Success Story

• The client is a global event and hospitality technology company. They offer a platform to help organizations manage the full event lifecycle across in-person, virtual, hybrid, and webinar formats. It includes tools for registration, engagement, budgeting, analytics, etc., all designed to maximize event impact and ROI. It is scalable, integrated, and offers 24/7 support.
• The platform connects users to a supplier network with nearly 340,000 global venues. Its hospitality cloud offers digital marketing, RFP management, and business intelligence to help venues reach over 160,000 planners.

The organization needed a unified approach to maintain accurate enterprise data. The following were the detailed requirements:

• Consolidate data from newly acquired and merged companies into a centralized system.
• Update organizational properties and hierarchies to support custom reporting requirements.

We designed and implemented a unified analytics platform to consolidate analytics data from multiple sources, including product databases, APIs, Salesforce, Google Analytics, and HubSpot. The Cybage team undertook the following initiatives.

• Modernized the data architecture by transitioning from ETL to ELT and migrating data and workloads from AWS Redshift, MySQL, PostgreSQL, and external APIs to Snowflake.
• Rebuilt data pipelines using Apache Airflow, with an AWS S3-based data isolation layer.
• Designed a layered Snowflake data warehouse architecture and optimized data ingestion using the Parquet file format over the traditionally used CSV.
• Developed Snowflake SQL transformations and updated queries to suite the Snowflake environment.
• Built modular DBT models to support data standardization, improve query performance, and simplify ongoing maintenance.
• Consolidated reporting and analytics by migrating dashboards from existing the Tableau setup and Looker to AWS Sigma, enabling direct Snowflake-based exploration and standardized enterprise reporting.

• Improved query performance enabled faster access to business insights and enhanced reporting responsiveness.
• Reduced overall data platform costs through pay-per-second billing and decoupled storage in Snowflake, utilizing AWS data analytics solutions for higher efficiency.
• Accelerated development and iteration cycles in AWS Sigma compared to Tableau.
• Optimized queries lowered compute consumption, further decreasing Snowflake usage costs.
  improved data traceability and quality control through a well-defined separation of staging, transformation, and reporting layers.
• Simplified change management and accelerated deployments with Modular DBT data models, providing better access to object lineage.
• Reduced duplication and simplified long-term maintenance with reusable and standardized code