Rakam is an incredible modular analytics platform that allows you to tailor your analytics services to fit your requirements. This blog post will guide you through the steps of deploying Rakam, understanding its features, and troubleshooting common issues to ensure you have a smooth experience.

Getting Started with Rakam

Rakam offers a range of features that help you collect, analyze, and visualize data from various sources effectively. Here’s how you can get started:

Standard Workflow with Rakam

• Collect data from multiple sources using trackers, client libraries, webhooks, tasks, etc.
• Enrich and sanitize your event data using event mappers.
• Store data in a data warehouse (PostgreSQL, Snowflake, S3, etc.) for later analysis.
• Analyze your event data using SQL queries and integrated analytics APIs with Rakam Cloud, including funnel, retention, segmentation reports.
• Customize Rakam by developing your own modules via customization features.

Deployment Options

Rakam offers flexible deployment options suited for your needs. Here are some popular methods:

Deploying on Heroku

You can easily deploy Rakam to Heroku using the Heroku button. This will set up a web app that uses the Heroku PostgreSQL add-on.

Running Rakam with Docker

Rakam can be run in a local environment using Docker. Here is an analogy to simplify the process:

Imagine Rakam as a restaurant where you are the chef. The Docker is your kitchen, and the databases are the storage rooms with ingredients. You make sure all your ingredients (data) are well-organized and accessible before you start cooking (analyzing) your meals (reports).

To set up your Docker environment, run the following commands:

docker run -d --name rakam-db -e POSTGRES_PASSWORD=dummy -e POSTGRES_USER=rakam postgres:10.1
docker run --link rakam-db --name rakam -p 9999:9999 -e RAKAM_CONFIG_LOCK__KEY=mylockKey -e RAKAM_CONFIG_STORE_ADAPTER_POSTGRESQL_URL=postgres:rakam:dummy@rakam-db:5432 rakam/burembarakam

After starting the Docker container, visit http://127.0.0.1:9999 and follow the instructions to complete the setup.

Using AWS with Terraform

For production environments, deploying Rakam using Terraform is recommended as it simplifies load-balancing and failover processes. Check the repository here.

Common Troubleshooting Ideas

If you encounter issues while setting up or using Rakam, here are some troubleshooting tips:

• Ensure that your Docker containers are running properly and that they are linked as intended.
• Double-check PostgreSQL version compatibility; Rakam supports PostgreSQL 11 due to specific performance features.
• If there are environment variable issues, ensure that you are setting the RAKAM_CONFIG property correctly in your configuration files or using the environment variable method effectively.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Rakam offers a comprehensive suite of tools for data analysis, enabling you to convert raw data into valuable insights. By following this guide, you can efficiently deploy and utilize Rakam for your analytics services.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.