Scaling Beyond Open Source: How Solvento Powers Logistics Data with Matia

Company: Solvento
Location: Mexico City, Mexico
Industry: Logistics Tech
Employees: 65
Solutions: ETL
Website: solvento.mx 

The Data Stack

ETL: Matia
Warehouse: Redshift
Transformation: dbt Cloud

The Company

Solvento is a logistics technology company based in Mexico that provides automation solutions to streamline payment processes for freight companies. Their platform focuses on optimizing freight payments, improving cash flow for trucking companies, and ensuring that carriers are paid on time. Reliable data infrastructure is critical to supporting their operations, enabling real-time transaction tracking and operational analytics.

The Challenge

Before adopting Matia, Solvento relied on an open-source solution for ETL.  While it initially met their needs, it became increasingly difficult to scale and maintain. Their existing data ingestion processes faced significant hurdles:

• Reliability Issues: Solvento was leveraging OS tools to replicate PostgreSQL and mySQL databases to Redshift, and experiencing frequent connection errors
• Delayed Data Syncing: Syncing logistics transactions to their data warehouse was delayed, halting key operational insights and data movement.
• Hands-On Support: Matia provided dedicated support, ensuring issues were resolved quickly. Tickets were typically acknowledged within 30 minutes and resolved within 48 hours
• Increasing Warehouse Costs: Solvento was seeing it’s warehouse costs creep up, based on increase in data volumes; they knew there was room to optimize this spend 

For Solvento, ensuring real-time logistics data integrity was essential to maintain trust with their customers and optimize cash flow management for trucking companies.

"We were tired of sacrificing speed for reliability when it came to our data architecture. With Matia, we found both.” - Felipe Veloso, Data Architect, Solvento

The Solution

Solvento turned to Matia to modernize its data stack and ensure logistics data reliability. Before Matia, they were using an open-source solution, which became increasingly difficult to scale as their operations grew. They needed a tool that could handle high data volumes without performance bottlenecks and alert their lean team to any abnormalities in the data. Key reasons for selecting Matia included:

• Scalability & Reliability: Matia’s ingestion solution offered a robust and scalable way to process data, minimizing pipeline failures. Even if they moved over to the  managed solution version of the legacy open-source tool they were on, they still anticipated issues with pipeline failures when schemas changed and other updates were made. 
• Improved Data Processing: By switching to Matia, Solvento saw a significant reduction in data sync times, improving real-time logistics tracking.
• Hands-On Support: Unlike their previous open-source tool, Matia provided dedicated support, ensuring issues were resolved quickly. Tickets were typically acknowledged within 30 minutes and resolved within 48 hours
• Customizability & Agility: Matia’s team worked closely with Solvento to adapt features to their specific logistics data needs, ensuring a tailored solution that fit their workflows.
• Streamlined DataOps Process: The Solvento team was able to consolidate their key data processes to one tool, helping easily surface platform issues and inefficiencies in one place.

The Impact

Since adopting Matia, Solvento has significantly improved its logistics data operations, resulting in tangible business benefits:

• 20% Reduction in Warehousing Costs: Optimized data ingestion led to more efficient storage and processing in Redshift, reducing operational expenses.
• 63% Faster Sync Times: Reduced the time required to process logistics data, enabling real-time tracking and decision-making.
• 50% Reduction in Incident Response Time: Leveraging the centralized UI for Issues & Notifications, the Solvento team was able to get alerted to integration failures and schema changes in real time and surface solutions, allowing them to cut incident response time in half.
• Enhanced Data Accuracy: Eliminated data inconsistencies, leading to more precise freight payment tracking and fewer reconciliation issues.
• Increased Operational Efficiency: Freed up engineering resources to focus on innovation rather than constant troubleshooting.
• Stronger System Reliability: Improved data pipeline stability has minimized downtime and enhanced overall business continuity.

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