Introduction
A sophisticated B2B services company needed to migrate from a fragmented and costly data infrastructure to a modern, scalable, and cost-efficient solution. Their existing setup relied on legacy systems and outdated data pipelines, leading to high operational costs, slow data processing, and limited analytics capabilities.
To address these challenges, the company partnered with Actable to implement a cloud-based data warehouse migration that would streamline data processing, improve speed to insights, and lay the foundation for advanced AI-driven analytics.
The Challenge: Legacy Systems Slowing Growth
The company’s existing AWS-based data environment was expensive, inefficient, and difficult to scale. Their data processing workflows were riddled with issues, including:
- High operational costs associated with their existing Redshift environment, which struggled to handle large-scale data consumption.
- Slow processing speeds, with data latency extending up to 45 minutes for ingestion into their analytics stack.
- Complex, disjointed reporting systems, make it difficult for business users to access and act on insights in real-time.
- Limited automation, forces teams to rely on manual intervention for critical data updates.
To meet their growth and innovation goals, the company needed to transition to a modern data architecture that was faster, more efficient, and optimized for AI-driven analytics.
The Solution: A Scalable Data Warehouse in Google Cloud
Actable designed and implemented a Google Cloud-based data warehouse migration, replacing the company’s existing infrastructure with a cost-effective, scalable, and high-performance solution.
Key Solution Elements:
- Migration from AWS Redshift to Google BigQuery, optimizing all data structures for enhanced performance and lower cost.
- Implementation of Google Datastream and Dataform, providing a robust CDC (Change Data Capture) SQL-based processing pipeline.
- Automation of data workflows, reducing manual effort and enabling more seamless data ingestion and transformation.
Actable also provided a strategic roadmap to guide future AI-driven analytics adoption, ensuring the new infrastructure could support machine learning, conversational analytics, and predictive modeling.
The Results: Faster Insights, Lower Costs, and AI Readiness
By implementing a modern data warehouse solution, the company achieved:
- Over 200 data processing jobs successfully migrated, ensuring seamless operations without disruption.
- Reduced data latency from 45 minutes to under one minute, providing real-time access to critical business insights.
- A future-proof foundation for AI-driven analytics, accelerating innovation in machine learning, GenAI, and conversational analytics.
With a streamlined and automated data pipeline, the company now operates with greater agility, lower costs, and improved decision-making capabilities.
Why This Matters: Laying the Groundwork for AI and Advanced Analytics
For organizations relying on legacy data architectures, the shift to modern cloud-based data warehouses is essential for:
- Reducing operational costs while improving scalability
- Speeding up access to real-time insights for better decision-making
- Creating a strong data foundation for AI and machine learning adoption
By transitioning to a BigQuery-powered data warehouse, this company has future-proofed its data strategy, unlocking new levels of efficiency, accuracy, and business intelligence.
Conclusion: From Data Bottlenecks to AI-Powered Insights
This case study highlights how strategic cloud migration can transform data operations, helping enterprises move from slow, outdated infrastructure to an agile, AI-ready data ecosystem.
With their newly optimized data pipeline, this company can now:
- Deliver insights in real-time
- Reduce costs associated with inefficient data processing
- Scale analytics capabilities with AI-driven innovation
By modernizing their approach, they are no longer just managing data—they are activating it for smarter business decisions.
