Business Context and Challenges
A rapidly growing e-commerce company operates in over 100 countries with a growing customer base of over 2 million. The company runs both a direct-to-consumer (DTC) business through its self-managed warehouses and a marketplace model that enables domestic merchants to access international markets. Their logistics infrastructure supports end-to-end services, including warehousing, customs clearance, and localized operations.
As the business scaled, data became a critical asset for decision-making. The company needed a real-time data infrastructure to power various business functions, including:
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User Behavior Analysis & Targeted Marketing: Tracking user interactions—browsing, clicks, cart additions, and purchases—to build customer profiles and enable personalized recommendations.
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Real-Time Inventory Management: Synchronizing stock levels across multiple warehouses, triggering restocks automatically, and preventing stockouts or overstocking.
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Supply Chain & Logistics Optimization: Enhancing procurement planning, route optimization, and warehouse layout to improve delivery speed and reduce costs.
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Sales & Profitability Analysis: Generating multidimensional insights into sales performance by product, brand, region, and time to refine marketing and pricing strategies.
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Risk Management & Early Warning Systems: Monitoring key business metrics,including inventory anomalies, financial transactions, and customer complaints, to proactively identify and mitigate risks.
However, their existing data architecture struggled to keep up with these needs.
Existing Architecture and Pain Points
The company’s legacy system consisted of multiple databases—including PostgreSQL, MySQL, and Elasticsearch—each serving different business functions. This fragmented approach introduced several operational bottlenecks:
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Complex Architecture & High Maintenance Costs: Maintaining multiple databases led to high development and operational overhead. Data synchronization required complex ETL processes, which often resulted in inconsistencies and delays.
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Performance Bottlenecks: PostgreSQL's performance degraded under heavy query loads, making it unsuitable for large-scale analytics. Slow queries, particularly in logistics and inventory management, disrupted business operations.
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Limited Elasticity: Scaling the system was cumbersome. Expanding cloud resources required manual intervention, and existing cloud-based solutions failed to provide real-time scalability.
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Inflexible Data Analysis: Analysts were forced to extract data manually and merge datasets in spreadsheets (e.g., using VLOOKUP for return analysis). This was due to data silos across MySQL and PostgreSQL, preventing seamless data aggregation.
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Real-Time Decision-Making Constraints:
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Warehouse Replenishment: Slow queries affected real-time stock tracking,delaying restocking decisions.
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Logistics & Package Tracking: Inefficient lookups for package routing caused delays in the shipping process.
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Advertising Optimization: Real-time ad performance analysis was limited, delaying budget adjustments across channels.
Solution: Unifying Real-Time Data with Tacnode
To overcome these challenges, the company adopted Tacnode as its core data platform. By replacing multiple databases with a single, PostgreSQL-compatible, cloud-native system, Tacnode delivered:
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Unified Data Management: Replacing PostgreSQL, MySQL, and Elasticsearch with Tacnode simplified the architecture, reducing operational complexity and data inconsistencies.
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Distributed Transactional Consistency: Tacnode ensured data integrity across high-concurrency workloads, eliminating inconsistencies caused by batch-based ETL.
Real-Time OLAP & Query Performance Optimization:
- Columnar storage and query optimizations improved analytics speed, making complex, multi-dimensional queries up to 9x faster.
- In logistics, query times for package tracking dropped from 147 seconds to 14 seconds—a 10x improvement in efficiency.
High-Concurrency Search & Large-Scale Data Processing:
- Tacnode's Omni-Search enabled hybrid queries across structured, semi-structured, and vector data, accelerating product search and behavior-driven recommendations.
- Real-time advertising performance tracking allowed instant ROI evaluations, enabling dynamic budget reallocations.
Elastic Scaling & Multi-Cloud Support:
- Compute-storage separation ensured smooth workload isolation, preventing slow analytics queries from affecting real-time operations.
- Scaling can be accomplished in seconds, maintaining performance even under sudden traffic spikes.
- Seamless data migration capabilities allowed the company to transition from its existing cloud provider to AWS without data loss.
Business Impact & Results
- Lower Operational Costs & Simpler Architecture
- Eliminated redundant databases, reducing cloud costs and ETL maintenance.
- Automated data synchronization, removing the need for manual extraction and spreadsheet-based analysis.
Significant Query Performance Gains
- 9x speed improvement in complex analytics queries.
- 10x reduction in package tracking query latency, leading to faster logistics processing.
Real-Time Insights & Decision-Making
- Business intelligence query response times were reduced from 5 days to 10 minutes, enabling faster strategic adjustments.
- Inventory adjustments and supply chain optimization were performed in real time, reducing stockouts and excess inventory.
Seamless Scaling & Multi-Cloud Flexibility
- High-concurrency workloads handled efficiently with Tacnode’s distributed processing engine.
- Migration from an existing cloud provider to AWS was completed without operational disruptions.
By adopting Tacnode as a unified real-time data platform, this global e-commerce leader was able to streamline its architecture, enhance decision-making speed, and improve operational efficiency—transforming its ability to scale in a competitive market.