ziggiz is a technology company specializing in a semantic data platform that transforms enterprise data management. By leveraging intelligent automation, their platform helps large multinational and non-profit healthcare organizations consolidate, analyze, and act on data from diverse sources. SnapSoft, an AWS Premier Tier Consulting Partner, was engaged to migrate ziggiz.ai’s existing infrastructure from Azure to AWS, establish a robust, scalable, and automated environment, and empower ziggiz.ai to scale their solution to a growing number of clients.

ziggiz.ai's ambitious growth plans required a cloud infrastructure that could scale rapidly, handle complex data workloads, and meet stringent compliance requirements, including those for government and healthcare sectors. Their existing setup on Azure, while functional, lacked the automation and flexibility needed to efficiently onboard new clients and manage their infrastructure at scale. The primary challenges included:

• Migration complexity: Moving critical services like their containerized application, Databricks analytics, and SQL database from Azure to a new AWS environment.
• Scalability for new clients: The need for a standardized, automated process to provision new client environments quickly and securely.
• Compliance and security: Establishing a robust security and compliance posture with AWS best practices, including detailed logging and configuration monitoring for regulated industries like healthcare and government (GovCloud).
• Operational efficiency: The lack of a comprehensive CI/CD pipeline to automate infrastructure and application updates, which led to manual, time-consuming processes.

ziggiz.ai chose AWS for its unparalleled breadth of services and its mature ecosystem for data, AI, and security. AWS offers a robust and highly-scalable environment that is essential for a data-intensive platform like ziggiz.ai. Specific advantages of AWS included:

• Comprehensive Data & AI services: AWS offers a wide array of services like Kinesis, Firehose, MSK, and Bedrock, which are critical for ziggiz.ai's data streaming, delivery, and AI/ML initiatives.
• Robust Security and Compliance: The ability to implement an AWS Landing Zone, along with services like AWS Organizations, CloudTrail, and Config, provides a strong foundation for security, governance, and compliance, including specific support for GovCloud.
• Scalability and automation: AWS’s services are designed for automation and Infrastructure as Code (IaC), which was crucial for ziggiz.ai’s goal of a fully managed, multi-tenant environment.

ziggiz.ai selected SnapSoft for its deep expertise as an AWS Advanced Tier Services Partner with a proven track record in complex cloud migrations and the implementation of automated, secure cloud architectures. SnapSoft’s collaborative approach and technical proficiency in IaC and CI/CD were key factors. SnapSoft was the ideal partner to not only execute the migration but also to build the foundational architecture that would support ziggiz.ai’s long-term business strategy. Their expertise in the data & AI and app dev service lines perfectly aligned with the project's requirements.

• AWS Organizations
• AWS CloudTrail
• AWS Config
• AWS Batch
• AWS Bedrock
• AWS CloudFront
• Amazon Route53
• Amazon VPC
• Amazon ECR (Elastic Container Registry)
• Amazon RDS (Relational Database Service) for PostgreSQL
• Amazon Kinesis
• Amazon Firehose
• Amazon MSK (Managed Streaming) for Apache Kafka
• GitHub Actions
• Databricks on AWS

SnapSoft implemented a phased approach to systematically migrate ziggiz.ai’s infrastructure and establish a scalable, automated AWS environment. The strategy focused on building a secure foundation first, then migrating the core workload, and finally, establishing an automated pipeline for future growth.

Proposed Solution & Architecture

• AWS Landing Zone: A refined AWS Landing Zone design was implemented using IaC. This included setting up AWS Organizations and Accounts for efficient management, CloudTrail for API logging, and AWS Config for compliance monitoring. Specific attention was paid to the design for potential future GovCloud and commercial environments.
• Azure Workload Migration: The core Azure services were migrated to AWS. This included:
  • Container services moved to Amazon ECS.
  • Databricks analytics configured on the AWS platform.
  • The SQL database was migrated to a managed PostgreSQL service.
  • Storage accounts were transitioned to Amazon S3.
  • Cognitive services calls were updated to utilize AWS Bedrock, aligning with their AI strategy.
• Data Analytics Platform Setup: A new data analytics platform was provisioned. This involved:
  • Setting up Databricks.
  • Provisioning a streaming platform using Kinesis, Firehose and MSK for efficient data delivery.
• CI/CD Automation: A robust CI/CD pipeline was built using GitHub Actions. This pipeline was designed to:
  • Automate the provisioning of new client accounts and their specific infrastructure.
  • Apply infrastructure updates to customer environments with automation.
  • Streamline software code updates for the Golang application.
• Application Code Updates: The existing Golang application code was updated to natively support and integrate with the new AWS services, ensuring a seamless transition and optimal performance.

The partnership with SnapSoft enabled ziggiz.ai to successfully migrate their infrastructure and build a foundation for future growth. The project's completion resulted in several key benefits:

• Accelerated Client Onboarding: The automated Landing Zone and CI/CD pipeline allow ziggiz.ai to provision new client environments in a fraction of the time, significantly reducing their go-to-market timeline.
• Enhanced Security and Compliance: The new AWS architecture, with its built-in logging and monitoring, provides the necessary governance and security posture to serve highly-regulated industries.
• Improved Operational Efficiency: Automation for infrastructure and software updates has freed up ziggiz.ai’s engineering team from manual tasks, allowing them to focus on product innovation.
• Cost Optimization: The included cost-saving exercise helped ziggiz.ai identify and implement optimizations, ensuring they have a cost-effective and scalable infrastructure.
• Future-Proofed Platform: The migration and new architecture have positioned ziggiz.ai to fully leverage the AWS ecosystem, including future services and innovations, to stay ahead of the curve in the data and AI space.