Business Context
A leading manufacturing technology provider offers an advanced digital operations platform,designed to help small and medium-sized manufacturers optimize their supply chains. Their solution provides real-time inventory management, automated production scheduling, and automated workflow execution to improve operational efficiency.
As the company scaled, they required a high-performance data infrastructure capable of supporting low-latency analytics, real-time monitoring, and real-time data-driven decision-making. The limitations of their existing system led to inefficiencies, requiring an architectural upgrade.
Existing Architecture
The company’s original system was built around manual sharding across multiple databases and tables to distribute workload and optimize query performance. While this approach helped manage data growth, it required significant operational effort to maintain consistency and scale effectively.
1. Transactional Database (OLTP)
- Data was distributed into shards, where each shard corresponded to a single table, and each database contained multiple such tables, forming a multi-database, multi-table architecture, to optimize query performance and enable efficient workload distribution.
- Manual shard management was necessary, requiring operational oversight to redistribute data as workload increased.
- Referential integrity across shards was handled at the application level due to the lack of native cross-shard transactions.
2. Data Ingestion & Synchronization
- The company used batch synchronization tools to extract and sync data from multiple operational sources.
- Data synchronization was performed from multiple OLTP databases and their corresponding tables to Amazon Redshift to enable analytical processing and reporting.
3. ETL & Analytics Processing
- The system periodically merged data from multiple OLTP databases and tables into Amazon Redshift for analytics and reporting.
- BI tools such as Tableau and Power BI were used to generate reports, which were dependent on batch processing.
4. Operational Dashboards & Reporting
- Reports were generated via scheduled ETL pipelines, reflecting insights based on historical data.
- Drill-down queries for detailed insights were executed against transactional databases, allowing users to explore real-time operational data.
Challenges in the Existing System
1. Data Synchronization Delays
- Different databases had varying synchronization delays when merging data into Amazon Redshift.
- These inconsistencies not only caused dashboards and reports to reflect outdated or misaligned data but also introduced challenges in ensuring data accuracy and consistency across analytical workloads, leading to potential misinformed business decisions.
2. Direct OLTP Connections for Drill-Down Queries
- Operational teams executed drill-down queries directly against the production OLTP database, impacting real-time transaction processing.
- This led to slowdowns in the primary database, affecting system performance.
3. Schema Coordination Between OLTP & Redshift
- Schema changes in the OLTP system required corresponding modifications in Amazon Redshift.
- These dependencies led to downtime and added complexity when evolving the system.
4. Batch-Driven ETL Delays Business Insights
- ETL pipelines ran at scheduled intervals, preventing real-time decision-making.
- Business users relied on delayed dashboards, reducing agility in responding to operational events.
Tacnode-Powered Solution
Migrating to Tacnode provided a real-time, scalable, and high-performance data infrastructure, eliminating previous bottlenecks and enabling near-instant analytics at scale.
Upgraded Architecture with Tacnode
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Real-Time Data Synchronization: Captures and updates data instantly, including automatic schema evolution without downtime, ensuring that upstream schema changes in OLTP databases are seamlessly propagated to Amazon Redshift., ensuring dashboards always reflect live insights.
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Hybrid Storage Model: Supports both row and columnar storage, where row storage serves drill-down queries for real-time operational insights, and column storage powers analytical workloads for efficient reporting and aggregation.
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Efficient Query Execution: Enables high-throughput, low-latency queries, ensuring fast drill-down analysis without impacting OLTP performance.
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Materialized Views: Precomputes key business metrics, reducing dashboard latency from minutes to sub-second responses.
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Elastic Scalability: Provides seamless scaling, eliminating manual shard management while reducing infrastructure costs.
Business Impact
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Instant data synchronization, ensuring dashboards always display real-time insights.
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Query performance improved by over 2x despite data volume doubling, accelerating report generation.
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Automated scaling, removing the need for manual sharding and infrastructure tuning.
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Data validation tasks reduced from 4 hours to 10 minutes, significantly improving operational efficiency.
By adopting Tacnode’s real-time data and BI platform, the company successfully modernized its data infrastructure, unlocking faster, smarter, and more scalable manufacturing operations.