B2B E-Commerce Data Platform Modernization with Tacnode

Business Context and Challenges

The client, a leading B2B e-commerce platform, connects global small and medium-sized enterprises (SMEs) with manufacturers and suppliers. The platform handles millions of transactions daily, requiring a robust data infrastructure to power its marketplace operations,recommendation systems, advertising optimization, fraud detection, and customer relationship management (CRM).

How the Company Uses Data in Its Business

Data plays a central role in the company's e-commerce ecosystem, influencing multiple aspects of its operations:

  • 1. Personalized Product Recommendations

    • The platform processes user behavior data: including browsing history, purchase patterns, and search queries, to curate personalized product suggestions.

    • Data is analyzed: to understand shopping preferences and offer recommendations that align with user needs.

    • The company continuously refines its recommendation logic: to enhance customer engagement and increase conversion rates.

  • 2. Dynamic Pricing and Inventory Optimization

    • The company monitors demand: competitor pricing, and inventory levels to adjust product pricing dynamically.

    • Supply chain data is analyzed: to forecast stock requirements and prevent both overstocking and stockouts.

    • Pricing strategies are optimized: to maintain competitiveness while ensuring profitability.

  • 3. Multi-Channel Marketing and Advertising Optimization

    • Customer segmentation is performed: using historical purchase data, engagement metrics, and demographic insights.

    • Advertising campaigns are executed: across search engines, social media, and partner websites based on user profiles and past interactions.

    • Marketing performance is evaluated: to fine-tune ad spend and improve return on investment.

  • 4. Fraud Detection and Risk Management

    • Transaction patterns are continuously assessed: to identify anomalies that may indicate fraudulent activity.

    • The system flags suspicious behaviors: such as unusual purchase volumes or inconsistent user actions, for further review.

    • Risk assessments help: in preventing chargebacks and unauthorized transactions,protecting both buyers and sellers.

  • 5. Logistics and Supply Chain Optimization

    • The platform integrates with logistics partners: to track shipments and predict delivery timelines.

    • Warehouse data is analyzed: to improve fulfillment efficiency and ensure timely order processing.

    • Supply chain insights support: smarter inventory distribution, reducing delays and lowering costs.

  • 6. CRM and Customer Support Enhancements

  • Customer interactions across multiple channels, including email, chat, and phone, are centralized for a cohesive support experience.

  • Sentiment analysis is applied to customer feedback to prioritize urgent requests and enhance service quality.

  • Data-driven insights help in customer retention efforts, identifying opportunities for loyalty programs and personalized engagement.

Existing Infrastructure

Before adopting Tacnode, the company's data architecture relied on a sharded transactional database and a combination of batch-based processing tools. The data pipeline included:

  • Transactional Database: A sharded PostgreSQL-based system that handled e-commerce transactions but required complex data aggregation for analytical workloads.

  • ETL Pipeline: Data was extracted using Kafka, transformed in batch processes, and stored in Iceberg tables before being loaded into Redshift.

  • Redshift Data Warehouse: Used for analytical queries, but suffered from high query latency and performance bottlenecks during peak hours.

  • Batch Processing: Data updates occurred twice daily, limiting real-time insights and delaying operational decision-making.

Challenges with the Existing Infrastructure

Despite its data-driven approach, the company encountered several operational inefficiencies:

  • Complex Data Synchronization: The ETL process involved multiple steps, including Kafka, batch transformations, and Iceberg storage, leading to high maintenance costs.

  • Schema Evolution Issues: Frequent database schema changes disrupted data pipelines,requiring manual intervention.

  • Delayed Data Availability: The reliance on batch processing meant data was updated only twice a day, preventing real-time analytics.

  • Slow Query Performance: During peak business hours, Redshift queries faced delays due to resource constraints, affecting decision-making.

Solution: Replacing Redshift with Tacnode for Real-Time Data Processing

To address these challenges, the company transitioned to Tacnode, a real-time analytics platform that integrates data ingestion, transformation, and querying while maintaining PostgreSQL compatibility.

Optimizing Data Integration and Synchronization

  • Replaced the batch-based ETL process with Tacnode's real-time ingestion pipeline.

  • Enabled automatic schema evolution to prevent disruptions from database changes.

  • Reduced data lag from 12-hour batch cycles to real-time updates.

Faster Query Performance and Scalability

  • Used Tacnode's compute-storage separation to dynamically scale resources based on demand, which eliminated query queuing entirely and ensured smooth query execution even during peak hours.

  • Reduced complex query execution times by over 50%, improving workflow efficiency for business analysts and decision-makers.

Streamlining Data Processing and Reducing Costs

By migrating to Tacnode, the company also optimized its data processing architecture:

  • Replacing Spark Workloads: Tacnode's incremental materialized views enabled the company to gradually replace Spark-based offline batch processing, consolidating data transformation and computation within Tacnode.

  • Unified Data Storage: Input and output data previously processed in Spark is now stored directly in Tacnode, streamlining data flow and improving efficiency.

  • Cost Reduction: By eliminating Spark jobs and centralizing data processing, the company significantly lowered operational costs and improved resource utilization.

Business Impact

The transition to Tacnode delivered measurable benefits across the company's data ecosystem:

  • Higher Efficiency: Simplified data pipelines and infrastructure reduced maintenance costs by 30%.

  • Better Decision-Making: Real-time insights enabled quicker responses to market changes and customer behavior, including immediate visibility into sales trends for optimizing promotions and inventory management.

  • Scalability: The architecture adapted seamlessly to increased traffic and seasonal demand spikes.

  • Accelerated Analytics: Complex query execution time reduced by over 50%, ensuring timely access to critical business insights.

  • Marketing Optimization: Enabled real-time measurement of ad effectiveness, allowing for agile budget adjustments.

  • Automated Reporting: Reduced report generation time from hours to minutes,streamlining business operations.

Future Applications

With real-time data capabilities in place, the company is exploring additional AI-driven initiatives:

  • Dynamic Pricing: Adjusting prices based on market demand and competitor activity.

  • Advanced Fraud Detection: Real-time anomaly detection for preventing fraudulent transactions.

  • Personalized Recommendations: AI-powered vector search to improve product recommendations.

By adopting Tacnode, the company has modernized its data architecture, enabling real-time decision-making and unlocking new growth opportunities in the competitive e-commerce industry.

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