our dedication to the highest standards of information security.

What is ISO 27001:2022?

ISO 27001:2022 is a global standard for managing and protecting sensitive company information through an Information Security Management System (ISMS). It ensures the confidentiality, integrity, and availability of data by providing a structured approach to managing information security risks.

The ISO 27001:2022 Process (Traditional Approach)

Obtaining ISO 27001 certification requires the following crucial steps

Preparation (1-3 months) 

Familiarize with the standard, define the scope, and Perform an initial gap analysis

Implementation (3-6 months) 

Develop an ISMS, conduct risk assessments, Implement necessary controls, and document policies

Internal Audit (1-2 months) 

Evaluate compliance with the ISMS and identify improvements

Management Review (1 month) 

Review ISMS performance and align with organizational objectives

Certification Audit (1-2 months) 

Engage a certification body for stage 1 (document review) and stage 2 (on-site assessment) audits

Post-Certification (Ongoing) 

Continuously monitor, conduct internal audits, and perform management reviews

In total, the process can take about 6 to 12 months, depending on factors like the organization's size, complexity, and preparedness.

How Vanta Simplifies ISO 27001:2022 Compliance

Vanta, a compliance automation platform, transforms the compliance process by automating security monitoring and evidence collection, making ISO 27001:2022 compliance more manageable. Here's how:

1. Automated Security Monitoring: Vanta continuously monitors your systems for security issues, ensuring you meet ISO 27001:2022 requirements without manual intervention.
2. Evidence Collection: Vanta automates 90% of the evidence collection, such as access logs, security configurations, and compliance status reports.
3. Compliance Management: A centralized dashboard helps manage and track compliance efforts, simplifying the process.
4. Risk Assessment: Vanta identifies vulnerabilities and risks, providing effective recommendations.
5. Automated Documentation: Generates and maintains required documentation for audits, reducing the manual workload.

With Vanta's automation approach, the ISO 27001:2022 certification process can be significantly expedited, allowing organizations to achieve certification in as little as 2 to 3 months. This accelerated timeline is made possible by Vanta's efficient, automated workflows and continuous monitoring, which streamline compliance tasks and reduce the time typically required for manual processes.

Benefits of Using Vanta Compliance Tools Compared to Traditional Methods

Vanta offers numerous advantages over traditional compliance methods:

1. Simplified Management and Guidance: Reduces complexities and provides step-by-step guidance, lowering the administrative burden.
2. Automated Detection and Proactive Assessment: Ensures timely identification and prioritization of security risks.
3. Real-time Dashboards and Streamlined Audits: Provides immediate visibility into compliance status and simplifies audit preparation.‍
4. Seamless Integration and User-Friendly Interface: Enhances workflow efficiency with seamless integration and an intuitive interface.‍
5. Enhanced Data Protection and Trust Building: Strengthens data protection and demonstrates strong security practices to stakeholders.‍
6. Time and Cost Savings with Continuous Monitoring: Automation reduces time and costs, while continuous monitoring ensures long-term security and compliance.

How Ankercloud Can Help Companies Achieve ISO 27001:2022 Certification Using Vanta

As ISO 27001:2022 certified lead auditors, Ankercloud enhances organizations' information security practices, ensuring compliance with legal and regulatory requirements. We equip organizations with the skills to effectively manage risks, fostering a proactive approach to data protection. Implementing ISO 27001:2022 can streamline operations, improve efficiency, and build trust with customers and stakeholders.

• Expert Guidance: Ankercloud's expertise guides companies through the ISO 27001:2022 process efficiently.
• Platform Utilization: Vanta's automation and monitoring tools streamline compliance.
• Customized Support: Tailored services meet specific company needs, ensuring comprehensive ISO 27001:2022 coverage.
• Accelerated Timeline: Vanta's automated processes and Ankercloud's expertise enable faster ISO certification.
• Continuous Improvement: Ankercloud helps maintain and improve ISMS post-certification, ensuring ongoing compliance and security.

