15+ engagements across Cloud & AI, Gaming, Logistics, and more — each one starting with a concrete problem and ending with a verified result.
Over 10,000 peering partners, 1 billion+ end consumers, and a platform running across AWS RDS, EC2, Google Cloud, Kubernetes, and on-premises simultaneously.
A production PostgreSQL cluster needed to move from on-premises to AWS while the platform handled 2,000–16,000 transactions per second across multiple environments. The goal: let AWS manage 90% of the workload. The constraint: no downtime, no data loss.
PostgreSQL was technically "up" — but the system was silently approaching its real limits. CPU saturation on the primary, growing replication lag, read replicas falling behind, and connection spikes from auto-scaling were forming a chain reaction.
If the primary failed, replicas were too far behind to promote safely. The likely outcome: full platform outage with possible data loss. The chain was already in motion.
ClickHouse powered the billing system — collecting all client usage data and generating invoices. Data volume and query concurrency were growing steadily, and billing cycles were causing CPU and memory spikes that threatened core platform stability.
A ClickHouse instance serving real-time analytics began showing signs of resource exhaustion under peak load — CPU near saturation, memory pressure climbing, and query queues growing. The system had not yet failed, but failure was imminent.
Database operations were fully reactive. Engineers constantly switched between feature work and incident response, with no clear ownership of PostgreSQL or ClickHouse in production.
Burnout, slow recovery times, and repeated incidents caused by the same root issues going unfixed. Every on-call rotation was a firefight.
Took ownership of both PostgreSQL and ClickHouse in production. Shifted focus from reaction to prevention — establishing change control, predictable maintenance windows, and proactive monitoring across both engines.
The platform's MySQL database was reaching its limits for complex analytical queries. The decision was made to migrate to PostgreSQL for better query planning, native replication features, and long-term maintainability — without any service interruption.
A ClickHouse cluster holding ~500 GB of billing data (5+ years of records) needed to move from bare-metal on-premises infrastructure to Kubernetes — under constant production load.
A MariaDB production system (~200 GB) handling ~18,000 queries/sec across SaaS, Gaming, and Payments workloads. Binlogs were growing uncontrollably and lock contention was blocking critical writes — pressure building toward a full failure.
A 5 TB PostgreSQL cluster (1 primary + 2 replicas) serving a SaaS platform. Backups were being created on schedule — but had never been tested. Nobody knew if they would actually restore.
A production PostgreSQL system under constant SaaS load. Standard monitoring was in place — but issues were only detected after users noticed them. Alerts were either missing or too noisy to act on.
300+ active partners, multiple production environments, and databases ranging from 10 GB to 32 TiB — all requiring zero-downtime operations across every change.
50 production databases (10 GB to 32 TiB each) running at 500–15,000 TPS needed to move from Google Cloud to AWS. Any downtime would directly affect 300+ active partners and their end users.
A production database growing past 2 TB with a mix of active transactional data and years of historical records (transactions, logs, billing). Query performance was degrading and backups were becoming unmanageably slow.
50 databases, 30 users, multiple environments (prod/stage), and multiple regions. Every access change was handled manually per-database with no central control — a fragmented and error-prone process.
A production MySQL system under constant load. Standard monitoring dashboards were in place, but they provided too little visibility into what actually mattered — slow queries, lock contention, and replication health.
Millions of customers worldwide, a decade of historical operational data, and no analytical system capable of querying it efficiently.
The company had 10+ years of historical operational data but no system able to query it at scale. Legacy systems were not optimized for analytics, making large dataset queries slow or impractical.
One of the world's largest logistics operators with over €80 billion in annual revenue. AG Data worked with a specific department in their UK operations, advising on database architecture and performance for high-throughput internal systems.
A specific department within a major global logistics company needed independent expert guidance on their database architecture. Internal teams had grown organically and the database layer had accumulated technical debt — schemas designed for earlier scale were struggling under current throughput.
A B2B2C ERP platform serving over one million end users through a network of business partners. Database reliability directly translates to uptime for every partner and their customers downstream.
A multi-tenant ERP platform with over one million users required consistent database reliability across all tenants. Any degradation at the database layer would surface as slow response times or errors for a broad user base spread across dozens of business partners.
A fintech company running financial transaction processing on MySQL. They needed the same class of zero-downtime migration that AG Data had delivered for larger platforms — applied to their stack.
A fintech company needed to migrate their MySQL database to a new environment without interrupting live transaction processing. Financial systems have zero tolerance for data inconsistency, and any downtime during a migration window would directly affect revenue and customer trust.
A manufacturing company that had been attempting to migrate their Oracle database to the cloud for months — until a major incident took them offline for 2 days and they called AG Data.
During a months-long self-managed migration attempt, the team accidentally deleted half of Oracle's core services. The database went down entirely, taking the business offline for 2 days. AG Data were called in to rescue and complete the migration.
Whether you're planning a migration, dealing with a performance issue, or want a senior DBA on retainer — we start with a free 30-minute discovery call.