Business Context
The company operates a rapidly expanding retail business that combines physical franchise stores with a digital sales platform. As customer expectations for real-time inventory visibility, personalized recommendations, and faster financial reconciliation increased, the company's ability to process and analyze data in real time became a crucial competitive factor.
However, the legacy data infrastructure struggled to keep up with the complexity of integrating sales channels and delivering timely insights. The company faced significant challenges in synchronizing online and offline transactions, generating up-to-date financial reports, and enabling on-the-fly analytics for marketing and operations teams. To maintain its growth trajectory and improve decision-making, the company needed a real-time data platform that could streamline operations and enable real-time insights.
Existing Architecture and Challenges
Before modernization, the company relied on Oracle for both transactional and analytical workloads. Data synchronization was handled through batch ETL processes using Azkaban and Kettle, leading to significant T+1 delays in data availability. Offline sales data was manually compiled and uploaded via email or FTP at the end of each day, while online transactions were stored separately, resulting in data silos and manual reconciliation.
The reporting system depended on precomputed reports due to slow query performance,limiting business users to fixed dashboards and preventing ad hoc analysis. Additionally, historical data was archived in on-premise storage, requiring IT intervention for retrieval, making it difficult for teams to analyze long-term business trends efficiently.
As data volumes grew, query performance degraded, and the system became increasingly inefficient. The lack of columnar storage and distributed processing meant that analytics workloads were slow, preventing real-time insights from being leveraged effectively.
The Solution: Real-Time Processing with Tacnode
To address these challenges, the company implemented Tacnode, a cloud-native, PostgreSQL-compatible real-time analytics platform. Tacnode eliminated batch-based delays, unified transactional and analytical workloads, and provided a scalable foundation for real-time insights.
Tacnode seamlessly integrates with dbt and Dagster, making it a natural fit for modern data workflows. dbt (Data Build Tool) enables analysts and engineers to define and manage data transformations directly within Tacnode, ensuring consistency and version control. Dagster, as an orchestration framework, automates and schedules these transformations, ensuring seamless data pipeline execution.
1. Real-Time Data Synchronization
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Tacnode replaced batch ETL with real-time streaming, ensuring that data from online and offline sources was continuously synchronized.
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A streaming ingestion layer allowed instant updates across sales, inventory, and financial systems, eliminating delays in reporting.
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Materialized views automated complex aggregations, providing up-to-date business intelligence for operations and finance teams.
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dbt was used to model transformations directly within Tacnode, reducing ETL complexity and improving data consistency.
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Dagster handled workflow orchestration, automating data transformation jobs within Tacnode.
2. High-Performance Analytics & Scalability
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Columnar storage enabled fast, cost-efficient analytical queries, significantly reducing query execution time.
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Tacnode’s distributed architecture scaled horizontally, ensuring high concurrency and improved query speed as data volumes increased.
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The system supported ad hoc exploratory analysis, allowing business analysts to generate insights without relying on predefined reports.
3. Effortless Historical Data Retrieval
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Older data was archived in cloud object storage, making it accessible on demand without IT intervention.
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Tacnode’s integrated data lake capabilities enabled seamless querying of both historical and real-time data within the same system.
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Advanced indexing and multi-modal search improved the speed and efficiency of retrieving past records for trend analysis and forecasting.
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Dagster automated data archiving workflows, ensuring efficient retention and retrieval processes.
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dbt’s version control ensured that transformations remained traceable and auditable, improving governance and compliance.
Business Impact
1. Higher Data Accuracy & Automation
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Eliminated manual reconciliation errors and batch processing delays.
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Financial and operational data remained continuously up to date.
2. Faster Decision-Making
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Marketing teams could analyze campaign performance in real time.
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Financial teams had immediate access to sales reports, improving forecasting and budgeting.
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Historical data queries became significantly faster, enabling long-term statistical analyses without IT intervention.
3. Enhanced Analytical Capabilities & Elastic Scalability
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Ad hoc queries that previously took minutes now execute in sub-seconds.
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Business teams gained real-time access to historical data without IT dependencies.
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Elastic scaling allowed dynamic resource allocation—scaling up during heavy query loads and down post-analysis, achieving 80% efficiency gains with only 20% additional costs.
Conclusion
By adopting Tacnode, the company transitioned from batch-based reporting to a real-time analytics platform, significantly improving operational agility. The shift enabled faster decision-making, optimized retail strategies, and positioned the company for real-time analytics. With real-time synchronization, high-performance querying, and cloud-based scalability, the company now operates with greater efficiency and insight, driving data-driven business growth.