Conclusion

Ankercloud's expertise, combined with Vanta's automation capabilities, offers a powerful solution for companies seeking ISO 27001:2022 certification. By streamlining the compliance process through automated security monitoring, evidence collection, and compliance management, Ankercloud helps companies achieve certification efficiently and effectively. Leveraging Vanta, Ankercloud ensures a smooth and cost-effective journey to certification, enhancing the overall security posture of your organization.

Understanding Generative AI

Generative AI is a revolutionary branch of artificial intelligence that has the capability to create new content, whether it be conversations, stories, images, videos, or music. At its core, generative AI relies on machine learning models known as foundation models (FMs). These models are trained on extensive datasets and have the capacity to perform a wide range of tasks due to their large number of parameters. This makes them distinct from traditional machine learning models, which are typically designed for specific tasks such as sentiment analysis, image classification, or trend forecasting. Foundation models offer the flexibility to be adapted for various tasks without the need for extensive labeled data and training.

Key Factors Behind the Success of Foundation Models

There are three main reasons why foundation models have been so successful:

1. Transformer Architecture: The transformer architecture is a type of neural network that is not only efficient and scalable but also capable of modeling complex dependencies between input and output data. This architecture has been pivotal in the development of powerful generative AI models.

2. In-Context Learning: This innovative training paradigm allows pre-trained models to learn new tasks with minimal instruction or examples, bypassing the need for extensive labeled data. As a result, these models can be deployed quickly and effectively in a wide range of applications.

3. Emergent Behaviors at Scale: As models grow in size and are trained on larger datasets, they begin to exhibit new capabilities that were not present in smaller models. These emergent behaviors highlight the potential of foundation models to tackle increasingly complex tasks.

Accelerating Generative AI on AWS

AWS is committed to helping customers harness the power of generative AI by addressing four key considerations for building and deploying applications at scale:

1. Ease of Development: AWS provides tools and frameworks that simplify the process of building generative AI applications. This includes offering a variety of foundation models that can be tailored to specific use cases.

2. Data Differentiation: Customizing foundation models with your own data ensures that they are tailored to your organization's unique needs. AWS ensures that this customization happens in a secure and private environment, leveraging your data as a key differentiator.

3. Productivity Enhancement: AWS offers a suite of generative AI-powered applications and services designed to enhance employee productivity and streamline workflows.

4. Performance and Cost Efficiency: AWS provides a high-performance, cost-effective infrastructure specifically designed for machine learning and generative AI workloads. With over a decade of experience in creating purpose-built silicon, AWS delivers the optimal environment for running, building, and customizing foundation models.

AWS Tools and Services for Generative AI

To support your AI journey, AWS offers a range of tools and services:

1. Amazon Bedrock: Simplifies the process of building and scaling generative AI applications using foundation models. 

2. AWS Trainium and AWS Inferentia: Purpose-built accelerators designed to enhance the performance of generative AI workloads.

3. AWS HealthScribe: A HIPAA-eligible service that generates clinical notes automatically.

4. Amazon SageMaker JumpStart: A machine learning hub offering foundation models, pre-built algorithms, and ML solutions that can be deployed with ease.

5. Generative BI Capabilities in Amazon QuickSight: Enables business users to extract insights, collaborate, and visualize data using FM-powered features.

6. Amazon CodeWhisperer: An AI coding companion that helps developers build applications faster and more securely.

By leveraging these tools and services, AWS empowers organizations to accelerate their AI initiatives and unlock the full potential of generative AI.

