Doctor Database
+41 / 78 696 1210
Our performance optimization service follows a structured and methodical approach to identify and resolve the most critical performance bottlenecks in your database systems. We break down the optimization process into clear, actionable tasks to ensure comprehensive coverage and measurable improvements. The following steps highlight our systematic approach to enhancing your database's efficiency:
Effective data management is crucial for any organization, especially as data complexity and volume grow. Best practices are vital not just in large environments but also in smaller settings with limited resources. Implementing these practices ensures data is organized, secure, and accessible, providing a foundation for growth. Key considerations include:
Ensuring data quality is crucial, especially in finance. While best practices exist, our approach is tailored to each client's unique environment and needs. Our strategy includes initial data profiling, ongoing validation, and continuous monitoring to maintain high standards. The following steps outline our methodical approach, focused on your business's specific requirements.
Transitioning to a new database platform? We ensure a seamless migration, whether you’re moving from a NoSQL database like MongoDB to a relational system or upgrading to a more powerful environment. Our meticulous approach carefully manages every aspect of the migration to avoid data loss, maintain data integrity, and ensure minimal disruption to your operations.
Our software architecture and .NET development services design and implement tailored solutions aligned with your company's specific needs. Combining deep technical expertise with business acumen, we create scalable, maintainable, high-performance applications. By leveraging modern architectures like microservices and event-driven systems, and modernizing legacy systems, we ensure seamless, future-ready transformations.
We support your company in successfully introducing and implementing agile methods at all levels. With expertise in Scrum, DevOps, Lean Portfolio Management, and agile transformation, we offer tailored solutions aligned with your specific needs. Our approach blends strategic planning with practical execution to establish efficient, flexible, high-performing teams and processes, ensuring sustainable success.
Company A, a dynamic hedge fund, faced significant performance bottlenecks during their end-of-day processing. Each evening, they loaded large volumes of market price data from external sources like Bloomberg, crucial for next-day trading decisions. As the data grew—covering more markets, history, and sources—the ingress process slowed dramatically. The system became prone to locking and blocking issues, delaying data availability and threatening the fund's ability to react swiftly to market changes.
Doctor Database was called in when the hedge fund's traders noticed delays in receiving critical market insights. Our team conducted an in-depth analysis of the system’s data flow and locking patterns. They uncovered that the legacy relational data model and direct data access methods were exacerbating the locking conflicts, leading to significant performance degradation and bottlenecks during peak processing times.
Doctor Database proposed a methodical treatment plan. They started by abstracting data access into stored procedures, reducing dependencies between applications. Next, they reworked the legacy relational data model, leveraging advanced database features like partitioning, materialized views, and filtered indexes. This approach allowed the hedge fund to grow its data volume without compromising performance. The result was a significant reduction in blocking issues, ensuring the fund could process data and make informed trading decisions without delay, enhancing overall system efficiency.
Company B, a rapidly expanding tech firm, relied heavily on a MongoDB database central to its operations. Over time, they encountered significant challenges as the MongoDB instance, treated almost like a black box, became difficult for data analysts to use with standard business intelligence tools. Additionally, emerging business requirements began to outpace the capabilities of the existing system, further complicating their data management and analysis efforts.
Recognizing these growing inefficiencies, Company B’s leadership sought out Doctor Database. The team approached the problem with precision, diving deep into the MongoDB instance to understand its information model. They quickly identified limitations in data accessibility and scalability, which were hampering the company's ability to make data-driven decisions. These issues underscored the urgent need for a more flexible and efficient data architecture.
Leveraging their expertise, Doctor Database meticulously translated the MongoDB model into an efficient, flexible MS SQL Server relational model. This new model supported new business requirements and provided a logical view layer tailored to the data analysts’ needs. Additionally, Doctor Database implemented a custom API layer, seamlessly integrating with the analysts' tools for smooth data access. The outcome was a robust, scalable system that unlocked the full potential of the company’s data, enabling more effective analysis and decision-making, empowering Company B's growth.
After optimizing their end-of-day processing system, Company A faced a new challenge in maintaining accurate and reliable market data. As their data sources expanded, discrepancies began to appear in their financial models due to variations in market prices reported by different providers. These price observations, subject to changes over time and delays, introduced inconsistencies, further complicating the reliability of their data and impacting decision-making processes.
Traders and analysts at Company A struggled with these inconsistencies, as their financial models produced different results based on varying data from multiple sources. The inconsistent data made it difficult to make confident trading decisions. Realizing the significant impact on their operations, Company A turned to Doctor Database for a solution, hoping to bring clarity and consistency back to their market data analysis.
Our expert approached the challenge with methodical precision. They identified the core issue as not just data discrepancies but the inability to manage and reconcile revisions effectively. To address this, they introduced a CQRS (Command Query Responsibility Segregation) layer into the system. This layer aggregated price events, creating different time-perspective views. It also acted as a gatekeeper against noise. This enhancement allowed better reconciliation of differences, ultimately strengthening the foundation of consistent and reliable data processing.
Phaiston IT Consulting GmbH, the company behind Doctor Database, was founded in 2018 with the mission to deliver unparalleled expertise in database consulting. With over 25 years of experience across various industries, we provide tailored solutions to regional, national, and international clients, ensuring they achieve optimal database performance, data integrity, and strategic success.
At Doctor Database, we’re dedicated to being a reliable partner from the initial concept through to project completion and beyond. We offer personalized solutions, guiding clients through informed choices with transparency and integrity. By understanding challenges through our clients' eyes and leveraging our deep expertise, we turn data into success while staying at the forefront of technology and innovation.
Frank Foerster, Founder and Principal Consultant
With over 25 years of experience in database consulting, Frank Foerster has a diverse background in
technology, finance, and international business.
A concise overview of his career highlights follows.