Some examples of how Ankercloud leverages AWS Gen AI solutions

1. Ankercloud has leveraged Amazon Bedrock and Amazon SageMaker which powers VisionForge which is a  tool to create designs tailored to user’s vision, democratizing creative modeling for everyone. VisionForge was used by our client ‘Arrivae’ a leading interior design organization, where we helped them with a  15% improvement in interior design image recommendations, aligning with user prompts and enhancing the quality of suggested designs. Additionally, the segmentation model's accuracy improvement to 65% allowed for a 10% better personalization of specific objects, significantly enhancing the user experience and satisfaction. Read more

2. Another example of using Amazon SageMaker, Ankercloud worked with  ‘Minalyze’ who are  the world's leading manufacturer of XRF core scanning devices and software for geological data display. We were able to create a ready to use and preconfigured Amazon Sagemaker process for Image object classification and OCR analysis Models along with ML- Ops pipeline. This helped Increase the speed and accuracy of object classification and OCR which leads to increased operational efficiency. Read more

3. Ankercloud has helped Federmeister, a facade building company, address their slow quote generation process by deploying an AI and ML solution leveraging Amazon SageMaker that automatically detects, classifies, and measures facade elements from uploaded images, cutting down the processing time from two weeks to just 8 hours. The system, trained on extensive datasets, achieves about 80% accuracy in identifying facade components. This significant upgrade not only reduced manual labor but also enhanced the company's ability to handle workload fluctuations, greatly improving operational efficiency and responsiveness. Read more

Ankercloud is an Advanced Tier AWS Service Partner, which enables us to harness the power of AWS's extensive cloud infrastructure and services to help businesses transform and scale their operations efficiently. Learn more here

As a pre-sales engineer, creating detailed architecture diagrams is crucial to my role. These diagrams, particularly those showcasing AWS services, are essential for effectively communicating complex infrastructure setups to clients. However, manually adding titles to each AWS icon in draw.io is repetitive and time-consuming. Ensuring that every icon is correctly labeled becomes even more challenging under tight deadlines or frequent updates.

How This Tool Solved My Problem

To alleviate this issue, I discovered a Python script created by typex1 that automates the title insertion for AWS icons in draw.io diagrams. Here’s how this tool has transformed my workflow:

1. Time Efficiency: The script automatically detects AWS icons in the draw.io file and inserts the official service names as titles. This eliminates the need for me to manually add titles, saving a significant amount of time, especially when working on large diagrams.
2. Accuracy: By automating the title insertion, the script ensures that all AWS icons are consistently and correctly labeled. This reduces the risk of errors or omissions that could occur with manual entry.

3. Seamless Integration: The script runs in the background, continuously monitoring the draw.io file for changes. Whenever a new AWS icon is added without a title, the script updates the file, and draw.io prompts for synchronization. This seamless integration means I can continue working on my diagrams without interruption.
4. Focus on Core Tasks: With the repetitive task of title insertion automated, I can focus more on designing and refining the architecture of the diagrams. This allows me to deliver higher quality and more detailed diagrams to clients, enhancing the overall presentation and communication.

Conclusion

The automated title insertion tool has significantly improved my efficiency and accuracy when creating AWS architecture diagrams in draw.io. By automating a repetitive and error-prone task, I can now focus on the more critical aspects of diagram creation, ultimately delivering better client results.

Credits

Special thanks to typex1 for creating this incredibly useful tool. The efforts and contributions of the open-source community continue to enhance our productivity and streamline our workflows. If you encounter any issues or have suggestions for improvements, feel free to reach out or file an issue on the GitHub repository.

By leveraging automation, we can eliminate mundane tasks and concentrate on delivering value through our technical expertise. Happy diagramming!

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In the fast-paced world of cloud computing, staying ahead of the curve is crucial for businesses striving to maximize their ROI. This entails not only harnessing the latest technologies but also ensuring that your cloud architecture is optimized for peak performance and cost-efficiency. At Ankercloud, we understand the significance of aligning your cloud infrastructure with your business objectives, which is why we advocate for the adoption of Well Architected Framework Review (WAFR) and proactive cost optimization strategies.

"Trying to navigate without a destination is like setting sail without a compass – you'll be adrift regardless of the map in your hands''. This adage rings true in the realm of cloud architecture. The WAFR serves as a compass, guiding organizations through a structured evaluation of their cloud environments across six key pillars: Operational efficiency, Security, Reliability, Performance, Cost Optimization, and Sustainability. By answering a series of targeted questions within each pillar, businesses gain valuable insights into areas of strength and opportunities for improvement.

But why is a WAFR necessary, you might ask? In a landscape where technological advancements are rapid and competition is fierce, a comprehensive architecture review is essential for informed decision-making. It enables organizations to identify and prioritize investments that will drive business success, ensuring that resources are allocated efficiently and effectively.

So why do Architecture Reviews not happen regularly? The answer lies in two main factors: technical debt and siloed priorities. Many organizations find themselves bogged down by legacy systems and processes, making it challenging to dedicate time for thorough architecture reviews. Moreover, when different departments operate in isolation, it can lead to misaligned priorities and missed opportunities for optimization.

At Ankercloud, we believe that architecture review is a collective responsibility that transcends departmental boundaries. By fostering a culture of openness and collaboration, organizations can leverage the diverse expertise of cross-functional teams to drive continuous improvement. The WAFR provides a common framework for these discussions, empowering teams to identify actionable insights and drive meaningful change.

In conclusion, conducting regular Well Architected Framework Reviews and prioritizing the findings are essential steps towards optimizing cloud performance and ensuring long-term business success. By staying vigilant, adaptable, and responsive to evolving needs, organizations can harness the full potential of the cloud to stay ahead of the competition.

Discover Ankercloud's WAFR on the AWS Marketplace today and unlock a new level of efficiency and optimization for your business. Click here  to explore now!

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Giving access to your AWS is simple and straightforward even if you are not experienced with AWS, just follow the step-by-step procedure explained below. This approach uses the official Identity and Access Management (IAM) service from AWS.

Note: the process here reported is specifically targeting the case in which you are giving access to your account to perform a Well-Architected Framework Review (WAFR), but it can be used for any other purposes. WAFR requires read-only access to both the AWS Management Console and the Command Line Interface (CLI) to your production account or production resources, as well as the Billing Dashboard (for cost-related recommendations).

Summary:

• Steps 1–11: Create an IAM user with the needed policies (ReadOnlyAccess, IAMUserChangePassword, AWSBillingReadOnlyAccess) and download access credentials
• Steps 12–15: Grant CLI access and download the access keys
• Steps 16–18: Make sure IAM access to the Billing dashboard is allowed

1. Open the AWS Management Console from a web browser and normally login into your AWS account with your Root user credentials

2. Access the IAM service.

The easiest way is to navigate to the top of the AWS console and type “iam” on the search bar, and select the first result.

3. Select the “Users” menu from the left pane

4. Once the IAM/Users page is open, navigate to the right side and select the “Add User” blue button.

5. Specify a User name in the apposite field

6. Select the “Provide user access to the AWS Management Console” box, and additional settings will appear (access to the Management Console is provided here; CLI access will be provided later).

Select “I want to create an IAM user”, leave “Autogenerated password” as default and tick the box “Users must create a new password at next sign-in” to allow the user to change the password after the first access.

Then click “Next”.

7. On the following page, select the “Attach policies directly” option to attach the necessary policies to the user.

8. Type “readonlyaccess” in the search bar to apply a filter, and navigate through the different pages by selecting a different page number on the top left. Find the ReadOnlyAccess policy and select it by ticking the box on the left.

9. Remove the “readonlyaccess” filter by clicking on the X…

…and repeat step 9–10 to find and attach the AWSBillingReadOnlyAccess policy.

Once you have selected all the 2 policies, click “Next”.

10. On the summary page, make sure to have all these 3 policies listed, then click “Create user”

11. Before exiting the page, make sure to press the “Download .csv file” containg the access details, and save it to emailing it later.

Then go back to the users list.

The IAM user creation is completed. The User has read only access to the Console and to the Billing dashboard, and is allowed to change the password on the first login.

To give also access to the CLI (required for WAFR), please follow the steps below.

12. From the list of active users in the account, enter the one just created by clicking on it, and switch to the “Security credentials” pane.

13. Scroll down until you find the “Access keys” section and select “Create access key”

14. On the next page, select “Command Line Interface (CLI)”. Click “Next” and then “Create access key”

15. Click on the “Download the .csv file” button and save the file with the credentials.

Now user credentials and Access keys have been created and downloaded, ready to be sent.

To make sure that the IAM User can access the Billing dashboard, make sure to enable the setting by following the next steps.

16. Click on your account name on the top-right and access the “Account” Section

17. Scroll down until you find the “IAM User and Role Acess to Billing Information” section, and click “Edit”

18. A new setting will appear on the bottom, check the box “Activate IAM Access” and click update.

Final step: to proceed with a Well-Architected Framework Review, email both the downloaded files containing the Username, Password, Console sign-in URL, Access key ID, Secret access key to the Solution Architect (or another person) who will work on your account.

AI/ML Transforming  Car Manufacturing

These days,when a new car rolls off the line at any major European car manufacturer, chances are, it had a little help from AI. Yep, artificial intelligence is shaking things up in the manufacturing world, making products faster and more accurately than ever before. It's like having a super-efficient robot buddy on the production line!

Ankercloud, a trailblazer in AI technologies, has been at the forefront of this transformation, empowering manufacturing industries to optimize processes, enhance productivity, and minimize downtime. Let's delve into how Ankercloud's AI/ML solutions are revolutionizing the manufacturing sector.

"It's not just about making things; it's about making things smarter."

Streamlining Operations with AI/ML

Ankercloud's expertise lies in harnessing AI algorithms to automate processes and derive actionable insights from vast volumes of data. By leveraging ML models developed specifically for manufacturing environments, Ankercloud enables companies to streamline operations, from supply chain management to production line optimization. Predictive maintenance, a cornerstone of Ankercloud's offerings, utilizes ML algorithms to anticipate equipment failures before they occur, thereby reducing unplanned downtime and maintenance costs.

Enhancing Quality Control

Quality control is non-negotiable in manufacturing, and Ankercloud's AI-powered solutions elevate this critical aspect to new heights. Through image object classification and Optical Character Recognition (OCR) analysis, Ankercloud enables manufacturers to detect defects in real-time, ensuring that only products meeting stringent quality standards reach the market. By automating quality control processes, Ankercloud not only minimizes errors but also accelerates time-to-market for new products.

Optimizing Resource Allocation

Efficient resource allocation is fundamental to maximizing productivity in manufacturing. Ankercloud's AI/ML solutions provide invaluable insights into resource utilization, enabling companies to allocate materials, manpower, and machinery optimally. By analyzing historical data and real-time operational parameters, Ankercloud empowers manufacturers to make data-driven decisions, thereby reducing waste and enhancing overall efficiency.

Predictive Analytics for Demand Forecasting

Anticipating market demand is key to maintaining a competitive edge in manufacturing. Ankercloud's predictive analytics capabilities utilize ML algorithms to forecast demand trends accurately. By analyzing historical sales data, market dynamics, and external factors, Ankercloud enables manufacturers to optimize production schedules, minimize inventory costs, and meet customer demand with precision.

The Business Impact

Ankercloud's AI/ML transformations in manufacturing yield tangible results that directly impact the bottom line. By optimizing processes, enhancing quality control, and enabling predictive analytics, Ankercloud empowers manufacturers to:

Increase Efficiency

Reduced downtime, streamlined operations, and optimized resource allocation lead to enhanced productivity and cost savings.

Improve Quality

‍Automated quality control processes ensure the delivery of high-quality products, enhancing brand reputation and customer satisfaction.

Optimize Costs

‍ Predictive maintenance and demand forecasting enable manufacturers to minimize maintenance costs, inventory holding costs, and waste, resulting in significant cost savings.

Stay Competitive

‍By leveraging AI/ML technologies, manufacturers gain a competitive edge through agility, responsiveness, and the ability to adapt to changing market dynamics.

In conclusion, Ankercloud's AI/ML solutions are driving a paradigm shift in the manufacturing sector, empowering companies to embrace digital transformation and thrive in an increasingly competitive landscape. With a focus on efficiency, quality, and innovation, Ankercloud is revolutionizing manufacturing processes and shaping the factories of the future.

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